Public Blockchain-based covert transmission (CT) can address the limitations of traditional CT methods. Monero is a blockchain-based cryptocurrency with strong privacy protection techniques. However, existing Monero-based CT methods are limited to unicast scenarios. If applied directly to group CT scenarios, they would lead to a significant increase in transaction volume as the number of receivers increases. Meanwhile, existing Bitcoin/Ethereum-based group CT methods at least face three challenges, including susceptibility to key and identity inference attacks, information leakage during off-chain negotiations, and exposure of communication channels.This paper proposes a Monero-Based Group CT approach (MBGCT), which enables on-chain group key (used for receivers to filter covert transactions and extract messages) issuance, fine-grained access control of messages, and covert transaction identification and decryption isolation. MBGCT can ensure confidentiality of group keys, unforgeability of messages, integrity of each transmitted message, obscurity of covert channels, isolation of key generation from key management, and enhanced anonymity. As a result, MBGCT can not only prevent information leakage and channel exposure, but also resist the attacks of entity impersonation, data tampering, key and identity inference. We implemented MBGCT in Monero client v0.18.1.0, and validated its capability of high embedding rates, low transaction fees, and high execution efficiency on the Monero public chain Stagenet.

Metaverse as-a-Service (MaaS) enablesMetaverse tenants to execute theirAPPlications (MetaAPP) by allocating Metaverse resources in the form of Metaverse service functions (MSF). Usually, each MSF is deployed in a virtual machine (VM) for better resiliency and security. However, these MSFs along with VMs and virtual machine monitors (VMM) running them will encounter software aging after prolonged continuous operation. Then, there is a decrease in MetaAPP dependability, namely, the dependability of the MSF chain (MSFC), consisting of MSFs allocated to MetaAPP. This paper aims to investigate the impact of both software aging and rejuvenation techniques on MetaAPP dependability in the scenarios, where both active components (MSF, VM and VMM) and their backup components are subject to software aging. We develop a hierarchical model to capture behaviors of aging, failure, and recovery by applying Semi-Markov process and reliability block diagram. Numerical analysis and simulation experiments are conducted to evaluate the approximation accuracy of the proposed model and dependability metrics. We then identify the key parameters for improving the MetaAPP/MSFC dependability through sensitivity analysis. The investigation is also made about the influence of various parameters on MetaAPP/MSFC dependability.

The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajectory planning in complex urban environments. However, existing studies often overlook key factors, such as urban airspace constraints and economic efficiency, which are essential in low-altitude economy contexts. Deep reinforcement learning (DRL) is regarded as a promising solution to these issues, while its practical adoption remains limited by low learning efficiency. To overcome this limitation, we propose a novel UAV trajectory planning algorithm that integrates DRL with the large language model (LLM) reasoning to enable safe, compliant, and economically viable trajectory planning. Specifically, we model the trajectory planning task as a partially observable Markov decision process, explicitly incorporating obstacle avoidance, regulation awareness, and energy constraints. We design a hybrid optimization algorithm based on the soft actor-critic algorithm and LLM reasoning to enable adaptive decision-making in uncertain and dynamic environments. Experimental results demonstrate that our algorithm achieves the best overall performance, with the highest data collection rate (99.50%), almost zero collision avoidance rate and regulation violation rate, a successful landing rate of nearly 100%, and the lowest energy consumption rate (76.95%). These results validate the effectiveness of our algorithm in addressing UAV trajectory planning key challenges under constraints of the low-altitude economy networking.

Availability and/or performance of PBFT (Practical Byzantine Fault Tolerance) consensus service has been widely studied. However, the existing studies overlook the situation of software aging of honest nodes, which can degrade system performance over time. Rejuvenation techniques can mitigate the negative impact of aging. This paper aims to make a quantitative joint analysis of availability and performance (a.k.a performability) of PBFT consensus service in the scenario where honest nodes are susceptible to software aging and rejuvenation techniques are adopted for recovery. We propose a Semi-Markov process (SMP) based approach for model-based evaluation. Unlike traditional models that rely on exponential distributions, our approach allows the time intervals of all events to follow general distributions, thereby enable a more nuanced analysis of PBFT dynamics. We detail the modeling process and the derivation of metric formulas. We also carry out numerical analysis for the evaluation to assess the performability of PBFT consensus service.

The paper applies analytical modeling techniques to quantitatively analyze the reliability of the two-stage electric-vehicles (EV) charging service of State Grid e-charging platform (SGecP), one of the top 5 EV charging platforms in China. Reliability means that the SGecP EV-charging service satisfies performance requirements of EV-charging requests from users. We first develop a monolithic semi-Markov process (SMP) model for reflecting the dynamics of components involved in the two-stage service. To tackle the scalability weakness inherent in the monolithic model for a large-scale system, we then develop a novel hierarchical SMP model, which can effectively study the dynamics of SGecP charging service. We also derive the formulas for computing evaluation metrics. Simulation and numerical results validate the accuracy and efficiency of the proposed models, and highlight their potential to improve service reliability and user satisfaction.

In the cloud–edge–end collaborative architecture, when data traverses from Data Source Nodes (DSNs) to the Cloud Center Node (CCN) via Edge Verification Nodes (EVNs) for data aggregation, it is crucial to guarantee data integrity for the reliability of subsequent data analysis. However, traditional data integrity verification schemes suffer from high computation overhead, complex certificate management, and limited privacy protection. This paper aims to address these issues. We propose a Two-Tier Batch Data Integrity Verification (T2BDIV) scheme for the cloud–edge–end architecture. The scheme consists of three mechanisms. The first is the batch verification mechanism, which at the edge layer enables an EVN to efficiently verify integrity of data uploaded by DSNs before forwarding them to the CCN. Additionally, at the cloud layer, this mechanism enables the CCN to efficiently verify integrity of aggregated data from each EVN. The second is an identity-based signature mechanism to avoid the complexity of certificate management. The third is a dynamic Pseudo-Identities (PIDs) update mechanism, which generates dynamic pseudo-identities for each transmission to achieve anonymity and unlinkability. Security analysis demonstrates that the proposed scheme ensures data integrity, data source authenticity, and privacy preservation. Performance evaluation shows that our scheme significantly reduces computation and communication overhead compared to the existing scheme, making it highly suitable for large-scale cloud–edge–end deployments.

Attack-path planning plays a key role in proactive cybersecurity because of its ability in helping defenders anticipate adversaries and uncover critical vulnerabilities. This paper proposes GAPPO, a novel deep reinforcement learning-based attack path planning scheme that integrates Graph Attention Networks (GAT) and expert knowledge into Proximal Policy Optimization (PPO). There are three mechanisms in GAPPO. The first is using GAT to produce graph-structure-aware embeddings that emphasize critical connections, enabling expressive state representations for decision making. The second is a ruled-based action masking mechanism, which incorporates expert knowledge to prune the action space based on node dependencies and then to prevent illegal actions from negatively impacting training. The third is combining the results of the first two mechanisms into PPO for attack path planning. Our extensive experimental results demonstrate that GAPPO outperforms existing methods in terms of faster convergence and higher-quality attack paths across diverse scenarios.

Connected Autonomous Vehicles (CAVs) represent a transformative advancement in transportation, offering enhanced safety, improved traffic efficiency, and reduced environmental impact through intelligent driving. As CAVs operate without human intervention, they heavily rely on secure vehicle-to-vehicle (V2V) communication for cooperative perception and coordinated decision-making. These real-time inter-vehicle exchanges underpin safe coordination, dynamic decision-making, and collision avoidance. To ensure trust in such communication, robust and efficient authentication mechanisms are essential. However, existing schemes often fall short in terms of security resilience and operational practicality. In this paper, we propose a novel Certificateless Signature Scheme with Conditional Privacy-Preserving Authentication (CLSS-CPPA) tailored to CAV environments. The proposed scheme addresses three fundamental limitations in existing schemes: signature forgery vulnerabilities, single-authority dependency, and lack of dynamic revocation capability. Our approach employs distributed key generation to prevent signature forgery attacks, utilizes prefix tree structures for efficient dynamic key revocation, and implements dual-agency pseudonym management with mutual authority constraints to prevent single-entity power abuse. Lightweight cryptographic operations are also adopted to suit resource-constrained vehicular systems. Formal security analysis and extensive evaluations demonstrate that CLSS-CPPA enhances privacy preserving and reduces signing and verifying costs by 20%–90% compared to state-of-the-art schemes, making it a promising solution for real-world CAV deployments.

Existing research has demonstrated that autonomous driving policies based on deep reinforcement learning (DRL) are vulnerable to adversarial attacks, which poses challenges for the practical deployment of these policies. Designing effective adversarial attacks is a crucial prerequisite for building robust driving policies. In view of this, we propose a novel adversarial attack method, which can attack the DRL-based autonomous driving agents in a stealthy and efficient manner. This method models the attack as a mixed-integer optimization problem that aims to maximize the safety violations (e.g., collisions) of the agents while minimizing the number of attack steps. Then, a DRL-based adversary is devised in this method to solve the problem to automatically learn the optimal attack policy without domain knowledge. To further enhance the adversarys learning capability, this method incorporates attack-related information into its observations to provide more decisionmaking context and employ a trajectory clipping technique to enhance sample quality. Extensive evaluation results reveal that our method achieves a remarkable 105% enhancement in attack efficiency compared to existing methods.

Data provision, referring to data upload and data access, is one key phase in vehicular digital forensics. The unique features of driverless taxi (DT) bring new issues to this phase: I1) efficient verification of data integrity when diverse data providers (DPs) upload data; I2) DP privacy preservation during data upload; and I3) privacy preservation of both data and investigator (IN) under complex data ownership when accessing data. Considering that the existing works on digital forensics cannot address all these issues, we first propose a novel lightweight and privacy-preserving data provision (LPDP) approach consisting of three mechanisms: 1) privacy-friendly batch verification mechanism (PBVm); 2) data access control mechanism (DACm); and 3) decentralized IN warrant issuance mechanism (DIWIm). PBVm ensures scalable verification of data integrity to address I1. PBVm also ensures the DP privacy preservation in terms of the location privacy and unlinkability of data upload requests to address I2. Besides, DACm and DIWIm are combined to ensure data privacy preservation and the identity privacy of IN in terms of the anonymity and unlinkability of data access requests without sacrificing the traceability to address I3. Security analysis and performance evaluations validate LPDP’s capabilities in addressing the three issues.

Landslides pose significant threats to human life, property, and critical infrastructure. Due to complex landslide dynamics and the lack of high-quality monitoring data, timely and accurately predicting landslide displacement remains challenging. This study proposes a deep learning model to predict landslide displacement by integrating multi-scale InSAR and GNSS monitoring data. InSAR technique provides high-density surface measurements of the landslide, while GNSS technique captures high-precision and real-time landslide dynamics. We first extract temporal features from high-frequency GNSS data and build a heterogeneous spatial network to represent the topological relationship between the multi-scale datasets. Two alternative strategies are developed to fuse GNSS and InSAR data by updating node features (attribute augmented model) or reshaping graph edge relationships (graph topological model). Then, a deep learning model composed of graph convolutional networks (GCNs) and gated recurrent units (GRUs) leverages the integrated global information to deliver high‑precision landslide displacement prediction. Experimental results show that the proposed model achieves the MAE of 0.014 and the MAPE of 7.8 %, outperforming single‑source models and accurately simulating displacement fluctuation signals of the landslide. The graph topological model excels with stable, strongly correlated monitoring data, while the attribute augmented model remains robust under weaker or fluctuating monitoring correlations. These findings underscore the necessity and feasibility of multi-source monitoring data in landslide displacement prediction, providing a robust and scalable framework for landslide disaster prevention.

Driverless taxis (DTs) are gaining attention for their potential to improve urban transportation efficiency. However, unforeseen incidents caused by unsupervised users and the personalized needs of passengers in DTs highlight the need for authenticating user identities and attributes. Additionally, protecting user privacy while enabling rapid traceability of malicious users remains a challenge for the widespread adoption of DTs. This paper proposes a novel Attribute-based Anonymity Enhanced (A2E) authentication scheme for users to access DT services. The security capabilities of A2E include: 1) A2E is attribute-based authentication, which is achieved by designing a user attribute credential. Meanwhile, this attribute credential also satisfies unlinkability. And 2) A2E has enhanced anonymity, which is achieved by designing a decentralized credential issuance mechanism, safeguarding user attributes from association with anonymous identities. Moreover, this mechanism provides traceability and non-frameability to users. From the performance aspect, A2E causes low overhead when tracing malicious users and updating credentials. Besides, both scalability and lightweight are satisfied, which contributes to A2E’s practicability. We conduct security and performance analysis to validate these capabilities.

Federated Learning (FL), a privacy-oriented distributed ML paradigm, is gaining great interest in the Internet of Things because of its capability to protect participants’ data privacy. Studies have been conducted to address the challenges of communication efficiency and privacy-preserving, which exist in standard FL. However, they cannot achieve the goal of making a tradeoff between communication efficiency and model accuracy while guaranteeing privacy. This paper proposes a Conditional Random Sampling (CRS) method and implements it into the standard FL (CRS-FL) to tackle the above-mentioned challenges. CRS explores a Poisson-sampling-based stochastic coefficient to achieve a higher probability of obtaining zero-gradient unbiasedly and then decreases the communication overhead effectively without model accuracy degradation. Moreover, we dig out the relaxation Local Differential Privacy (LDP) guarantee conditions of CRS theoretically. Extensive experiment results indicate that (1) in communication efficiency, CRS-FL performs better than the existing methods in metric accuracy per transmission byte without model accuracy reduction in more than 7% sampling ratio (# sampling size / # model size); (2) in privacy-preserving, CRS-FL achieves no accuracy reduction compared with LDP baselines while holding the efficiency, even exceeding them in model accuracy under more sampling ratio conditions.

Traditional covert transmission (CT) approaches have been hindering CT application while blockchain technology offers new avenue. Current blockchain-based CT approaches require off-chain negotiation of critical information and often overlook the dynamic updating of session keys, which increases the risk of message and key leakage. Additionally, in some approaches the covert transactions exhibit obvious characteristics that can be easily detected by third-parties. Moreover, most approaches do not address the issue of decreased reliability of message transmission in blockchain attack scenarios. Bitcoin-and Ethereum-based approaches also have the issue of transaction linkability, which can be tackled by Monero-based approaches because of the privacy protection mechanisms in Monero. However, Monero-based CT has the problem of sender repudiation. In this paper, we propose a novel$M$onero-$B$ased CT approach (MBCT), which enables on-chain session key dynamically updating without off-chain negotiation. MBCT can assure confidentiality of on-chain session key, non-repudiation of transmission parties, reliability of message transmission under blockchain attack, unlinkability and obscurity of covert transactions. They are achieved by the three components in MBCT, namely, a sender authentication method, a dynamically on-chain session key updating method and a state feedback method. We implement MBCT in Monero-0.18.1.0 and the experiment results demonstrate its high embedding capacity of MBCT.

Container technology, as the key enabler behind microservice architectures, is widely applied in Cloud and Edge Computing. A long and continuous running of operating system (OS) hosting container-based services can encounter software aging that leads to performance deterioration and even causes system failures. OS rejuvenation techniques can mitigate the impact of software aging but the rejuvenation trigger interval needs to be carefully determined to reduce the downtime cost due to rejuvenation. This paper proposes a comprehensive semi-Markov-based approach to quantitatively evaluate the effect of OS rejuvenation on the dependability and the performance of a container-based service. In contrast to the existing studies, we neither restrict the distributions of time intervals of events to be exponential nor assume that backup resources are always available. Through the numerical study, we show the optimal container-migration trigger intervals that can maximize the dependability or minimize the performance of a container-based service.

In this paper, we describe a mechanism to improve the speed of the Practical Byzantine Fault Tolerance (PBFT) consensus protocol in a multi-leader setting by overlapping the COMMIT and PREPREPARE stages where applicable. We analyze the performance of the proposed mechanism using a probabilistic model and show that it achieves high throughput at high block arrival rates, with cycle time approaching one maximum round-trip time between the nodes on the consensus committee. The mechanism is suitable for blockchain applications since invalid blocks are detected and rejected directly by the consensus protocol without the need to retract the decisions already reached.

Implementing a consensus protocol in a Proof-of-Stake context requires a delicate tradeoff between different system parameters. Ethereum 2.0, probably the most popular PoS system today, uses a large number of validators to achieve decentralization, but long time windows, during which both blocks and attestations for those blocks are considered valid, open up the possibility for a number of attacks that target the process of consensus. A possible remedy would be to try to achieve single-slot finality similar to that obtained in Practical Byzantine Fault Tolerance (PBFT). In this paper, we develop a Markov chain model of validator lifecycle in an Ethereum 2.0-like system with single-slot finality which includes penalties and rewards, as well as the possibility of voluntary exit and waiting to rejoin the validator pool. Using the model, we obtain the probability of achieving consensus as the function of probabilities of different events, most notably the probability of truthful voting by the validator. Our results indicate that consensus is rather sensitive to false voting, and that low probability of waiting and low probability of voluntary exit help improve the probability of consensus.

Federated Learning (FL), as a privacy-oriented distributed machine learning paradigm, can obtain a well-trained global model without private dataset transferring. Nevertheless, FL is subject to severe security threats of adversarial examples (AEs) with unnoticeable perturbations, generated by white-box attacks in honest-but-curious FL participants. Adversarial training is an effective solution to enhance the robustness of the model by identifying AEs as correct samples. However, the AE training efficiency is crucial in realistic scenarios of adversarial training, such as autonomous driving. Researchers have proposed the Fast Gradient Sign Method (FGSM) and its improvement to generate AEs rapidly. In this paper, we propose a novel optimizer-based FGSM, FastAdaBelief-based FGSM (FAB-FGSM), in order to generate AEs more efficiently and effectively. Benefitting from time-vary coefficients and a vanishing factor, FAB-FGSM realizes a more adaptive iteration step size than AdaBelief-based FGSM (AB-FGSM) and Adam-based FGSM (AI-FGSM). We explore the probable causes by recalling the theoretical analysis of three optimizers. Extensive experiment results demonstrate that compared to AB-FGSM and AI-FGSM, our FAB-FGSM achieves the fastest convergence and the best attack success rate in four target models, including Inception v3, Inception v4, Inception ResNet v2, ResNet-101.

Both blockchain and oracle are among key technologies which are leveraged in Web 3.0 to empower the internet industry. Oracle aims to provision blockchain with real world data and support external connectivity for closed blockchain systems. Compared with a centralized oracle system, a distributed oracle system can tackle the issues of single point failure and untrusted data. This paper explores analytical modeling techniques to quantitatively study the dependability (availability and reliability) of the distributed oracle system with arbitrary number of heterogeneous oracle nodes. We first develop a Markov model to describe oracle system dynamics. Then we derive both the formula of system availability and the formula of mean time to failure (MTTF) to study the system reliability. The experimental results indicate 1) the system availability is mainly affected by mean node failure time when it is smaller than 10 days, 2) the system recovery ability has critical impact on availability, and 3) MTTF can be significantly improved by introducing more nodes.

Abstract Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.

Unmanned aerial vehicles (UAVs) are being broadly employed to assist in efficient data collection for Internet of Things (IoT) networks. Studies have been conducted to ensure the effectiveness and safety of UAVs in the data collection process. However, they only considered part of the challenges of energy consumption, collision avoidance, and mobility of IoT devices. In this article, we study a UAV path planning optimization problem for UAV-assisted data collection to maximize the amount of collected data. Different from these existing works, this optimization problem not only considers all these challenges, but also considers the kinematic and communication constraints. Moreover, in this problem, the duration required for the UAV to complete the mission is unknown, makes it more challenging to solve this problem through traditional optimization methods. We thus formulate the problem as a partially observable Markov decision process (POMDP) with a continuous action space and propose a proximal policy optimization-based algorithm to address it. Experiment results demonstrate that our algorithm has significant advantages over other baseline algorithms in terms of success rate, data collection rate, and collision rate.

Recently, big data has seen explosive growth in the Internet of Things (IoT). Multi-layer FL (MFL) based on cloud-edge-end architecture can promote model training efficiency and model accuracy while preserving IoT data privacy. This paper considers an improved MFL, where edge layer devices own private data and can join the training process. iMFL can improve edge resource utilization and also alleviate the strict requirement of end devices, but suffers from the issues of Data Reconstruction Attack (DRA) and unacceptable communication overhead. This paper aims to address these issues with iMFL. We propose a Privacy Amplification scheme on iMFL (PA-iMFL). Differing from standard MFL, we design privacy operations in end and edge devices after local training, including three sequential components, local differential privacy with Laplace mechanism, privacy amplification subsample, and gradient sign reset. Benefitting from privacy operations, PA-iMFL reduces communication overhead and achieves privacy-preserving. Extensive results demonstrate that against State-Of-The-Art (SOTA) DRAs, PA-iMFL can effectively mitigate private data leakage and reach the same level of protection capability as the SOTA defense model. Moreover, due to adopting privacy operations in edge devices, PA-iMFL promotes up to 2.8 × communication efficiency than the SOTA compression method without compromising model accuracy.

Federated learning (FL) as a secure distributed learning framework gains interests in Internet of Things (IoT) due to its capability of protecting the privacy of participant data. However, traditional FL systems are vulnerable to free-rider (FR) attacks, which causes unfairness, privacy leakage and inferior performance to FL systems. The prior defense mechanisms against FR attacks assumed that malicious clients (namely, adversaries) declare less than 50% of the total amount of clients. Moreover, they aimed for anonymous FR (AFR) attacks and lost effectiveness in resisting selfish FR (SFR) attacks. In this article, we propose a parameter audit-based secure and fair FL scheme (PASS) against FR attack. PASS has the following key features: 1) prevent from privacy leakage with less accuracy loss; 2) be effective in countering both AFR and SFR attacks; and 3) work well no matter whether AFR and SFR adversaries occupy the majority of clients or not. Extensive experimental results validate that PASS: 1) has the same level as the state-of-the-art method in mean square error against privacy leakage; 2) defends against AFR and SFR attacks in terms of a higher defense success rate, lower false positive rate, and higher F1-score; and 3) is still effective where adversaries exceed 50%, with F1-score 89% against AFR attack and F1-score 87% against SFR attack. Note that PASS produces no negative effect on FL accuracy when there is no FR adversary.

Processing sensitive data and deploying well-designed Intellectual Property (IP) cores on remote Field Programmable Gate Array (FPGA) are prone to private data leakage and IP theft. One effective solution is constructing Trusted Execution Environment (TEE) and its secure boot process on FPGA-SoC (FPGA System on Chip).This paper aims to establish Secure Runtime Customizable TEE (SrcTEE) on FPGA-SoC through the design of a novel secure boot scheme and the design of the following three components: 1) CrloadIP, which enforces access control on TEE applications deploying IP at runtime such that SrcTEE can alleviate threats from unauthorized TEE applications and then SrcTEE can be adjusted dynamically and securely; 2) CexecIP, which not only enables the execution of newly-installed IP cores without modifying the operating system of FPGA-SoC TEE, but also prevents insider attacks from executing IPs in SrcTEE; 3) CremoAT, which can provide the newly-measured SrcTEE state and establish a secure communication path between remote verifiers and SrcTEE. Our secure boot scheme supports refreshable root trust key, and assures the authenticity and integrity of boot codes during the SrcTEE booting process. We conduct a security analysis of SrcTEE and its performance evaluation on Xilinx Zynq UltraScale+ XCZU15EG 2FFVB1156 MPSoC.

In this paper, we analyze the performance of Ethereum data distribution network using a probabilistic model which allows accurate modeling of data propagation but also of forking, which happens when the blockchain maintained by the network temporarily splits into multiple versions due to a disagreement over the validity of a particular block. We also investigate the duration of inconsistent states of the ledger, which refers to the amount of time that the network remains split or partitioned. Finally, we model the block withholding attack and block slowdown attack, and analyze their impact on network performance of the network in terms of quality indicators such as block delivery time, the duration of ledger inconsistency, and forking probability. We also propose countermeasures for the block withholding attack.

Delegated Proof of Stake (DPoS) is a high throughput, low power consumption consensus mechanism is which elected participants or witnesses vote to accept blocks to be included in the blockchain ledger. However, DPoS is prone to centralization of voting power which can introduce bias and degrade the usability of the blockchain. In this work, we propose the concept of virtual stake which measures the truthfulness of witness voting throughout the round. Virtual stake at the end of a round is used as incentive for the next election of witnesses, but electors have a discretionary right to cast a portion of their votes for candidate witnesses regardless of their past behavior. Virtual stake is also used to guide the process of selecting the leader(s) to propose blocks for voting in PBFT cycles within the round, which prevents mis.behaving witnesses from submitting blocks. We describe and solve the analytical model of witness behavior, assuming that witnesses can be categorized into behavioral classes with different probability of truthful voting, false voting, and abstention. Our results show the impact of class populations, and voting behavior on virtual stakes, distribution of votes, and, most importantly, on overall consensus probability. The impact of centralization and witness misbehavior in voting can be countered by an increase of voting groups and decrease of round size, although further control of voting process during a round may be necessary to maintain the desired performance level of the blockchain system.

GHOST, like the longest-chain protocol, is a chain selection protocol and its capability in resisting selfish mining attack has been validated in imperfect (delay-existing-) blockchains of Bitcoin and its variants (Bitcoin-like). This paper explores an analytical-model-based approach to investigate the impact of stubborn mining attack in imperfect GHOST Bitcoin-like blockchains. We first quantify chain dynamics based on Markov chain process and then derive the formulas of miner revenue and system throughput. We also propose a new metric, “Hazard Index”, which can be used to evaluate attack threat severity and also assist the adversary in determining whether it is profitable to conduct an attack. The experiment results show that 1) An adversary with more than 30% computing power can get huge profit and extremely downgrade system throughput by launching stubborn mining attack. 2) An adversary should not launch stubborn mining attack if it has less than 25% computing power. 3) Stubborn mining attack causes more damage than selfish mining attack under GHOST. Our work provides insight into stubborn mining attack and is helpful in designing countermeasures.

Structured data is widely used in big data storage and analytics but only a few Structured Data Symmetric Searchable Encryption (SD-SSE) schemes were designed. Moreover, they at least have two security issues: lack of both forward security and keyword privacy. In addition, the existing various SSE schemes designed for unstructured data cannot be applied to structured data. The paper proposes a Secure and Efficient SSE Scheme (SES2) for structured data. SES2 can not only address the above two security issues but also is more efficient than the existing Structured Data SSE (SD-SSE) schemes. Forward security is achieved by using a new key to generate the related index when the data is updated. Keyword privacy is assured by adding noise to the query trapdoor. Efficiency is improved by generating indexes with Bloom filter in a more efficient way.

In this paper, we analyze the performance of Ethereum data distribution network using a probabilistic model which allows accurate modeling of data propagation. We then apply the model to analyze the performance of data propagation in Ethereum when using geth protocol under block withholding attack, Eclipse attack, and block slowdown attack. We also propose countermeasures for the block withholding attack and Eclipse attack.

Practical Byzantine Fault Tolerance (PBFT) is widely used despite its limitations. In this paper we extend a previously described multiple entry version of PBFT with fast cycle capability. In this approach, multiple data blocks are proposed by different nodes or replicas, but instead of choosing only one to undergo the consensus procedure, replicas collect and order the proposals which are then accepted through a simplified consensus cycle. We analyze the operation of this scheme using a probabilistic model and show that consensus performance actually improves over that of the original PBFT as the traffic load increases. In this manner, the proposed scheme appears to be well suited for blockchain-based Internet of Things (IoT) applications.

In this paper we propose a gated cycle scheme for prioritized block access in a PBFT-like consensus mechanism suitable for blockchain-based IoT applications. Blocks submitted by clients are processed in cycles of variable length, in the order determined according to their priorities which can be assigned according to the block length or orderer's stake. We investigate the performance of this scheme using an analytical model and show that it indeed allows for clear differentiation of traffic of different priorities whilst retaining good performance, esp. under higher traffic load, compared to the original PBFT.

With the popularization of emerging technologies such as artificial intelligence, 5G and beyond, extended reality and blockchain, the next generation Internet is rapid expansion. “Metaverse” as an evolving paradigm of next-generation Internet, can be recognized as a fully immersive, hyper spatiotemporal and self-sustaining virtual shared space, and its concept is continuous development and evolution. It is moving from imagination to the coming reality, but it is still far from being realized. One of reasons is that distinct sub-metaverses deploying their services on heterogeneous blockchains results in major problems for interoperability, preventing the implementation of seamless integrated metaverse. Facing the challenge, this paper proposes a decentralized identifiers (DIDs) assisted secure cross-metaverse authentication scheme for MEC-enabled metaverse, which is based on a novel designed infrastructure build on MEC and blockchain. In addition, the proposed scheme adopts DIDs, which can not only achieve the secure cross-metaverse authentication, but also increase the decentralization of the metaverse. In addition, the adoption of ID-based aggregate signature can reduce the overhead of computation, communication and storage.

Bitcoin is the largest PoW blockchain, which currently uses the longest-chain protocol for chain selection and is vulnerable to various attacks like stubborn mining attack. As a variant of selfish mining attack, stubborn mining attack usually has 7 types of strategies, each of which does damage to the blockchain system. GHOST is another chain-selection protocol, which has been demonstrated to make the blockchain system more secure than the longest-chain protocol under selfish mining attack. There were studies on stubborn mining in perfect GHOST blockchains and they only studied two types of stubborn mining strategies. But it is a fact of life that the blockchain is an imperfect network due to ubiquitous network congestion and/or attacks. This paper aims to explore a simulation-based approach to quantitatively evaluate the threat capability of all 7 stubborn mining strategies. We first develop all stubborn strategies in imperfect GHOST blockchains. Then we evaluate miner revenues and system throughput over different network conditions. The results show that the lead-fork-stubborn strategy is the dominant strategy for attackers when they have more than 33% total computing power. The stubborn attackers with less than 20% total computing power lose their revenue whichever stubborn mining strategy is used. The blockchain with high network quality still has the risk of significant throughput downgrade. Our work can help the stubborn mining attack detection and secure blockchain system design.

Abstract Internet of Things (IoT) jobs not only require computational resources but also are delay-sensitive and security-sensitive. Edge computing emerges as a promising paradigm to improve the quality of experience for IoT users. Edge computing faces many security threats, perhaps even more than traditional data centers. With a growing amount of data offloaded to Edge Data Centers (EDCs), the EDC performance needs to be considered and evaluated carefully for improving the vulnerable EDC resource utilization while satisfying IoT job requirements. This paper develops an analytical model, which can capture the dynamics of an EDC system with the following features: (i) The system is under heterogeneous workloads; (ii) the system is subject to attacks, which prevent equipment units in the system from providing service and (iii) the jobs in the system are delay-sensitive. Namely, the job processing fails before the processing is completed. Based on the proposed model, we develop formulas for performance and profit metrics and conduct a series of simulation experiments to verify the correctness and accuracy of our model. Finally, through our model, we evaluate the performance of the EDC, and we offer solutions for EDC administrators to maximize profit.

Abstract The continuous increase of electric vehicles is being facilitating the large-scale distributed charging-pile deployment. It is crucial to guarantee normal operation of charging piles, resulting in the importance of diagnosing charging-pile faults. The existing fault-diagnosis approaches were based on physical fault data like mechanical log data and sensor data streams. However, there are other types of fault data, which cannot be used for diagnosis by these existing approaches. This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault, charging-pile program fault, user personal fault, signal fault (offline), pile compatibility fault, charging platform fault, and other faults. We aim to find out how to combine existing feature-extraction and machine learning techniques to make the better diagnosis by conducting experiments on realistic dataset. 4 word embedding models are investigated for feature extraction of fault data, including N-gram, GloVe, Word2vec, and BERT. Moreover, we classify the word embedding results using 10 machine learning classifiers, including Random Forest (RF), Support Vector Machine, K-Nearest Neighbor, Multilayer Perceptron, Recurrent Neural Network, AdaBoost, Gradient Boosted Decision Tree, Decision Tree, Extra Tree, and VOTE. Compared with original fault record dataset, we utilize paraphrasing-based data augmentation method to improve the classification accuracy up to 10.40%. Our extensive experiment results reveal that RF classifier combining the GloVe embedding model achieves the best accuracy with acceptable training time. In addition, we discuss the interpretability of RF and GloVe.

This article investigates a hierarchical aerial computing system, where both high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) provision computation services for ground devices (GDs). Different from the existing works which ignored UAV task offloading to HAPs and suffered long transmission delay between HAPs and GDs, in our system, UAVs are responsible for collecting the tasks generated by GDs. Considering limited resources and constrained coverage, UAVs need to cooperatively allocate their resources (including spectrum, caching, and computing) to GDs. After collecting GD tasks, UAVs are allowed to offload part of these tasks to the HAP, in order to minimize task processing delay and then better satisfy GD delay requirement. Our objective is to maximize the amount of computed tasks while satisfying tasks’ heterogeneous Quality-of-Service (QoS) requirements through the joint optimization of UAV resource allocation and task offloading. To this end, a joint optimization problem is first formulated as a partially observable Markov decision process (POMDP) under the constraints of available resources, UAV energy, and collision avoidance. Then, we design a multiagent proximal policy optimization (MAPPO)-based algorithm to solve the optimization problem. By introducing the centralized training with decentralized execution framework, UAVs acting as agents can cooperatively make decisions on GDs association, resource allocation, and task offloading according to their local observations. In addition, state normalization and action mask are also adopted to improve training efficiency. Experimental results verify the efficiency of the proposed algorithm and the system performance is also analyzed by the numerical results.

The consensus algorithm is an essential ingredient of any blockchain system. Many different consensus mechanisms, such as practical Byzantine fault tolerance (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and their many derivatives, have been proposed over the years, but the complementary problems of performance and resilience to malicious behavior of the nodes have yet to be resolved in a satisfactory manner. In this work, we propose a consensus mechanism that integrates PoS with PBFT, which can effectively deal with dishonest nodes, both individual validators and leaders, while maintaining high performance. Our model incentivized truthful behavior by using trust score and reward mechanisms as crucial components of the block validation and ordering processes. The performance of the proposed scheme is evaluated using an analytical model that employs a semi-Markov process, defined by an ergodic multidimensional Markov chain with a finite number of states. The results show the efficiency of the proposed model in consensus-based decision making, even under a high likelihood of dishonest node behavior.

This paper aims to use analytical modeling technique to quantitatively study the dependability of Vehicle Platooning Application, which consists of Multiple Sub-Services (VPP-MSS) to achieve its functionality. Each sub-service (SS), based on network function virtualization technology, is executed in a container. Both SSes and OSes which SSes run on can suffer from software aging after a long and continuous running, reducing VPP-MSS dependability. Rejuvenation techniques are usually used to combat software aging, but they require the support of backup components. Quantitative study of VPP-MSS dependability enables in-depth understanding of the effectiveness of rejuvenation techniques based on analytical models. In contrast to the existing studies, we develop a semi-Markov process (SMP) model to jointly analyze the impact of rejuvenation technique trigger intervals (RTTIs), backup components’ behaviors, time-dependent interactions between various behaviors and the number of active SSes deployed on an OS on the effectiveness of rejuvenation technique. Sensitivity analysis helps identify key parameters for improving the dependability of VPP-MSS. Extensive numerical experiments demonstrate the necessity of considering backup components’ behaviors and investigating non-exponentially distributed failure times. We also determine both the optimal RTTI combination and the optimal combination of SSes and OSes, which can maximize VPP-MSS dependability.

The Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) integrated architecture is a key enabling platform for 5G to run multiple customized services in the form of service function chain (SFC) configured as an ordered set of service functions (SFs). However, memory-related software aging in the SF that can be exploited by attackers becomes a new threat to the dependability of MEC-SFC services. To provide dependable MEC-SFC services, proactive rejuvenation techniques to counteract the SF aging problem are essential. In this paper, we develop a semi-Markov model to quantitatively investigate the transient availability and steady-state dependability (availability and reliability) of MEC-SFC services. Our model enables the analysis of a MEC-SFC with any number of SFs, and can capture complex time-dependent behaviors of aging, failure, and recovery. The approximate accuracies of the presented model on dependability measures are comprehensively evaluated through comparative studies with simulation experiments. We then detect potential bottlenecks for a MEC-SFC system through sensitivity analysis and further analyze the impact of event-time interval distributions on steady-state dependability. Finally, we investigate the transient behaviors of a MEC-SFC service when varying system parameters during MEC-SFC operation.

Unmanned aerial vehicles (UAVs) have been widely used in Intelligent Transportation Systems (ITS) due to their rapid deployment and high mobility, which are considered as a promising solution to expand the scope of communication, especially in inaccessible areas. However, there is a lack of a universal and extensible multi-hop collaboration model in the existing research on UAV-involved ITS. In this paper, we innovatively introduce a novel UAV-enabled multi-hop collaborative fog computing (FC) system model, in which several moving UAVs with unpredictable locations provide effective and efficient communication and computation services for ground user equipments (UEs). With this model, we mathematically formulate a joint user association, UAV association, task offloading, transmission power, computation resource allocation, and UAV location optimization problem, which is a mixed integer nonlinear programming (MINLP) problem and challenging to deal with. To solve the non-convex problem, we propose a novel multi-hop collaborative algorithm to derive the optimal task offloading and resource allocation decisions for each UAV. Simulation results demonstrate the superiority of the UAV-enabled multi-hop collaborative FC system and validate the effectiveness of the proposed scheme.

Task offloading combined with reinforcement learning (RL) is a promising research direction in edge computing. However, the intractability in the training of RL and the heterogeneity of network devices have hindered the application of RL in large-scale networks. Moreover, traditional RL algorithms lack mechanisms to share information effectively in a heterogeneous environment, which makes it more difficult for RL algorithms to converge due to the lack of global information. This article focuses on the task offloading problem in a heterogeneous environment. First, we give a formalized representation of the Lyapunov function to normalize both data and virtual energy queue operations. Subsequently, we jointly consider the computing rate and energy consumption in task offloading and then derive the optimization target leveraging Lyapunov optimization. A Deep Deterministic Policy Gradient (DDPG)-based multiple continuous variable decision model is proposed to make the optimal offloading decision in edge computing. Considering the heterogeneous environment, we improve Hetero Federated Learning (HFL) by introducing Kullback-Leibler (KL) divergence to accelerate the convergence of our DDPG based model. Experiments demonstrate that our algorithm accelerates the search for the optimal task offloading decision in heterogeneous environment.

Multi-accessedgecomputing (MEC)-enabledInternetofThings (IoT) is considered as a promising paradigm to deliver computation-intensive and delay-sensitive services to users. IoT service requests can be served by multiplemicroservices (MSs) that form a chain, called amicroservicechain (MSC). However, the high complexity of MSs and security threats in MEC-enabled IoT pose new challenges to MSC dependability. Proactive rejuvenation techniques can mitigate the impact of resource degradation of MSs and hostoperatingsystems (OSes) executing them. In this article, we develop a multi-dimensional semi-Markov model to investigate the effectiveness of proactive rejuvenation techniques in improving the dependability (availability and reliability) of a dynamic and heterogeneous MSC. The results of numerical experiments firstly reveal how MSs can be effectively combined, in different deployment configurations, with host OSes to improve MSC dependability, secondly jointly optimize the rejuvenation trigger intervals of host OS and MSs running on it, and finally show the impact of time-varying parameters. We also identify the bottlenecks for MSC dependability improvement by sensitivity analysis, and give the ranges of important parameter values guaranteeing five-nines availability. In addition, the superiority of our model is demonstrated by comparison with the continuous-time Markov chain model.

Fork after withholding (FAW) attack is an easy-to-conduct attack in the Bitcoin system and it is hard to be detected than some attacks like selfish mining and selfholding attacks. The previous studies about FAW attack made some strong assumptions, such as no propagation delay in the network.

In this work we combine Delegated Proof of Stake (DPoS) consensus technique with multiple entry Practical Byzantine Fault Tolerant voting in a permissioned blockchain network. Voting is organized in rounds that contain a number of PBFT consensus cycles. Nodes with low stake are forced to leave at the end of current round and may return to a new round when they repurchase the stake tokens, while nodes with sufficient stake may leave the network temporarily at the completion of current round due to mobility. We consider multiple DPoS classes based on node's initial stake and probability of truthful voting, and model their behavior using embedded Markov Chain which corresponds to a Semi Markov Process (SMP). We show that probability of reaching consensus is higher when rounds are shorter and/or there are more nodes in the network. In addition, we find that nodes from higher priority classes are mostly excluded from voting due to their mobility, while those from lower priority classes are excluded more often on account of low stake.

In this work we integrate Proof of Stake (PoS) consensus technique with multiple entry Practical Byzantine Fault Tolerance voting in a permissioned blockchain network. We introduce several PoS classes based on stake and truthfulness of voting. Each class is modeled using Semi Markov Process (SMP). We derive probability of reaching the two-thirds majority of total number of votes, and highlight the impact of the populations of individual stake/priority classes on achieving consensus. We have also connected stake classes with Enhanced Distribution Coordination Function EDCA for leader selection, which enables nodes from high stake classes to have higher frequency of leader role and gain more revenue for block handling.

Practical Byzantine Fault Tolerance (PBFT) is a widely used consensus protocol which is sensitive to malicious behavior of the designated leader. In this paper we discuss two mechanisms that allow any ordering node on the consensus committee to act as the leader, thus alleviating the dependency on the leader. The selection of the next leader is performed by arbitration, rather than through a predefined sequence or round-robin mechanism. As the result, the proposed mechanisms lead to improved security since a malicious leader cannot stall the consensus and the next leader is not known beforehand. Performance evaluation shows that the proposed mechanisms indeed offer independence of the chosen leader and a reduction of queuing times of client proposals, albeit with some performance degradation.

Malignant miners with small computing power can achieve unfair revenue and degrade system throughput through launching Fork after withholding (FAW) attack in a Proof-of-Work (PoW) blockchain system. The existing works about FAW attack have some of the following issues: (i) only studying Bitcoin blockchain, (ii) assuming that the blockchain network is perfect and then ignoring forks due to block propagation delay, and (iii) assuming that there is only one pool under attack. This paper attempts to investigate FAW attack in imperfect Bitcoin and Ethereum networks where malicious miners attack multiple victim pools. We develop a simulator to capture the chain dynamics under FAW attack in a PoW system where the longest-chain protocol is used. Two different computing power allocation strategies for malicious miners, PAS and EAS, are investigated in terms of the profitability of FAW adversaries, the loss of victims, and the blockchain throughput. The results reveal that FAW adversaries can get more revenue under PAS when more victim pools are subjected to attack in both Bitcoin and Ethereum. If FAW adversaries adopt EAS and the number of victims vary from 1 to 12, they can get maximal revenue when attack 7 victims in Bitcoin. The blockchain throughput decreases significantly under PAS while it is almost unchanged under EAS with the increasing number of victims in both Bitcoin and Ethereum. Our work helps the design of countermeasures against FAW attack.

Unmanned aerial vehicle (UAV) communication is easily wiretapped by malignant nodes due to the broadcast nature of line-of-sight (LoS) wireless channels. To tackle this problem, this paper investigates a dual-UAV aided secure dynamic ground-to-UAV (G2U) communication system. By dynamic, we mean UAVs communicate with moving ground devices (GDs). Our objective is maximizing the sum secrecy rate by the joint optimization of UAV trajectory and GDs transmit power. To achieve it, we first formulate this nonconvex optimization problem as a Constrained Markov Decision Process (CMDP) under the constraints of UAV flying speed, initial and final locations, limited energy, and average transmit power. Then, a Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning algorithm is designed, named SC-TDPC, to learn the optimal transmit power and UAV trajectory. The experiment results demonstrate that, compared to other benchmark schemes, SC-TDPC can efficiently enhance the UAV communication security in terms of sum secrecy rate.

Federated Learning (FL) is a distributed learning framework and gains interest due to protecting the privacy of participants. Thus, if some participants are free-riders who are attackers without contributing any computation resources and privacy data, the model faces privacy leakage and inferior performance. In this paper, we explore and define two free-rider attack scenarios, anonymous and selfish free-rider attacks. Then we propose two methods, namely novel and advanced methods, to construct these two attacks. Extensive experiment results reveal the effectiveness in terms of the less deviation with conventional FL using the novel method, and high false positive rate to puzzle defense model using the advanced method.

Vehicle-to-everything (V2X) communication is the key enabler for emerging intelligent transportation systems. Applications built on top of V2X require both authentication and privacy protection for the vehicles. The common approach to meet both requirements is to use pseudonyms which are short-term identities. However, both industrial standards and state-of-the-art research are not designed for resource-constrained environments. In addition, they make a strong assumption about the security of the vehicle's on-board computation units. In this paper, we propose a lightweight auto-refreshing pseudonym protocol (LARP) for V2X. LARP supports efficient operations for resource-constrained devices, and provides security even when parts of the vehicle are compromised. We provide formal security proof showing that the protocol is secure. We conduct experiments on a Raspberry Pi 4. The results demonstrate that LARP is feasible and practical.

The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples—the legitimate input added with imperceptible and well-designed perturbation that can fool DNNs easily in the DNN testing/deploying stage. Previous white-box adversarial example generation algorithms used the Jacobian gradient information to add the perturbation. This imprecise and inexplicit information can cause unnecessary perturbation when generating adversarial examples. This paper aims to address this issue. We first propose to apply the more informative and distilled gradient information, namely, integrated gradient, to generate adversarial examples. To further make the perturbation more imperceptible, we propose to employ the restriction combination of L 0 and L 1 / L 2 second, which can restrict the total perturbation and the perturbation points simultaneously. Meanwhile, to address the nondifferentiable problem of L 1 , we explore a proximal operation of L 1 third. On the basis of these three works, we propose two Integrated gradient-based White-box Adversarial example generation algorithms (IWA): Integrated gradient-based Finite Point Attack (IFPA) and Integrated gradient-based Universe Attack (IUA). IFPA is suitable for situations where there are a determined number of points to be perturbed. IUA is suitable for situations where no perturbation point number is preset to obtain more adversarial examples. We verify the effectiveness of the proposed algorithms on both structured and unstructured data sets, and compare them with five baseline generation algorithms. The results show that our proposed algorithms craft adversarial examples with more imperceptible perturbation and satisfactory crafting rate. L 2 restriction is suitable for unstructured data sets and L 1 restriction performs better in the structured data set.

With the rapid and wide development and deployment of system virtualization, service availability analysis has become increasingly important in a virtualized system (VS) which suffers from software aging. Software rejuvenation techniques can be applied to improve service availability but its effectiveness depends on the rejuvenation policy, which defines when and where to rejuvenate, and which rejuvenation technique to be triggered. This article aims to analyze the optimal inspection time interval for maximizing application service (AS) availability under a three-level rejuvenation policy, in which rejuvenation techniques are deployed at each level, namely, AS, virtual machine (VM), and virtual machine monitor (VMM) levels. We first apply Markov regenerative process to construct an analytical model for the VS. Experiments of injecting memory leaks are conducted to measure aging-related parameters. Furthermore, numerical analysis is carried out to study the quantitative relationship between AS availability and inspection time interval, and determine the approximate optimal inspection time interval.

The longest-chain and Greedy Heaviest Observed Subtree (GHOST) protocols are the two most famous chain-selection protocols to address forking in Proof-of-Work (PoW) blockchain systems. Inclusive protocol was proposed to lower the loss of miners who produce stale blocks and increase the blockchain throughput. This paper aims to make an analytical-model-based quantitative comparison of their capabilities against selfish mining attack. Analytical models have been developed for the longest-chain protocol but less to the GHOST protocol. However, the blockchain dynamics and evolution are different when adopting different chain-selection protocols. Therefore, the corresponding analytical models and/or the formulas of calculating metrics (such as miner profitability and system throughput) may be different. To address these challenges, this paper first develops a novel Markov model and the formulas of evaluation metrics, in order to analyze a GHOST-based blockchain system under selfish mining attack. Then extensive experiments are conducted for comparison and we observe that: (i) The GHOST protocol is more resistant to selfish mining attack than the longest-chain protocol from the aspect of relative revenue of selfish miners. (ii) Inclusive protocol can promote the security (evaluated in terms of miner profitability) improvement of the system which has little total computational power or a high forking probability. Additionally, the longest-chain protocol is more sensitive to inclusive protocol than GHOST protocol. (iii) It is hard for each of the two common-used difficulty adjustment algorithms to achieve higher system throughput and security.

Migration-based Dynamic Platform (MDP) technique, a type of Moving Target Defense (MTD) techniques, defends against sophisticated cyber-attacks by randomly and dynamically selecting a platform for executing service/job. Security defense mechanisms protect service/job usually at the cost of degrading its performance. Therefore, it is valuable to make a trade-off between service/job security and its performance. However, previous researches on MTD techniques either focused on analyzing MTD effectiveness of protecting service/job or studied service/job performance with the assumption that attacks on service/job make no influence on its execution. This article aims to apply analytical modeling techniques to investigate the impact of MDP technique on job completion time in a system under attack. We use Stochastic Reward Nets (SRNs) to develop a Markov chain-based model for capturing typical behaviors of the adversary, the vulnerable system and a job. The formulas are derived for calculating the metrics of interest. Numerical analysis is conducted to study the impact of key parameters on job completion time and job security loss.

The well known Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is not well suited to blockchain-based Internet of Things (IoT) systems which cover large geographical areas. To reduce queuing delays and eliminates a permanent leader as a single point of failure, we use a multiple entry, multi-tier PBFT architecture and investigate the distribution of orderers that will lead to minimization of the total delay from the reception of a block of IoT data to the moment it is linked to the global blockchain. Our results indicate that the total number of orderers for given system coverage and total load are main determinants of the block linking time. We show that, given the dimensions of an area and the number of orderers, partitioning the orderers into a smaller number of tiers with more clusters will lead to lower block linking time. These observations may be used in the process of planning and dimensioning of multi-tier cluster architectures for blockchain-enabled IoT systems.

Practical Byzantine Fault Tolerance (PBFT) is a widely used consensus protocol which is sensitive to malicious behavior of the consensus leader, esp. when that behavior occurs only sporadically which makes it harder to detect. We propose an arbitration scheme suitable for Internet of Things (IoT) applications in which a replicated blockchain ledger is used for storage of IoT data. The scheme removes most of the vulnerabilities at the expense of slight performance degradation. We further describe a modification which adds redundancy to remove the possibility of additional attacks. Experimental results confirm the validity and efficacy of the proposed scheme.

Various Machine Learning (ML) models have been developed for malware detection. But their widespread application is challenged by adversarial attacks using adversarial malware examples. Generative Adversarial Networks (GAN) is one of the effective approaches to help build possible unknown attacks and expose the vulnerability of targeted systems. The existing GAN-based ML models have the weaknesses of unstable training and low-quality adversarial examples. In this paper, we propose a novel Mal-LSGAN model to tackle these weaknesses. By using a Least Square (LS) loss function and new activation function combinations, Mal-LSGAN achieves a higher Attack Success Rate (ASR) and a lower True Positive Rate (TPR) in 6 ML detectors, compared with the existing MalGAN and Imp-MalGAN. In Multi-Layer Perceptron (MLP), Mal-LSGAN can even decrease TPR from 97.81% of original examples to 2.92% of adversarial examples. The experimental results also demonstrate that Mal-Lsgangets the preferable transferability of adversarial malware examples.

The use of blockchain for Internet-of-Things (IoT) data recording necessitates an efficient and scalable consensus mechanism. In this paper, we describe a two-tier architecture in which IoT data is packaged in batches or blocks, approved by a low-tier cluster first and a top-tier cluster second, before being added to the replicated blockchain ledger. Both tiers use PBFT-like consensus enhanced with multiple-entry point operation using bandwidth reservation. This approach eliminates the dependence on a single primary leader that is characteristic for PBFT-like protocols, and allows the system to be deployed in geographically wide area. We provide a detailed probabilistic analysis of the proposed architecture using a discrete time Markov chain, and show that system performance depends on the number of ordering nodes in each cluster and the number of low-tier clusters.

Electronic health (e-health) information system relies on cloud computing technologies to provide massive medical data computing and storage services. Especially, the recently proposed Machine Learning as a Service (MLaaS) on these medical data can not only effectively improve the healthcare service quality, but also support the end users with limited computing resources. However, MLaaS on the massive medical data faces the challenge of privacy. Homomorphic encryption technology has been explored to assure the privacy of medical data owners in MLaaS but with the weaknesses of limited homomorphic operations and low efficiency. To alleviate these weaknesses, this paper proposes a novel privacy-preserving non-collusion dualcloud (NCDC) model-based e-health information system using neural network (NN) computing. The system can not only assure medical data privacy through adopting homomorphic encryption technology but also assure NN model privacy by adding fake neurons to the NN. In addition, the proposed e-health information system also has the following advantages: (i) Simple key generation. (ii) No constraint on the size of medical data to be encrypted. (iii) The less loss of prediction accuracy between encrypted and original medical data. (iv) Supporting more homomorphic operations and having better computing efficiency through experiment verification.

Abstract Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN module and AT module. LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples. AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD). Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack. The results also verify the performance of the generated RMD in the recognition rate of AME.

Connected vehicles (CVs) are getting increasing attention in intelligent transportation systems (ITSs). In terms of the stringent security and anonymity issues, a novel anonymous and decentralized payment platform is demanded. Bitcoin is regarded as a potential solution. But the existing Bitcoin and its various improvements lack a beforehand anti-fraud Bitcoin deposit scheme, which is necessary to prevent potential fraud risks from Bitcoin to CVs. This paper aims to build a novel anti-fraud deposit scheme as a seamless bridge between CV networks and the Bitcoin payment platform. We propose a Bitcoin-to-Connected-Vehicle deposit scheme (Bit2CV) with an outsourcing endorsement in order to build an anti-fraud deposit transaction (dtr). Firstly, Bit2CV leverages a special Bitcoin-opcode-OP_RETURN based method to record the dtr, the CV's request, and the endorsement together on Blockchain ledger. This method can achieve security features, including non-repudiation, privacy-friendly endorsement, future audit, and anti-fraud. Meanwhile, Bit2CV is fully decentralized without any involvement of centralized authorities and fully compatible with the current Bitcoin network. We conduct the security analysis and also simulations for performance evaluation. In a typical scenario of our simulation, Bit2CV total time cost is less than 477.03 ms, and the size of the endorsement is 1,732 bytes, which is much less and smaller than transaction confirmation time and the average block size, respectively. These designs and results demonstrate that Bit2CV is feasible and practical.

Selfish mining, as a serious threat to blockchain, has been attracting attentions from academic and industry. Stochastic modeling has been explored to quantitatively investigate selfish mining in imperfect blockchain networks. However, prior modeling-based analysis approaches have some of the following issues: (1) only focus on Bitcoin or Ethereum, or (2) ignore extended forks and just consider natural forks, or (3) only compute the mining revenue without assessing the performance and security of the blockchain system when the system suffers from selfish mining. In this paper, we aim to address these issues. We build a Markov chain to make quantitative analysis of selfish mining in imperfect Bitcoin and Ethereum networks with natural and extended forks. Formulas are derived to calculate the mining revenue for the selfish pool (comprising selfish miners) and honest miners, respectively. Moreover, we derive the formulas of performance metrics (namely, transactions per second and stale block ratio) and the formula of security metric (namely, the probability of double-spending success) of the system. These quantitative results can help understand the impact of selfish mining on imperfect blockchain networks and then help the detection of selfish mining.

Moving target defense (MTD), emerging as a game-changer in the cyber defense area, has got a lot of attention and development recently. As a proactive defense technique, MTD dynamically changes system attributes in order to create more uncertainties of the system and has been proved to be effective against cyber attacks. Beyond this, there is still a lack of researches with respect to the quantitative analysis of the effect of MTD on system performance. This paper aims to quantitatively investigate how MTD affects system performance while bringing security. We develop Markov process-based models for two different MTD strategies and derive the formulas for metrics of interest. We carry out simulation experiments to validate our proposed models with Mininet. Furthermore, numerical analysis is conducted for comparing these two different strategies in terms of system performance. The numerical results also show how different parameters affect the evaluation metrics. Our models can help defenders conFigure the MTD system in the most suitable way.

Data propagation in the Bitcoin network is inefficient due to its permissionless nature and the lack of multicast/broadcast features. In particular, the number of messages needed to propagate a single transaction is very high which is rather wasteful in terms of bandwidth utilization. In this work we propose a simple modification of Bitcoin software that allows a vast reduction of the number of messages needed for propagating a transaction over the network. The modification consists of deferring the transaction announcements until a certain predefined number of new transactions is collected. We show that the number of messages can be substantially reduced in this manner. The price to pay for this reduction is an increase in transaction propagation delay. However, the tradeoff between the reduction of traffic and transaction delay can be minimized by judicious choice of the threshold number of deferred transactions.

Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is unsuitable for Internet of things (IoT) applications due to the need for a single view leader. In this work we propose to augment PBFT with a contention-based bandwidth reservation phase that allows any ordering node to initiate a new consensus round. We model the operation of the proposed algorithm, and show that system throughput is not significantly affected by the increased communication load when the number of ordering nodes increases, as the load per ordering node actually decreases. In this fashion, the proposed algorithm allows the deployment of wide area IoT networks that use PBFT-based consensus.

Compact blocks and compact block protocol are a recent addition to the Bitcoin (BTC) data propagation protocol that aims to reduce bandwidth requirements and, possibly, reduce latency as well. In this work we have evaluated improvement of operation of BTC network under a mix of regular and compact block traffic in low-bandwidth mode. We have performed queuing analysis of the BTC network and obtained performance descriptors of block and transaction delivery times as well as forking probability. Although compact block size is more than an order of magnitude smaller than regular block size, improvement of delivery times is within bounds of 0% to 20%. Forking probability shows highest improvement of 25%. However, further analysis shows that compact block protocol requires high transaction traffic in order to prevent transaction pool deficit which causes further interaction among the peers.

This work deals with problem of deployment of Practical Byzantine Fault Tolerance (PBFT) consensus algorithm in Internet of things (IoT) applications requiring strict order among records linked in blockchains at multiple geographical points. Due to the needs of reliability and coverage of larger geographical areas it becomes necessary to extend current PBFT systems with single entry node towards multiple entry nodes. In this work we model PBFT systems with single and multiple entry points and compare their performance. We have implemented multiple entry system using CSMA/CA algorithm over fully connected P2P networks. We have compared two systems with four orderers against increasing system load and against increasing geographical coverage. Our results indicate that system with four entry points has double capacity over the system with single entry point.

This work proposes and evaluates a Practical Byzantine Fault Tolerance (PBFT)-inspired ordering service for IoT data collection and block formation in a permissioned blockchain environment. We implement an algorithm for atomic insertion of request to ordering service in which each ordering node can initiate insertion and lead the consensus protocol, unlike traditional current implementations of ordering service which rely on a single point of entry. We have modeled record insertion service into the P2P ordering service with constant number of nodes, variable request rate, and known distribution of one-way propagation delays among the ordering peers. Performance results show the behavior of system descriptors and the limits of system capacity expressed in terms of total request rate.

Abstract The rapid and widespread adoption of internet of things-related services advances the development of the cloud-edge framework, including multiple cloud datacenters (CDCs) and edge micro-datacenters (EDCs). This paper aims to apply analytical modeling techniques to assess the effectiveness of cloud-edge computing resource allocation policies from the perspective of improving the performance of cloud-edge service. We focus on two types of physical device (PD)-allocation policies that define how to select a PD from a CDC/EDC for service provision. The first is randomly selecting a PD, denoted as RandAvail. The other is denoted as SEQ, in which an available idle PD is selected to serve client requests only after the waiting queues of all busy PDs are full. We first present the models in the case of an On–Off request arrival process and verify the approximate accuracy of the proposed models through simulations. Then, we apply analytical models for comparing RandAvail and SEQ policies, in terms of request rejection probability and mean response time, under various system parameter settings.

Abstract Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models. However, the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion. Previously, the seq2seq model has been explored to augment system-call sequences. Following this work, we propose a gated convolutional neural network (GCNN) model to thoroughly extract the potential information of augmented sequences. Also, in order to enhance the model’s robustness, we adopt adversarial training to reduce the impact of adversarial examples on the model. Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm. The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance. Furthermore, GCNN with adversarial training can enhance robustness significantly.

Machine learning (ML) algorithms provide better performance than traditional algorithms in various applications. However, some unknown flaws in ML classifiers make them sensitive to adversarial examples generated by adding small but fooled purposeful distortions to natural examples. This paper aims to investigate the transferability of adversarial examples generated on a sparse and structured dataset and the ability of adversarial training in resisting adversarial examples. The results demonstrate that adversarial examples generated by DNN can fool a set of ML classifiers such as decision tree, random forest, SVM, CNN and RNN. Also, adversarial training can improve the robustness of DNN in terms of resisting attacks.

In this work we analyze the blockchain forking events, blockchain partitioning, and duration of inconsistent state of the ledger in a Bitcoin delivery network. Using a comprehensive probabilistic model, we obtain the probability distribution of two- and three-way forks, the forked partition sizes, and the duration of ledger inconsistency until the resolution. We show that the three-way forking probability is substantially lower than that of a two-way forking and that the partition sizes in the case of two-way forking tend to equalize when the number of nodes increases. Finally, we show that the duration of ledger inconsistency state exhibits long tail probability distribution which means that successive forking events can force the ledger to remain inconsistent for long time.

Moving Target Defense (MTD) technology protects a target system by complicating the attacking process of adversaries. It has been gaining more and more attention with the massive growth of vulnerabilities and the widespread deployment of critical network services. This paper aims to analyze service Mean Time To Failure (MTTF) in a vulnerable network system which suffers attacks from adversaries. The system consists of multiple Physical Machines (PM) and each PM can support Docker Containers (DC) to run service. It applies Dynamic Platform Protection Technique (DPT), a kind of MTD techniques, to reduce the impact of attacks on service. A DC can be live migrated among these PMs in order to provision continuous service to users. We propose a model which captures the service behaviors during the service execution in the system. Our model allows both service residency/execution time at a PM and service migration time to be generally distributed. We also derive the formula for calculating MTTF and its approximate accuracy is validated through comparing analytical results with simulation results. Moreover, a formula is proposed to predict the total cost of the system, which helps administrators manage the network system effectively.

In this work we provide comprehensive analytical model for Bitcoin distribution network. We apply Jackson network model on the whole Bitcoin network where individual nodes operate as priority M/G/1 queuing systems. Data arrival process to the nodes is modeled as a non-homogeneous Poisson process in which the data arrival rates to the nodes are derived from the analytical model of gossip data delivery protocol. This model considers random probability distribution of node connectivity. Performance results include network distribution time for blocks, node response time for blocks, and populations of data distribution algorithms as functions on network size. Usefulness of this model is demonstrated by efficiently computing the forking probability for the Bitcoin blockchain.

Model-based performance evaluation of large-scale PaaS Fog Datacenter requires to develop a hierarchical model, which is usually composed of a series of monolithic models. This paper proposes an approximate analytic modeling approach to evaluate the performance of a pool of active containers on a PaaS Fog physical node under batch task arrivals, by using an $M^{[\mathrm{x}]}/G/m/m+K$ queue. We describe the details of the proposed model and the formulas for calculating performance measures of interest. Experiment results indicate that the proposed approach (including the model and the formulas) can approximately capture the system behaviors even when the task service-time distribution has a large coefficient of variation (>1.5).

Vehicular fog computing (VFC) complements vehicular cloud computing as a promising solution for accommodating the surge of mobile traffic and reducing latency. This paper considers vehicular fog service (VFS) provided by a vehicular fog (VF), which is formed on-the-fly by integrating computing and storage resources of parked vehicles. VF dynamicity, due to vehicles' random arrivals and departures, poses a number of challenges for reliable and secure VFS provision to client vehicles. We propose a novel mechanism which consists of a VF construction method and a VFS access method to ensure VFS reliability and security without sacrificing performance. The reliability and security of VFS under our mechanism are discussed in detail. Moreover, we investigate the impact of the proposed mechanism on VF throughput and show that the mechanism is lightweight enough to be used in the latency-sensitive VFC.

As modern vehicles and distributed fog services advance apace, vehicular fog services (VFSs) are being expected to span across multiple geo-distributed datacenters, which inevitably leads to cross-datacenter authentication. Traditional cross-datacenter authentication models are not suitable for the scenario of high-speed moving vehicles accessing VFS, because these models either ignored user privacy or ignored the delay requirement of driving vehicles. This paper proposes a blockchain-assisted lightweight anonymous authentication (BLA) mechanism for distributed VFS, which is provisioned to driving vehicles. BLA can achieve the following advantages: 1) realizing a flexible cross-datacenter authentication, in which a vehicle can decide whether to be reauthenticated or not when it enters a new vehicular fog datacenter; 2) achieving anonymity, and granting vehicle users the responsibility of preserving their privacy; 3) it is lightweight by achieving noninteractivity between vehicles and service managers (SMs), and eliminating the communication between SMs in the authentication process, which significantly reduces the communication delay; and 4) resisting the attack that the database governed by one center is tampered with. BLA achieves these advantages by effectively combining modern cryptographical technology and blockchain technology. These security features are demonstrated by carrying out security analysis. Meanwhile, extensive simulations are conducted to validate the efficiency and practicality of BLA.

In this work we examine the tradeoffs involved in cache operation in an IoT domain controlled by a single Constrained Application Protocol (CoAP) proxy using IEEE 802.15.4 low power wireless personal area network technology. As the requirements for fresh data supplied to the clients and the reduction of traffic in the IoT domain are contradictory, a viable tradeoff is sought to obtain satisfactory performance. To this end, we compare the performance of a recently proposed group-based approach to that of a Bernoulli-scheduled, server-initiated proactive refresh, and show that the latter achieves superior cache hit ratio while offering substantial savings in terms of actual number of IoT domain messages exchanged.

In this paper we develop lightweight algorithms for monitoring and estimating data lifetime and round trip time at CoAP proxy. We deploy these algorithms in CoAP IoT domain with observe feature with random inter-observation times which can be a consequence of parameterized queries. Algorithms are based on kernel estimation of probability density distributions (pdf). As a result proxy maintains approximate pdfs of these parameters which can be used in congestion control and/or anomaly detection in IoT domain. Results show that estimations with 400-500 samples render satisfactory tradeoff between accuracy and computational complexity even under skewed probability distributions such as exponential distribution.

This paper aims to analyze transient security and dependability of a vulnerable critical system, under vulnerability-related attack and two reactive defense strategies, from a severe vulnerability announcement until the vulnerability is fully removed from the system. By severe, we mean that the vulnerability-based malware could cause significant damage to the infected system in terms of security and dependability while infecting more and more new vulnerable computer systems. We propose a Markov chain-based survivability model for capturing the vulnerable critical system behaviors during the vulnerability elimination process. A high-level formalism based on Stochastic Reward Nets is applied to automatically generate and solve the survivability model. Survivability metrics are defined to quantify system attributes. The proposed model and metrics not only enable us to quantitatively assess the system survivability in terms of security risk and dependability, but also provide insights on the system investment decision. Numerical experiments are constructed to study the impact of key parameters on system security, dependability and profit.

Availability evaluation of a virtualized system is critical to the wide deployment of cloud computing services. Time-based, prediction-based rejuvenation of virtual machines (VM) and virtual machine monitors, VM failover and live VM migration are common high-availability (HA) techniques in a virtualized system. This paper investigates the effect of combination of these availability techniques on VM availability in a virtualized system where various software and hardware failures may occur. For each combination, we construct analytic models rejuvenation mechanisms to improve VM availability; (2) prediction-based rejuvenation enhances VM availability much more than time-based VM rejuvenation when prediction successful probability is above 70%, regardless failover and/or live VM migration is also deployed; (3) failover mechanism outperforms live VM migration, although they can work together for higher availability of VM. In addition, they can combine with software rejuvenation mechanisms for even higher availability; (4) and time interval setting is critical to a time-based rejuvenation mechanism. These analytic results provide guidelines for deploying and parameter setting of HA techniques in a virtualized system.

Analyzing the survivability of a cloud service is critical as the application or service migration from local to cloud is an irresistible trend. However, former research on cloud service or virtual system (VS) availability and/or reliability was only carried out from the perspective of steady state. This paper aims to analyze the survivability of the cloud service after a service breakdown occurrence by presenting a model and the closed-form solutions with the use of continuous-time Markov chain. The service breakdown may be caused by virtual machine (VM) and/or VM monitor (VMM) bugs or software rejuvenation and/or host failures and NAS (Network Area Storage) failures. In order to improve the cloud service survivability, the VS applies two techniques: VM failover and VM live-migration. Through the model proposed and the survivability metrics defined in this paper, we are able to quantitatively assess the system survivability while providing insights into the investment efforts in system recovery strategies. In order to study the impact of key parameters on system survivability, this paper also provides a parameter sensitivity analysis through numerical experiments.

Heterogeneity prevails not only among physical machines but also among workloads in real IaaS Cloud data centers (CDCs). The heterogeneity makes performance modeling of large and complex IaaS CDCs even more challenging. This paper considers the scenario where the number of virtual CPUs requested by each customer job may be different. We propose a hierarchical stochastic modeling approach applicable to IaaS CDC performance analysis under such a heterogeneous workload. Numerical results obtained from the proposed analytic model are verified through discrete-event simulations under various system parameter settings.

While Blockchain network brings tremendous benefits, there are concerns whether their performance would match up with the mainstream IT systems. This paper aims to investigate whether the consensus process using Practical Byzantine Fault Tolerance (PBFT) could be a performance bottleneck for networks with a large number of peers. We model the PBFT consensus process using Stochastic Reward Nets (SRN) to compute the mean time to complete consensus for networks up to 100 peers. We create a blockchain network using IBM Bluemix service, running a production-grade IoT application and use the data to parameterize and validate our models. We also conduct sensitivity analysis over a variety of system parameters and examine the performance of larger networks

Cloud data center (CDC) network virtualization is being regarded as a promising technology to provide performance guarantee for cloud computing applications. One critical issue in CDC network virtualization technology is virtual data center (VDC) embedding, which deals with the CDC physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VDC embedding problem is NP-hard, even in the offline case. Node heterogeneity and CDC network scale bring challenges to the VDC embedding. This paper aims to embed a VDC in a robust and green way. We propose two effective, computation-efficient and energy-efficient embedding algorithms. Extensive simulations under various network scales and topologies are carried out to compare the proposed algorithms with the existing VDC embedding algorithms in terms of the VDC acceptance ratio, the long-term revenue of the cloud service provider (CSP), the CDC’s long-term energy consumption in light-load CDCs, and in terms of CSP's long-term revenue in heavy-load CDCs.

Transient survivability analysis of a virtualized system (VS) is critical to the wide deployment of cloud services. The existing research of VS availability and/or reliability focused on the steady-state analysis. This paper presents a model and the closed-form solutions to analyze the survivability of both cloud service and VS after a service breakdown occurrence by using continuous-time Markov chain. Service breakdown may be caused by software rejuvenation of virtual machine (VM) and/or VM monitor (VMM), or caused by VM and/or VMM bugs. The VS applies two techniques for improving service survivability: VM failover and live VM migration. The proposed model and the defined survivability metrics not only enable us to quantitatively assess the system survivability but also provide insights on the investment efforts in system recovery strategies. Sensitivity analysis through numerical analysis is carried out to study the impact of key parameters on system survivability.

Cloud computing has been bringing fundamental changes to computing models in the past few years. Infrastructure as a Service (IaaS), a kind of basic cloud services, is provisioned to customers in the form of virtual machines (VMs). The increasing demands for IaaS cloud services require the performability analysis of cloud infrastructure. Analytic modeling is one of the effective evaluation approaches. This paper aims to develop a monolithic model, by using continuous time Markov chain (CTMC), for a IaaS CDC, which (1) consists of active and standby physical machines (PMs), (2) allows PM migration among active and standby PM pools, (3) all jobs are homogeneous, and (4) a running job could continue its running by using idle active PMs when the PM working for this job fails. Although a monolithic CTMC model for IaaS Cloud performability analysis may face largeness and stiffness problems, it could be used to verify the scalable approximate model. We present the details of state transition rules of the proposed model and the formula for computing metrics, including the immediate service probability, the mean response time and so on. Numerical analysis and simulations are carried out to verify the accuracy of the proposed model.

Embedding virtual network requests in an underlying physical infrastructure, the so-called virtual network embedding (VNE) problem, has attracted significant research interests already. A realistic scenario might entail embedding multiple VN requests (MVNE) that arrive simultaneously (batch arrivals). The existing heuristic MVNE approaches neither consider the coordination among multiple VNR embeddings nor embed all the arriving VNRs simultaneously considering the available physical resources. This paper considers the MVNE problem in the scenario where the available physical resources may not be sufficient to satisfy the physical resource demands of all the VNRs in the batch. We explore applying genetic algorithm (GA) to handle the MVNE problem. We propose an algorithm to decide which VNRs could be mapped together. Extensive simulations are carried out to evaluate the performance of the proposed algorithms in terms of the VN acceptance ratio and the long-term revenue of the service provider.

This paper develops a novel approximate analytical model to evaluate the performance of active virtual machines in IaaS clouds using an M/G/m/m+K queue. The proposed model, combined with the transform-based analytical approach, enables the computation of the probability distribution of the number of jobs in the system and subsequently a set of performance measures, including the mean number of jobs in the system, the mean response time, the probability of immediate service, and the blocking probability. Compared to the existing Markov models of cloud data centers, our approach can reflect the system behavior more accurately even when the service-time distribution has a large coefficient of variation (> 1.5) in a medium-sized IaaS cloud. Numerical results obtained from the proposed analytical model are verified through extensive simulations under various system parameter settings, and compared with the results from existing models.

Summary Infrastructure as a Service cloud provides elasticity and scalable virtual machines (VMs) as computing service to multiple tenants, but the tenants lose the full control of their data. Measuring the integrity of critical files of the VMs and providing the integrity attestation to the tenants on the basis of TCG trusted computing techniques is an effective way to alleviate their anxiety. This paper considers how to measure the integrity of the processes run in guest VMs and files opened in guest VMs. We propose an out‐of‐the‐box integrity measurement approach to measure the integrity of critical files through system call (syscall) interception without any modification of the guest VMs. Out‐of‐the‐box integrity measurement approach can not only measure the integrity of all files that have been considered by existing approaches but also measure the integrity of the system configuration files, program loaders, and script interpreters, which affect the system behaviors and integrity. The ability of supporting both system and manual measurement policies makes our approach flexible. We implement this approach in Xen hypervisor with little modification of the existing syscall interception method, and this approach can be ported to other virtualization platform easily. Copyright © 2014 John Wiley & Sons, Ltd.

Role-based access control (RBAC) is being today's dominant access control model due to its potential to mitigate the complexity and cost of access control administration. However, the migration from the access control lists (ACL) to RBAC for a large administration system may consume significant efforts, which challenges the adoption of RBAC. Role mining algorithms can significantly reduce the migration cost by providing a partially automatic construction of an RBAC policy. This paper explores Artificial Intelligence (AI) techniques in designing role mining algorithms, which can optimize policy quality in terms of policy size, user-attribute-based interpretability of the roles, and the combination of size and interpretability. We propose two algorithms, genetic algorithm (GA)-based and ant colony optimization (ACO)-based. GA-based algorithm works by starting with a set of all candidate roles and repeatedly removing roles. ACO-based algorithm works by starting with an empty policy and repeatedly adding candidate roles. We carry out extensive experiments with publicly available access control policies. The simulation results indicate that ®the proposed algorithms achieves better performance than the corresponding existing algorithms. (2) GA-based approach produces better results than ACO-based approach.□

Integrating system-level virtualization technology with Trusted Computing technology can significantly improve system security. The open-source virtual TPM facility, shipped with the open-source hyper visor Xen, aims to provide the illusion of a physical TPM to TPM-based trusted software executed in trusted virtual machines (TVMs) such that TPM-based trusted software works well in a TVM as in a native Operating System (OS). However, it is not true for the trusted software which works in a trusted para-virtual machine (TPVM). The TPM command packets sent in the TPVM OS boot phase may cause the TPVM OS boot failure. This paper design and implement E2VT, an effective and efficient mechanism for preventing the TPVM OS boot failure while (1) being transparent to the TPVM system, (2) maintaining the original system performance, (3) making minimal modifications to the existing architecture. We validate our analysis and design through experiments.

The virtual network embedding (VNE) problem has been receiving significant research interest lately. A realistic scenario might entail embedding multiple VN requests (MVNE) that arrive simultaneously (batch arrivals). The existing MVNE approaches either did not consider the coordination among multiple VNR embeddings or embedded all the arriving VNRs simultaneously without considering the substrate network state. This paper considers the MVNE problem in the scenarios where the available physical resources may not be sufficient to satisfy the physical resource demands of all the VNRs in the batch. We formulate the problem as mixed integer programs, which not only provide optimal MVN embedding but also improve the network service provider's long-term revenue. Simulation results validate the revenue gains of the proposed approach over the existing exact and heuristic MVNE approaches.

This paper aims to defeat TCG TOCTOU attacks occurring in trusted para-virtualized machines (TPVM). We propose a robust and efficient response mechanism (RERM). Compared to the existing response mechanisms, RERM is more effective in defeating the TPVM TCG TOCTOU attacks without incurring CPU overhead during the normal system execution. We verify the security ability of RERM via both model checking based formal verification techniques and experiments.

The virtual network embedding (VNE) problem deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in the offline case. The genetic algorithm (GA) is an excellent approach to solving complex problems in optimization with difficult constraints. This paper explores applying GA to handle the VNE problem. We propose two GA-based VNE algorithms and evaluate them by comparing with the existing state-of-the-art VNE algorithms, including PSO-based VNE approaches. Extensive simulation results validate the capability of the proposed GA-based VNE algorithms in terms of the InP long-term revenue and the VN embedding cost.

The current TCG architecture suffers from the Time-of-Check-To-Time-of-Use (TOCTOU) attacks in commodity PC operating systems (OS), in which kernel rootkits can get unrestricted access to OS resources. VMM-based approaches running at a privilege level higher than that of virtual machine (VM) kernel can effectively detect dynamic or static data attacks occurring in VMs. This paper proposes a lightweight response mechanism (LWRM) for TCG TOCTOU attacks occurring in VMs. LWRM has the following features: (1) compared to the existing response mechanism, LWRM is more effective in defeating the TCG TOCTOU attacks; (2) LWRM imposes less overhead on the system during normal execution; (3) LWRM is transparent to the kernel rootkits; and (4) LWRM can work in the scenarios with more than one run-time trusted virtual machine. We describe the design idea and the implementation by using the Xen virtual machine monitor (VMM) and the virtual TPM facility shipped with the Xen.

Interrupt coalescing (IC) technique has been used in general-purpose operating systems to mitigate receive livelock (RL) problem in gigabit Ethernet network hosts. Schemes for dynamically tuning the interrupt coalescing behavior of a communication interface based on traffic load or system state have been proposed. However, all the existing IC schemes are designed using heuristics. In this paper we present an analytical model for the IC technique and carry out a detailed study of existing IC schemes in terms of their performance characteristics including system goodput, CPU consumption and latency. We validate our analysis through measurement-based experiments.

Gigabit Ethernet is now finding wider deployment in computer networks. The conventional operating system suffers from the receive livelock problem in Gigabit Ethernet networks. The device hybrid (interrupt + polling) scheme has been widely used to overcome this problem in current operating systems such as GNU/Linux and FreeBSD. However, controlling the polling time without regard to the system state can degrade the ability of a hybrid scheme in some situations. This paper focuses on the system performance of the operating systems that employ the device hybrid scheme in kernel space. A queue-based adaptive polling (QAPolling) scheme is introduced that: (1) significantly improves system goodput and reduces packet loss over a wide range of computer hardware configurations and traffic conditions, (2) is scalable and easily deployed. The key idea behind QAPolling is to adjust the polling time adaptively according to the information of the application receiving queues, which are in kernel space and change with the system state, instead of the packet arrival rate. We validate our design through experimental results in Gigabit Ethernet networks.

Studies of the performance of interrupt-driven operating systems in high-speed networks have brought forth the problem of receive livelock. Device hybrid interrupt-polling and interrupt coalescing are two common techniques used in general-purpose operating systems to mitigate this problem. Adaptive schemes based on local knowledge have been proposed for each technique above. However, all the schemes proposed so far are designed using heuristics. In addition, the capabilities of the proposed schemes have not been systematically compared. In this paper, we first analyze the capabilities of these schemes by investigating the relationship between key system parameters and system goodput in different packet protocol processing modes under heavy traffic load. Then we propose a robust device hybrid interrupt-polling (RHIP) scheme which achieves high system goodput, low packet loss and good latency with low consumption of CPU cycles, compared to other schemes. The key idea of RHIP is to use the recipient's buffer information to adjust the interrupt rate and the protocol processing time. We validate our analysis and design through several experiments.

Achieving fair bandwidth distribution among uplink and downlink flows in the infrastructure based wireless local area networks (WLAN) which the distributed coordination function (DCF) mode is difficult. In this paper we present a new control theoretic approach to achieve a fair bandwidth distribution among the flows regardless of the transport protocol used by the flows. In addition, we explore methods to improve the channel bandwidth utilization and reduce the delay while improving the fair distribution. Our approach combines an AQM scheme for IFQ queue and the MAC layer design to achieve this goal. The effectiveness of our approach is demonstrated through extensive simulations over a wide range of network scenarios.

Recent research studies in over-provisioned networks have shown that the assured forwarding (AF) service in the current differentiated services (DiffServ) architecture fails to provide bandwidth assurance in some situations. The paper focuses on the situation where adaptive and non-adaptive traffic coexist in the same real queue at routers and the buffer management scheme treats the traffic of the same priority in the same AF class indiscriminately. An enhanced RIO is introduced, which, without excessively penalizing non-adaptive flows, can: (1) significantly improve bandwidth assurance of adaptive AF flows; (2) alleviate the starvation imposed on adaptive best-effort flows. These goals are achieved by doing the following when the failure of bandwidth assurance is detected: (1) mapping adaptive OUT traffic and non-adaptive OUT traffic to different virtual queues; (2) adapting the queue length thresholds according to whether bandwidth assurance is achieved. We validate our design through simulations.

Several active queue management (AQM) mechanisms have been proposed in the literature to provide better support for congestion. However, their performance is examined mainly in best-effort networks. In this paper, we present an empirical study of the effects of these AQM mechanisms on the performance of Assured Forwarding (AF) Services in the Differentiated Services (DiffServ) framework. In addition, we investigate the interaction of AQMs with intelligent traffic conditioners. The major conclusions from our study are: (1) When the standard traffic conditioner is used, the transient response and the ability of controlling queue length of an AQM scheme not only affect the achievement of bandwidth assurance, but also affect the attainment of excessive bandwidth and link throughput. (2) The behaviors of an AQM affect the ability of intelligent traffic conditioners. (3) Rate-based AQM schemes perform better than queue-based AQM schemes; self-tuning AQM schemes perform better than AQMs with fixed-gains in terms of the above performance metrics.

In this paper, we consider two fairness problems (downlink/uplink fairness and fairness among flows in the same direction) that arise in the infrastructure WLAN. We propose a virtual queue management approach, named VQ-RED to address the fairness problems. We demonstrate the effectiveness of our approach by conducting a series of simulations. The results show that compared with standard DCF, VQRED not only greatly improves the fairness, but also reduces packet delays.

Research studies have uncovered the unfair WLAN bandwidth distribution between uplink and downlink TCP flows in the infrastructure WLAN using the distributed coordination function mode. Different mechanisms at or above the MAC layer have been proposed for handling this problem. This paper presents a simple but effective approach in order to achieve the fair distribution by using an adaptive queue management mechanism to manage the queue between the logic link and MAC layers at the access point. Extensive simulation results show that this approach outperforms other mechanisms discussed in the literature over a wide range of network scenarios in terms of fair distribution, small delay, and high WLAN bandwidth utilization

Significant research effort has been devoted to designing robust active queue management (AQM) mechanisms for the Internet, with mixed results. In this paper, we apply sliding mode control theory plus integral control to design a robust controller for AQM, which stabilizes the instantaneous queue length around the desired value with fast transient response over a wide range of network dynamics. We validate our design through extensive simulations and compare its performance with some AQM mechanisms published in the literature.

In this paper a simple robust proportional-integral (R-PI) controller is proposed for active queue management (AQM). We assume that TCP/AQM dynamics can be described by the linearized TCP/AQM model (C. V. Hollot et al., April 2001). R-PI aims to address the tradeoff between responsiveness and stability and the tradeoff between responsiveness and high link utilization over a large range of structured and unstructured uncertainties. This controller achieves these goals by varying its control parameters according to the system state. We show that the closed-loop system is asymptotically stable as long as the control parameters are time-invariant and varying in a range. Extensive simulation results demonstrate the robust ability of R-PI compared with some other AQM mechanisms in the literature.

The assured forwarding (AF) based service in a differentiated services (Diffserv) network fails to provide bandwidth assurance to aggregates in some circumstances. Several intelligent marking mechanisms have been proposed in the literature to improve bandwidth assurance for aggregates using the knowledge gathered at the ingress nodes. In this paper, we apply a control theoretic approach to this problem. We design a nonlinear proportional-integral (NPI) controller, called NPI-ACT, for adapting the CIR threshold. Performance results using extensive simulations demonstrate significant improvement using NPI-ACT in achieving bandwidth assurance over a wide range of network conditions compared to earlier mechanisms proposed in the literature.

Recent research studies have shown that assured forwarding (AF) service in the current differentiated services (Diffserv) framework does not provide bandwidth assurance in some circumstances. This paper proposes an adaptive marking threshold mechanism, called adaptive CIR threshold (ACT), which aims to improve bandwidth assurance for aggregate flows sharing the same AF class only based on local knowledge. Extensive simulation results demonstrate significant improvement with ACT in bandwidth assurance under various conditions: different round trip times (RTT), different numbers of micro-flows in an aggregate, different target rates, different packet sizes, and the presence of non-adaptive flows, compared with earlier mechanisms proposed in the literature.

Recent research studies have shown that assured forwarding (AF) service in the current differentiated services (Diffserv) framework does not provide bandwidth assurance in some circumstances. This paper proposes a mechanism, called Adaptive CIR+PIR Threshold (ACPT), which improves bandwidth assurance and domain throughput simultaneously. Extensive simulation results demonstrate significant improvement with ACPT in bandwidth assurance and domain throughput under various conditions: different round trip times (RTT), different numbers of micro-flows in an aggregate, different target rates, different packet sizes, and the presence of nonadaptive flows, compared to other mechanisms proposed in the literature.