Blockchain-based certificate management schemes provide a distributed approach for Public Key Infrastructure through the integration of blockchain services, thereby enhancing transparency and security in identity authentication. However, existing schemes often suffer from limited scalability when accommodating new CAs, high overhead of blockchain systems, and a high false-positive rate in revocation status checks. To address above issues, we propose MHCertChain, a multi-CA hierarchical certificate blockchain with low overhead. Specifically, a two-layer blockchain structure including the main chain and sub-chains is designed to enhance scalability, while the main chain stores trust paths between CAs and each automatically deployed sub-chain records user certificates issued by an end-entity CA. Then, we propose a lightweight dual-signature certificate format that contains certificate location information without requiring any external certificate location method outside the blockchain. Considering time characteristics of certificates, a time-partitioned cuckoo filter is proposed with a low false positive rate and accelerates revocation status query. Moreover, we deduce an optimal global performance parameter of such filter through mathematical modeling. We present a thorough security analysis of our MHCertChain utilizing the universally composable framework, and extensive experiments demonstrate that the CPU overhead and false positive rate are reduced by 70% and 95%, respectively, compared to state-of-the-art schemes. Blockchain-based certificate management schemes provide a distributed approach for Public Key Infrastructure through the integration of blockchain services, thereby enhancing transparency and security in identity authentication. However, existing schemes seldom discuss hierarchical CA architecture, and often suffer from limited scalability and high overhead when considering new CAs, frequent certificate authentication and revocation status verification. To address above issues, we propose MHCertChain, a multi-CA hierarchical certificate blockchain with low overhead. Specifically, a two-layer blockchain structure including the main chain and sub-chains is designed, while the main chain stores trust paths between CAs and each automatically deployed sub-chain records user certificates issued by an end-entity CA. Then, we propose a lightweight dual-signature certificate format that contains certificate location information without requiring any external certificate location method outside the blockchain. Considering time characteristics of certificates, a time-partitioned cuckoo filter is proposed with a low false positive rate and accelerates revocation status query. Moreover, we deduce an optimal global performance parameter of such filter through mathematical modeling. We present a thorough security analysis of our MHCertChain utilizing the universally composable framework, and extensive experiments demonstrate that the CPU overhead and false positive rate are reduced by 70% and 95%, respectively, compared to state-of-the-art schemes. Blockchain-based certificate management schemes provide a distributed means to increase the transparency of PKI (Public Key Infrastructure) and prevent single point attacks in web communications. However, certificate management based on hierarchical multi-CA architecture often faces the problems of poor scalability and high CPU overhead in blockchain. In this article, we are the first to propose a multi-CA hierarchical certificate blockchain with low overhead. Specifically, a two-layer blockchain structure of the main chain and sub-chain is adopted, with the main chain storing the trust paths and the sub-chain storing the user certificates. By monitoring to the CA transactions of the main chain, the sub-chain is automatically deployed. Then, in the certificate operation, we consider the high CPU overhead of traditional certificate query in blockchain and propose a dual-signature certificate format. combined with the time characteristics of the certificate, a time-partitioned cuckoo filter is proposed with a low false positive rate for the revocation status query speeding, and we find a global performance optimal parameter through mathematical modeling. Finally, we use a general composable framework to prove the security of HiCertChain, and the experiments show that the CPU overhead and false positive rate are reduced by 90% and 95%, respectively, compared with those of state-of-the-arts.

In this paper, we propose a task-oriented semantic communication system based on the cloud-network-end (C-N-E) architecture to improve the energy efficiency of image transmission. Within the system, a cloud server provides storage and computation resources for image data collected by multiple cameras. The semantic information of an image is modeled as a scene graph, enabling the analysis of end-user interests. To reduce communication overhead, only useful semantic information relevant to user interests is transmitted. Considering the delay constraint, we formulate an optimization problem to minimize the total energy consumption by jointly selecting semantic information and allocating computation and communication resources. To solve this problem efficiently, an iterative algorithm based on optimal matching and sequential convex approximation is developed. Comparative simulations validate the efficacy of our algorithm.

Honeypots are commonly used tools in network security protection. However, low-interaction honeypots cannot obtain in-depth attack information, while the deployment of high-interaction honeypots is costly. This paper presents LLM-Pot, a novel high-interaction honeypot architecture powered by the Large Language Model (LLM), which explores the direction of intelligent honeypots and addresses the limitations of conventional honeypot solutions. LLM-Pot utilizes LLM to generate dynamic, context-aware responses that accurately simulate the behaviors of real operating systems. To demonstrate the effectiveness of LLM-Pot, this work used offline and online evaluations. The offline evaluation compared LLM-Pot and Cowrie by analyzing their responses to selected commands, and the results demonstrated LLM-Pot’s superior ability in handling complex operations. Online evaluation deployed honeypots in the cloud and captured extensive attack data over two weeks. The evaluation results demonstrate that our LLM-driven approach outperforms traditional honeypots across multiple key metrics, validating LLM-Pot’s superior deception capabilities.

In this paper, we propose BiTDB, a built-in Trusted Execution Environment (TEE) database for embedded systems, to realize higher system availability while ensuring data confidentiality. With BiTDB, dilemmas that the state-of-the-art research work on secure embedded databases has to face can be significantly reduced and eliminated, including (i) complicated research and realization on searchable encryption algorithms (SEA), (ii) limited support to all database operations, and (iii) almost none of specific design and optimizations toward built-in TEE embedded databases. Through BiTDB, all database operations can process plaintext in TEE instead of retrieving ciphertext by developing complicated SEAs. To enable BiTDB to handle database files in Rich Execution Environment (REE) as local ones, we extend the TEE OS with generic file I/O libraries. Then, we contribute three critical optimizations to significantly reduce redundant memory and file operations between TEE and REE, and BiTDB achieve better system performance and availability in embedded systems. Finally, we have implemented the prototype system based on OP-TEE and SQLite for several typical platforms, including virtualization and hardware environments. The TPC-H test shows BiTDB can achieve 85% (on average) of the original database performance while guaranteeing data confidentiality and integrity.

Unmanned Aerial Vehicle (UAV) flight control systems are increasingly exposed to software-level security threats. However, existing Trusted Execution Environment (TEE) technologies that can effectively defend against software attacks are difficult to deploy on the low-cost UAVs due to: (1) the lack of onboard security hardware, (2) limited Memory Protection Unit (MPU) resources, and (3) strict real-time requirements. To address these issues, we design FC-TEE, a lightweight TEE framework tailored for low-cost UAV platforms, which integrates fine-grained memory isolation and a multi-level task scheduling strategy. By analyzing the flight control code and control principles, we provide three key attributes (task priority, maximum invocation frequency, and required argument types) of flight control tasks and classify these tasks into two categories (privileged tasks and common tasks) based on the attributes. Then, we run the privileged tasks in FC-TEE to achieve memory isolation from common tasks, protecting the privileged tasks from software attacks. Meanwhile, we design multi-level task scheduling based on the task priority and categories to ensure the real-time requirements of the flight control system. Compared with existing UAV protection solutions such as MINION and TrustZone-based RT-TEE, the average additional execution overhead introduced by FC-TEE only is 2.7%. We prototype FC-TEE on a real quadrotor platform and validate its effectiveness through task-level performance and security evaluations.

With the rapid development of mobile apps, developers tend to implement a variety of functionalities to support users’ demands. Thus, they involve the usage of native libraries to fulfill the luxuriant functionalities and maintain fast system responses, instead of using a unitary programming language (i.e., Java). Nonetheless, such an inter-language programming framework also introduces more security issues because attackers can conceal malicious behaviors at the native level to evade Android security vetting. Existing state-of-the-art detection tools mainly rely on the information extracted in the Java code to infer the potential malicious behaviors implemented in native code. None of them could simultaneously study the correlated behaviors in Java and native code. Therefore, in this paper, we proposed a static semantic-driven malware detection tool,GNNDroid, to distinguish malware by combining the behaviors implemented in both Java and native code. First,GNNDroidseparately analyzes Java and native code to construct Java function call graphs and native function call graphs. It then utilizes a regex-based function recognition approach to explore the correlations between Java code and native code. According to the code correlations,GNNDroidconstructs Multi-Relational Directed Graphs (MRDGs) to extract the comprehensive behaviors. Finally, it executes a Gated Graph Neural Network (GGNN) to analyze the MRDGs and distinguish malicious apps. We assessedGNNDroidby analyzing 40,000 Android apps and compared them with state-of-the-art tools. The result demonstrated thatGNNDroidnot only performs well when analyzing Java+native apps (i.e., apps implemented by both Java and native code), achieving an F1 of 98.57% but also effectively exploits Java only apps (i.e., apps implemented by Java code), achieving an F1 of 96.31%.

This paper investigates the enhancement of secrecy energy efficiency (SEE) in a THz MIMO two-way relay system utilizing simultaneous wireless information and power transfer (SWIPT) and precoding techniques. We first model the SEE metric of the system under random SWIPT and precoding settings, and subsequently formulate the SEE maximization problem as a non-convex programming problem. By employing techniques such as Dinkelbach transformation, alternating optimization, and difference of convex programming, we then develop a multi-level theoretical framework to jointly optimize the energy splitting factor and precoding matrices. As a special case, we further demonstrate that the proposed theoretical framework can also be applied to solving the secrecy rate maximization problem. Finally, with the help of orthogonal-triangular decomposition and the Newton-Raphson method, we provide the optimal structure of the relay precoding matrix and a low-complexity suboptimal solution scheme for SEE maximization. Extensive numerical results validate our theoretical findings and demonstrate that the proposed schemes can significantly improve the system’s SEE compared to the conventional microwave-based schemes and the random baseline.

Outsourcing neural network (NN) inference services to the cloud gives rise to considerable privacy concerns about the model provider’s proprietary model and the user’s private data. Current cryptography-based secure NN inference schemes are not suited for high-latency networks due to their numerous communication overhead for computing the non-linear components of neural networks. In this paper, we present ParaNN, a secure cloud-based outsourced computation framework that supports lightweight secure neural network inference. At the core of ParaNN, we design a secure and parallel method for extracting the most significant bit (MSB) based on a parallel prefix adder. This forms the cornerstone for a series of secure and communication-efficient computation protocols specifically tailored to non-linear layers like ReLU and Maxpool. Our experiments show that ParaNN achieves a 6.7×-27.4× improvement in online inference time over wide area networks (WAN) compared to the state-of-the-art works.

As the core subject of IoT applications, IoT devices have faced numerous security challenges. Especially for IoT devices deployed in remote or harsh environments, they are often unattended for long periods, making it difficult to share the sensing data and susceptible to potential physical attacks. While aerial assistance methods represented by unmanned aerial vehicles (UAVs) can solve the problem of data sharing at a low cost, it is necessary to establish a secure channel between ground control stations, UAVs, and IoT devices due to the sensitivity of the sensing data. Recently, Physical Unclonable Function (PUF) has been proven to provide unique identity identification for devices using its tamper-proof feature. In this paper, we propose a lightweight UAV-assisted authentication and key agreement protocol for unattended IoT devices, ensuring secure communication and physical tamper-proof requirements. However, our work does not stop there. We noticed that some existing PUF-based authentication schemes misunderstand the ability of PUF, which leads to these schemes cannot actually provide physical protection. We analyzed the security vulnerabilities of these schemes and proposed rules that should be followed when designing authentication protocols using PUF. In addition, for the first time, we put forward the formal definitions and proof methods for PUF in the formal proof of the security protocol, which avoided the unreasonable initial assumptions adopted in the proof of the existing schemes. We extended Mao-Boyd (MB) logic and comprehensively analyzed the proposed protocol. We also evaluate the performance of the proposed scheme, and the results show that the proposed scheme has certain advantages in communication and computation overhead compared with existing schemes.

In this paper, we propose BiTDB, a built-in Trusted Execution Environment (TEE) database for embedded systems, to realize higher system availability while ensuring data confidentiality. With BiTDB, dilemmas that the state-of-the-art research work on secure embedded databases has to face can be significantly reduced and eliminated, including (i) complicated research and realization on searchable encryption algorithms (SEA), (ii) limited support to all database operations, and (iii) almost none of specific design and optimizations toward build-in TEE embedded databases. Through BiTDB, all database operations can process plaintext in TEE instead of retrieving ciphertext by developing complicated SEAs. To enable BiTDB to handle database files in Rich Execution Environment (REE) as local ones, we extend the TEE OS with generic file I/O libraries. Then, we contribute three critical optimizations to significantly reduce redundant memory and file operations between TEE and REE, and BiTDB achieve better system performance and availability in embedded systems. Finally, we have implemented the prototype system based on OP-TEE and SQLite for several typical platforms, including virtualization and hardware environments. The TPC-H test shows BiTDB can achieve 85% (on average) of the original database performance while guaranteeing data confidentiality and integrity. Our project repository is athttps://github.com/CharlieMCY/BiTDB.

As convolutional neural networks (CNNs) exhibit remarkable performance in various inference tasks, it is increasingly important to enable Internet of Things (IoT) devices to perform CNN-based applications. Many companies provide their carefully trained neural networks as inference services for resource-constrained clients (e.g., IoT devices). However, the use of CNN inference in many IoT applications raises privacy concerns. Cryptographic inference services provide a way to perform neural inference efficiently and, at the same time, preserve both the privacy of the client’s input data and the server’s proprietary model. Unfortunately, the existing solutions incur severe latency costs, stemming mostly from nonlinear activations such as ReLUs, which make them still unsuitable for deployment in real IoT devices. In this article, we propose Manto, a secure inference system of CNNs for IoT. Manto makes the following two specific efforts by combining the insights of machine learning and cryptography. First, we customize different quadratic activation functions to replace specific ReLU layers and further propose a sliding-window-based fine-tuning method to produce CNN models involving no or few ReLUs. These techniques allow us to speedup cryptographic inference and guarantee inference accuracy. Second, we develop a series of cryptographic protocols that support ReLU activations and its approximation variants (i.e., polynomial activations), which purely rely on the lightweight secret sharing techniques in the online execution and can well cope with the above-mentioned optimized CNN models in the ciphertext domain. Our experimental results show Manto obtains state-of-the-art performance, reducing online inference latency by$66.2\%\sim 87.7\%$over prior works on CIFAR-100 and TinyImageNet data sets.

The impact of fog-assisted healthcare Internet of Things (H-IoT) system is immense. The smart H-IoT equipments can upload healthcare information to fog nodes with low latency and high mobility. To facilitate secure interactions among three parties, including smart H-IoT equipments, fog nodes, and a cloud server, over the public and insecure channels, a few authenticated key agreement (AKA) protocols are proposed. However, existing works are constructed based on expensive cryptographic primitives (e.g., bilinear pairing), which lead to high computation costs. Besides, the anonymity of H-IoT users is failed to be provided. To tackle these issues, an anonymous and lightweight three-party AKA protocol (ALAKAP) is proposed, which leverages an efficient cryptographic primitive (i.e., Chebyshev chaotic map operation) to generate a shared session key among three parties and achieve security (anonymity and other six properties) and efficiency simultaneously. It then formally proves the security of ALAKAP under the broadly accepted Burrows–Abadi–Needham (BAN) logic model and demonstrates how the proposed protocol satisfies the desired requirements in the fog-assisted H-IoT system. Finally, the performance of ALAKAP is validated by conducting the experiments on Amazon EC2 and Raspberry Pi. The results show that our work can achieve at least 44% higher improvement than the state-of-the-art works.

The advances in deep neural networks (DNNs) have driven many companies to offer their carefully-trained DNNs as inference services for clients’ private data. The privacy concerns have increasingly motivated the need for private inference (PI), where DNN inferences are performed directly on encrypted data without revealing the client's private inputs to the server or revealing the server's proprietary DNN weights to the client. However, existing cryptographic protocols for PI suffer from impractically high latency, stemming mostly from non-linear operators like ReLU activations. In this paper, we propose PAPI, a Practical and Adaptive Private Inference framework. First, we develop an accuracy-adaptive neural architecture search (NAS) approach to generate DNN models tailored for high-efficiency ciphertext computation. Specifically, our NAS automatically generates the DNNs with fewer ReLUs while keeping the accuracy above a user-defined target. Second, we propose secure online/offline protocols for ReLU activation and its approximation variants (i.e., polynomial activations), which purely rely on the lightweight secret sharing techniques in the online execution and can well cope with our optimized DNNs in the ciphertext domain. Experimental results show that PAPI reduces online inference latency on the CIFAR-10/100 and ImageNet datasets by 2.7${\times}$$\sim$7.8${\times}$over the state-of-the-art.

Offloading task to roadside units (RSUs) provides a promising solution for enhancing the real-time data processing capacity and reducing energy consumption of vehicles in the vehicular ad-hoc network (VANET). Recently, multi-agent deep reinforcement learning (MADRL)-based offloading approaches have been widely used for task offloading in VANET. However, existing MADRL-based approaches suffer from offloading preference inference (OPI) attack, which utilizes the vulnerability in the policy learning process of MADRL to mislead vehicles to offload tasks to malicious RSUs. In this paper, we first formulate a joint optimization of offloading action and transmitting power with the objective of minimizing the system cost, including local and edge costs, under the privacy requirement of protecting offloading preference during offloading policy learning process in VANET. Despite the non-convexity and centralized of this joint optimization problem, we propose a privacy-aware MADRL (PA-MADRL) approach to solve it, which can allow the offload decision of each vehicle to reach the Nash Equilibrium (NE) without leaking offloading preference. The key to resisting the OPI attack is to protect the offloading preference by 1)elaborately constructing the noise based on ($\beta,\Phi $)-differential privacy mechanism and 2) adding it to the action selection and policy updating process of vanilla MADRL. We conduct a detailed theoretical analysis of the convergence and privacy guarantee of the proposed PA-MADRL, and extensive simulations are conducted to demonstrate the effectiveness, privacy-protecting capacity, and cost-efficiency of PA-MADRL approach.

Internet of Vehicles (IoV) is a typical application of mobile Internet of Things (IoT), which aims to improve road conditions and enhance the driving experience. However, the issue of identity leakage has drawn a major privacy concern during the vehicle’s authentication. It is impractical to apply traditional anonymous authentication methods directly to the IoV system due to their complex computation. To enhance the user’s privacy and the service’s efficiency, we propose a novel efficient anonymous authentication approach for the IoV based on the zero-knowledge proof (ZKP) and elliptic curve cryptography (ECC). We achieve the user’s strong anonymity and authenticity based on the Fujisaki–Okamoto Commitment algorithm. In addition, the third trusted authority can effectively trace users by tracking users’ verification keys. It holds the user’s traceability when a violation occurs. Meanwhile, we design a fast reconnection procedure based on the security context from the last access, which can reduce the computation overhead effectively. Through the security analysis, it proves that our scheme provides the anonymity, mutual authenticity, unlinkability, traceability, forward security, and replay-attack resistance. The experimental results indicate that our scheme has a better performance compared with the classic protocols in the IoV.

Industrial Internet of Things (IIoT) devices are widely used for monitoring and controlling the process of automated manufacturing. Owing to the limited computing capacity of the IIoT sensors in the production line, the scheduling task in the production line needs to be offloaded to the edge computing server (ECS). To obtain the desired quality of service (QoS) during offloading scheduling tasks, the precise interaction information between the production line and ECSs has to be uploaded to the cloud platform, which poses privacy issues. The existing works mostly assume that all the interaction information, i.e., the offloading decision for the subtask in a scheduling task, has same privacy level, which cannot meet the various privacy requirements of the offloading decision for the subtask. Hence, we propose a local-differential-privacy-based deep reinforcement learning (LDP-DRL) approach in the edge-cloud-assisted IIoT to provide personalized privacy guarantee. The LDP mechanism can generate different levels of noise to satisfy the various privacy requirements of the offloading decision for the subtask. The prioritized experience replay is integrated in DRL to reduce the impact of noise on the QoS performance of task offloading. The formal analysis of LDP-DRL is provided in terms of privacy level and convergence. Finally, extensive experiments are conducted to evaluate the effectiveness, the capacity of privacy protection, the impact of discount factor on the convergence, and the cost efficiency of the LDP-DRL approach.

The high computational capacity demanded in blockchain mining hinders the involvement of mobile devices due to their limited computation power. Offloading the blockchain mining task to base stations (BSs) is a promising solution for mobile blockchain. Recently, many reinforcement learning (RL)-based approaches achieve the long-term performance of Quality of Service (QoS) during online computation offloading, but they fail to consider the risk of privacy leakage. Existing works for privacy preserving share an unresolved problem that they provide a private mechanism by adding private constraints into the value function of RL algorithm but neglect to protect the value function itself. Hence, we investigate a novel privacy issue caused by value function leakage, named offloading preference leakage. To solve this issue, we propose a privacy-aware deep RL method (PA-DRL) for computation offloading over the mobile blockchain. Specifically, a functional noise is generated, then added to the exploring and policy updating processes of DRL. Furthermore, we adopt a cooperative exploring mechanism and prioritized experience replay (PER) to improve the convergence rate of the proposed method. We provide the theoretical analysis for privacy preserving and convergence. Finally, simulation results show that our method can perform cost-efficient computation offloading, compared with benchmark methods.

Information flow security has been considered as a critical requirement on complicated component-based software. The recent efforts on the compositional information flow analyses were limited on the expressiveness of security lattice and the efficiency of compositional enforcement. Extending these approaches to support more general security lattices is usually nontrivial because the compositionality of information flow security properties should be properly treated. In this work, we present a new extension of interface automaton. On this interface structure, we propose two refinement-based security properties, adaptable to any finite security lattice. For each property, we present and prove the security condition that ensures the property to be preserved under composition. Furthermore, we implement the refinement algorithms and the security condition decision procedure. We demonstrate the usability and efficiency of our approach with in-depth case studies. The evaluation results show that our compositional enforcement can effectively reduce the verification cost compared with global verification on composite system.

Information flow security has been considered as a critical requirement on software systems, especially when heterogeneous components from different parties cooperate to achieve end-to-end enforcement on data confidentiality. Enforcing the information flow security properties on complicated systems faces a great challenge because the properties cannot be preserved under composition and most of the current approaches are not scalable enough. To address this problem, there have been several recent efforts on the compositional information flow analyses developed for different abstraction levels. But these approaches have rarely been considered to incorporate with the process of system design. Integrating the security enforcement with the model-based development process can provide the designer with ability to verify information flow security in the early stage of system development. We propose a compositional information flow verification which is integrated with model-based system design in Sys ML by an automated model translation from semi-formal behavior and structure models to interface automata. Our compositional approach is general to support the complex security lattices and a variety of in distinguish ability relations. The evaluation results show the usability of our approach on practical system designs and the scalability of the compositional verification.

In service-oriented mobile social networks (S-MSN), many location-based services are developed to provide various applications to social participants. Services can in turn be composed with the help of these participants. However, the composite structure, the subjective interpretation of trust demand, and the opportunistic connectivity make service composition a challenging task in S-MSN. In this paper, we propose a novel approach to enable trustworthy service evaluation and invocation during the process of composition. By analyzing dependency relationships, our approach can decentralizedly evaluate the trust degree of each service based on a lattice-based trust model to prevent data from being transmitted to untrustworthy counterparts. Besides, service consumers and vendors are able to specify their global and local constraints on the trust degree of service components on demand for more effective composition. Finally, by introducing acquaintances to the neighbors iteratively, social participants form a trust-aware acquaintance graph to forward invocation messages.

Dynamic service composition in wireless environment provides us with a promising approach to build complex applications based on the basic value-added services. In different network domains, multiple services may provide data with different security levels. In order to prevent from information leakage, information flow security is a major concern in composite services. However, the energy-limited nature of user terminal in mobile computing environments poses a significant challenge for the centralized information flow verification where the verification node need cost lots of computation and network resources. In this paper, we specify the security constraints for each service participant to secure the information flow in service chain based on the lattice model, and then present a decentralized information flow verification framework that cooperates different service participants to complete the verification process distributively with respect to their information flow policies. Through the experiments and evaluations, the results show it decreases the verification cost on single service node.

Time constrained service composition raises several problems. Researches on QoS-driven service composition provide some preliminary solutions, but there are still some unsolved issues, which can be attributed to the following reasons: (1) the huge time consumption of inter-domain validation, (2) the dynamic execution time of services and (3) the difficulty in defining time constraint due to the opaque feature of composite services. In this paper, we propose a novel service composition algorithm, which models the service composition as multi-domain scheduling problem with minimal service resources and time constraint. Each service is modeled as an exclusive resource during its execution period. By computing the inter-domain communications and available services in each domain, the domain with optimal utilization rate is obtained to arrange services. Meanwhile, loop parallelization is adopted when a service cannot be executed on schedule. Moreover, redundant services of the initial composition are further optimized. Our experiment results show that our approach can effectively achieve service composition with time constraint.