Blockchain technology, renowned for its decentralized and secure nature, has gained substantial attention. Central to its functionality are consensus mechanisms, which are essential for validating transactions and upholding the integrity of the distributed ledger. However, the efficiency and scalability of blockchain are currently impeded by the resource limitations and excessive communication demands of existing consensus mechanisms. To address these challenges, we propose GT-BFT, a streamlined and lightweight blockchain consensus mechanism grounded in a global trust model. This model capitalizes on node behavior to form consensus groups and facilitate consensus achievement. GT-BFT integrates a novel approach of selective broadcasting along with a Byzantine threshold determination algorithm, significantly boosting both the efficiency and security of the network. Our extensive analysis and performance evaluation reveal that GT-BFT surpasses existing mechanisms in key areas such as security, system throughput, and transaction confirmation speed, marking a significant advancement in blockchain consensus technology.

The applications of unmanned aerial vehicle (UAV) swarms have effectively managed complex tasks, expanded operational areas, and executed missions either autonomously or in collaboration. Group authentication for UAV swarms is essential to ensure the authenticity, integrity, and confidentiality of sensitive data. However, the highly dynamic nature of UAV networks makes the group authentication process challenging. Traditional centralized group authentication methods are susceptible to a single point of failure. In distributed group authentication schemes, issues such as key-escrow attacks, inefficiencies in cross-domain authentication, and challenges in domain isolation within identity data management continue to persist. This paper explores a scalable and efficient authentication scheme assisted by blockchain and group decentralized identifiers (BGAS), which utilizes group decentralized identifiers (GDIDs) to extend decentralized identifiers (DIDs) for group authentication. Security and performance analyses demonstrate that our protocol offers robust security against various common types of attacks and is more efficient than current authentication protocols in terms of storage costs and group authentication time.

As well known, Internet of medical things (IoMT) produces large amounts of medical data and promotes the medical data sharing which serves the data user (i.e., physicians) to boost the clinical treatment and medical research. To protect data user’s privacy and data security during the sharing of medical data, data user must have a self-sovereign decentralized identity (DID) and data access authority. In existing solutions, data user’s privacy protection and authenticated-key-agreement (AKA) for protecting data security are worked independently, which easily results in typical security attacks (e.g., phishing inquiry attacks, ephemeral secret leakage attacks) during data access and system computing overload. To solve the challenge, a new credential-embedded authentication and key agreement scheme (CAKA) is proposed, which can seamlessly combine DID-credentials into AKA. First, CAKA supports bilateral authentication by allowing a digital user to authenticate its service provider, which can enhance the security of unilateral scheme (such as CanDID, IEEE S&P, 2021) and prevent phishing query attacks. Second, for secure data session communication, the user’s DID-credentials are used as the kernel of the session key (SK) generation. In security analysis and performance metrics comparisons, the results indicate that CAKA holds a significant advantage, especially, the storage costs, communication costs and computation costs consumed in CAKA are at least 43% reduction, compared to alternatives. In simulation experiments of CAKA, the results show that decentralized identity authentication and session key agreement are both less than 15 ms, that means CAKA is a practical and promising solution to medical data sharing.

Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the Hidden Markov Model. HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the Hidden Markov Model, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing cross-shard transactions across all shards. We conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its performance through the implementation of a system prototype. The results demonstrate that HMMDShard significantly reduces the proportion of cross-shard transactions and outperforms other baselines in terms of system throughput and transaction confirmation latency.

With the rapid development of Internet of Vehicles and autonomous driving technologies, car manufacturers provide more comfortable and safe driving experience while gradually exposing their vehicles to the background of cyber-attacks. As the car’s interior communicates through the CAN bus, the intrusion detection for CAN bus becomes crucial. Some studies use bus data characteristics, machine learning algorithms, or information theory algorithms to perform intrusion detection on the CAN bus, but they have problems such as low detection accuracy, high performance requirements, and insufficient detection granularity. This paper innovatively proposes a lightweight detection algorithm—Segment Detection Algorithm (SDA), which calculates the bit flip rate by segment, discovers the variation relationship between bits within each segment, and utilizes multiple inter-message features to achieve the detection of abnormal traffic. Experiments show that compared with existing research, the algorithm has effectively improved the detection accuracy, especially the detection of replay attacks. In addition, the algorithm has extremely low time complexity, can adapt to the limited resources in the vehicle environment, and achieve high-precision real-time detection of abnormal traffic.

With the advent of the Internet of Things (IoT), crowdsensing, as a new emerging application of the IoT that employs ubiquitous mobile users with smartphones for data collection and processing, has further deepened our knowledge. However, the problems of the current crowdsensing systems regarding system security, user privacy, and user payment (UP) raise serious privacy and security concerns, which affect participants’ adoption of the system. The Blockchain technology allows for nondeterministic multiple parties to interact with each other anonymously in a network that is not fully trusted. In this article, we propose a new decentralized crowdsensing system, calledCrowdHB. Unlike other blockchain-based crowdsensing systems,CrowdHBadopts a hybrid blockchain architecture and uses smart contracts to achieve location privacy preservation and ensure data quality while improving the system performance. Furthermore, to optimize task assignments to mobile users, we propose a location privacy-preserving optimization mechanism (LPPOM) and the approach of consistency optimization (ACO) to achieve a tradeoff between user privacy and system performance. The extensive experimental results show that the proposedCrowdHBoutperforms the other crowdsensing systems in terms of task success rate and performance for a large number of mobile users and tasks.

Brain imaging genetics develops rapidly, aiming to identify bi-multivariate associations between genetic loci and neuroimaging quantitative traits (QTs). The multi-task Sparse Canonical Correlation Analysis (MTSCCA) is a popular and effective technique in this area since it obtains superior results than those single-task based SCCA methods. Unfortunately, the most existing MTSCCA methods are either unsupervised or incapable of identifying the shared and specific patterns of multimodal neuroimaging QTs simultaneously. In this paper, we propose a novel diagnosis guided MTSCCA to identify the association between genetic and imaging phenotypic markers. Our method has three merits. First, it follows the same modeling paradigm of previous MTSCCA. This enables it to incorporate multimodal imaging QTs jointly, thereby facilitating a more comprehensive identification of genetic factors. Second, our method utilizes the parameter decomposition which could identify both modality-shared and -specific imaging QTs, and further uncovers their genetic mechanisms. Third, we also employed a new network constraint which could find out potentially meaningful brain imaging networks. Compared with conventional SCCA methods including both single-task and multi-task ones, the proposed method has improved or comparable correlation coefficients, and obtains a clean imaging pattern of good meaning. In addition, these results on the Alzheimer’s disease neuroimaging initiative (ADNI) cohort show that our method selects meaningful biomarkers, indicating that it could offer a significant addition to brain imaging genetic studies.

With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.

Mobile users are increasingly becoming targets of malware infections and scams. In order to curb such attacks it is important to know how these attacks originate. We take a previously unexplored step in this direction. Numerous in-app advertisements work at this interface: when the user taps on the advertisement, she is led to a web page which may further redirect until the user reaches the final destination. Even though the original applications may not be malicious, the Web destinations that the user visits could play an important role in propagating attacks. We develop a systematic static analysis methodology to find ad libraries embed in applications and dynamic analysis methodology consisting of three components related to triggering web links, detecting malware and scam campaigns, and determining the provenance of such campaigns reaching the user. Our static analysis system identified 242 different ad libraries and dynamic analysis system was deployed for a two-month period and analyzed over 600,000 applications while triggering a total of about 1.5 million links in applications to the Web. We gain a general understanding of attacks through the app-web interface and make several interesting findings including a rogue antivirus scam, free iPad scams, and advertisements propagating SMS trojans.

Combining tiny sensors and wireless communication technology, wireless body area network (WBAN) is one of the most promising fields. Wearable and implantable sensors are utilized for collecting the physiological data to achieve continuously monitoring of people’s physical conditions. However, due to the openness of wireless environment and the significance and privacy of people’s physiological data, WBAN is vulnerable to various attacks; thus, strict security mechanisms are required to enable a secure WBAN. In this article, we mainly focus on a survey on the security issues in WBAN, including securing internal communication in WBAN and securing communication between WBAN and external users. For each part, we discuss and identify the security goals to be achieved. Meanwhile, relevant security solutions in existing research on WBAN are presented and their applicability is analyzed.

Device-to-device (D2D) communication has emerged as a promising technique for improving capacity and reducing power consumption in wireless networks. Most existing works on D2D communications either targeted CDMA-based single-channel networks or aimed at maximizing network throughput. In this paper, we, however, aim to enable green D2D communications in OFDMA-based wireless networks. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize total power consumption while meeting user data rate requirements. We propose solving it using a joint optimization approach by presenting two effective and efficient algorithms, which both jointly determines mode selection, channel allocation and power assignment. It has been shown by extensive simulation results that the proposed algorithms can achieve over 68% power savings, compared to several baseline methods.

Abstract In this paper, we develop a delay‐centric parallel multi‐path routing protocol for multi‐hop cognitive radio ad hoc networks. First, we analyze the end‐to‐end delay of multi‐path routing based on queueing theory and present a new dynamic traffic assignment scheme for multi‐path routing with the objective of minimizing end‐to‐end delay, considering both spectrum availability and link data rate. The problem is formulated as a convex problem and solved by a gradient‐based search method to obtain optimal traffic assignments. Furthermore, a heuristic decentralized traffic assignment scheme for multi‐path routing is presented. Then, based on the delay analysis and the 3D conflict graph that captures spectrum opportunity and interference among paths, we present a route discovery and selection scheme. Via extensive NS2‐based simulation, we show that the proposed protocol outperforms the benchmark protocols significantly and achieves the shortest end‐to‐end delay. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract With the rapid growth of Android applications and malware, it has become a challenge to distinguish malware from a huge number of applications. The use of behavioral analytics is one of the most promising approaches because of its accuracy and resilience to malware variants. In this paper, we propose a behavior‐based malware detection system. Firstly, it uses Android APIs and libc (Bionic libc) function calls along with their arguments to describe sensitive application behaviors. Secondly, it conducts behavior analysis and malware detection using machine learning techniques, including Support Vector Machine, Naïve Bayes, and Decision Tree. The experiments are conducted with 1136 real‐world samples that are composed of various types of malware and benign applications. The evaluation results show that our system can effectively detect Android malware. In addition, we compare our system with the other behavior‐based malware detection system, and the comparison results show the advantage of our system on malware detection. Copyright © 2014 John Wiley & Sons, Ltd.

Device-to-Device (D2D) communication has emerged as a promising technique for improving capacity and reducing power consumption in wireless networks. Most existing works on D2D communications either targeted CDMA-based single-channel networks or aimed to maximize network throughput. In this paper, we, however, aim at enabling green D2D communications in OFDMA-based wireless networks. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize power consumption while meeting user data rate requirements. We then present an effective algorithm to solve it in polynomial time, which jointly determines mode selection, channel allocation and power assignment. It has been shown by extensive simulation results that the proposed algorithm can achieve over 57% power savings, compared to several baseline methods.

Saving power on base stations (BS) becomes a critical issue in wireless cellular networks. Many existing work has proposed to schedule BS into sleep to save energy. However, in reality, it is very difficult to shut down and reboot BSs frequently due to numerous technical issues and performance requirements. In this work, we propose a much more practical solution and offer a new perspective on implementing Green Wireless Networking by embracing the hot-trended small cell network idea. Instead of putting BSs into sleep, we tactically reduce the coverage (and the power usage) of each BS, and strategically place microcells (relay stations) to offload the traffic transmitted to/from BSs in order to save total power consumption. We propose approximation algorithms for various network design scenarios, with different wireless network setups and different power saving optimization objectives. Extensive numerical results are presented to confirm our theoretical analysis.

SUMMARY Trust model plays an important role in ensuring the security of interactions in peer‐to‐peer (P2P) systems where a peer's trust evaluation depends on the interaction experience of its own and recommendation information from other peers. However, current trust models have limitations in solving not only the issues of time efficiency of direct interaction information but also the reliability and inconsistency of recommendation information. In this paper, we propose a Dempster‐Shafer evidence theory based trust model (ETTM) for P2P systems. The primary goal of ETTM is to be able to address information uncertainty and conflicting recommendation problems in a reputation‐based P2P environment. To make D‐S theory fits into P2P applications, we creatively revise the combination rules and achieve greatly improved results. To further improve the accuracy and performance, ETTM filters out noisy referrals if they are not compatible with most other evidence. In addition, a feedback‐based probabilistic searching algorithm is proposed to find the referrals with improved searching success rate and lowered network traffic. Experimental results show ETTM has a clear advantage in aggregating recommendation information. Moreover, ETTM is more robust and can generate a higher successful transaction rate than some other existing frameworks. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile applications (or apps) are rapidly growing in number and variety. These apps provide useful features, but also bring certain privacy and security risks. For example, malicious authors may attach destructive payloads to legitimate apps to create so-called "piggybacked" apps and advertise them in various app markets to infect unsuspecting users. To detect them, existing approaches typically employ pair-wise comparison, which unfortunately has limited scalability. In this paper, we present a fast and scalable approach to detect these apps in existing Android markets. Based on the fact that the attached payload is not an integral part of a given app's primary functionality, we propose a module decoupling technique to partition an app's code into primary and non-primary modules. Also, noticing that piggybacked apps share the same primary modules as the original apps, we develop a feature fingerprint technique to extract various semantic features (from primary modules) and convert them into feature vectors. We then construct a metric space and propose a linearithmic search algorithm (with O(n log n) time complexity) to efficiently and scalably detect piggybacked apps. We have implemented a prototype and used it to study 84,767 apps collected from various Android markets in 2011. Our results show that the processing of these apps takes less than nine hours on a single machine. In addition, among these markets, piggybacked apps range from 0.97% to 2.7% (the official Android Market has 1%). Further investigation shows that they are mainly used to steal ad revenue from the original developers and implant malicious payloads (e.g., for remote bot control). These results demonstrate the effectiveness and scalability of our approach.

End-to-end delay minimization is one of key challenges in multi-hop Cognitive Radio Ad Hoc Networks (CRAHNs), where the opportunistic transmission impacts on each hop of routing paths. However, the problem is only considered in routes establishment while traffic assignment is also an important process of packet delivery in highly dynamic transmission environment. For this reason, a novel queue theory based optimal traffic assignment algorithm for multi-path routing in CRAHNs is proposed to minimize the overall end-to-end delay in this paper, which dynamically assigns the traffic load on multiple routing paths considering the spectrum availability and service rate of each hop. The algorithm is performed in using a gradient-based search method to find the optimal traffic assignment strategy. The simulation results demonstrate that the proposed algorithm significantly outperforms other baseline schemes in end-to-end delay in CRAHNs.

Smartphone sales have recently experienced explosive growth. Their popularity also encourages malware authors to penetrate various mobile marketplaces with malicious applications (or apps). These malicious apps hide in the sheer number of other normal apps, which makes their detection challenging. Existing mobile anti-virus software are inadequate in their reactive nature by relying on known malware samples for signature extraction. In this paper, we propose a proactive scheme to spot zero-day Android malware. Without relying on malware samples and their signatures, our scheme is motivated to assess potential security risks posed by these untrusted apps. Specifically, we have developed an automated system called RiskRanker to scalably analyze whether a particular app exhibits dangerous behavior (e.g., launching a root exploit or sending background SMS messages). The output is then used to produce a prioritized list of reduced apps that merit further investigation. When applied to examine 118,318 total apps collected from various Android markets over September and October 2011, our system takes less than four days to process all of them and effectively reports 3281 risky apps. Among these reported apps, we successfully uncovered 718 malware samples (in 29 families) and 322 of them are zero-day (in 11 families). These results demonstrate the efficacy and scalability of RiskRanker to police Android markets of all stripes.

We present a novel approach to indoor wireless localization using label propagation based on semi-supervised learning. Our aim is to reduce the effort of collecting labeled data in the offline training phrase, which are expensive to obtain. This learning algorithm combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar data should have similar labels, which has intimate connections with random walks to propagate label through the dataset along high density areas defined by unlabeled data. We test our algorithm in 802.11 wireless LAN environments, and demonstrate the advantage of our approach in both accuracy and its ability to utilize a much smaller set of labeled training data.

Managing trust is a key issue for a wide acceptance of P2P computing, particularly in critical areas such as e-commerce. Reputation based trust management has been identified in the literature as a viable solution to the problem. However, the mechanism faces the challenges of subjectively, experiential weighting referrals when aggregating recommendation information. Furthermore, not considering some malicious attacks when building trust relationship between peers in the existing schemes make trust model very vulnerable. This paper presents P2PTrust-a new trust framework based on reputation for unstructured P2P networks. In P2PTrust, except reputation value, reputation revision value is also taken into account in order to deal with the dynamic or spoiling behavior of peers, which makes P2PTrust differ from other trust models based on the reputation only. Considering the credibility of peers' referrals, a credibility quantification and update scheme is proposed in the paper as reliable means of seeking honest feedbacks. Subsequent experimental results show that, compared to the existing trust models, our scheme is robust in systems where the vast majority of users are malicious and more advanced in successful transaction rate.

Video streaming over wireless networks is becoming increasingly important. In this paper, we study the problem of multiple real-time video surveillance streaming over time-varying, error-prone wireless mesh networks (WMNs). The video streams are transmitted on the Internet through Internet Gateway (IGW). For better transmission, we propose a video streaming design by multiple flows gathering with opportunistic relay (OR) and network coding (NC): FORNEC. With meticulous design, FORNEC avoids multiple video streams aggregating around the most congested region of IGW. Further, with NC and OR, FORNEC provides robust packets forwarding with minimum loss rate to overall video streams.

In IEEE 802.16e, the energy saving mechanism is an important problem. Excessive listening operations in sleep-mode will waste a lot of energy and shorten the lifetime of a Mobile Subscriber Station (MSS). In this paper, we propose an enhanced energy saving mechanism to overcome this problem. An embedded Markov chain model is adopted to analyze the enhanced energy saving mechanism analytically. Meanwhile, a closed-form expression of the average energy consumption in the sleep-mode for the suggested mechanism is presented. We evaluate and validate the suggested scheme via analytical results and simulation results. Simulation results illustrate that the proposed mechanism can obtain better effects of energy conservation to minimize MSS power usage and to extend the lifetime of MSS effectively while not compromise the performance of Service Data Unit (SDU) response time.

A modeling approach is proposed in this paper to build the bipartite fault propagation model (FPM) for Internet services. The FPM is layered as Internet services involve multiple layers. Two fault localization algorithms, MCA (Max-Covering Algorithm) and MCA+, are designed for the bipartite FPM. MCA+ is an extension of MCA, taking lost and spurious symptoms into account. Simulation results show that MCA+ achieves high detection rate, low false positive rate and has polynomial computational complexity even in the presence of lost and spurious symptoms.

The problem of topology control in multi-rate multihop ad hoc networks is studied and a multi-rate aware topology control algorithm (MATC) is proposed in the paper. MATC fully takes the inherent multi-rate property of wireless networks into account. It removes some low rate links while preserving the network connectivity. On the basis of MATC, on-demand routing protocols can find the optimal route in the one-hop range. Extensive simulations show that MATC can significantly improve the network throughput.

Packet one-way delay (OWD) traces are important measurements for analyzing end-to-end performance in the Internet. Due to the fact that the clocks at the end systems are usually not synchronized, the OWD measurements can be quite inaccurate. For the more challenging case with clock resets, i.e., the clocks are reset to some reference times during the measurement period, the OWD measurements can be even worse. We propose a new algorithm to estimate and remove the relative clock skews and clock resets from measurements, based on fuzzy-clustering analysis. Numerical experiments shows that, compared with existing techniques such as linear programming and the convex-hull approach, the new algorithm is more accurate and robust.

Several handover schemes, such as smooth handover and fast handover, have been proposed to realize seamless handovers in mobile environment. However in our simulation, even if smooth handover in mobile IPv6 are implemented, TCP can not always be ensured to achieve better performance due to packets forwarding burst. Based on the performance analysis of TCP with the support of smooth handover, this paper proposes an enhanced buffer forwarding control scheme in smooth handover to improve TCP performance. In our scheme, a packet-pair probing technology is adopted to estimate the bandwidth of the new path from previous router (Prtr) to mobile node (MN), which is used by Prtr to control the buffered packets forwarding. Finally, simulation results demonstrate that smooth handover with our scheme can achieve better TCP performance than that without it.