Vehicle platoon (VP), as a typical form of traffic cooperation, can significantly enhance traffic efficiency and safety in Vehicular Ad hoc Networks (VANETs). However, malicious vehicles in VP poses a severe threat to the security of entire VP, requiring to be efficiently traced by identity authentication. In this paper, we propose a lattice-based efficient and traceable privacy-preserving batch authentication scheme for vehicle platoon in VANETs, named LETA. First, we design a dynamic VP identity structure VPD-Tree which is constructed based on hash tree and pseudonyms of vehicles to preserve privacy. Then, an aggregate signature is constructed based on VPD-tree and modular lattice for secure and efficient batch authentication of VP. Finally, Zero-Knowledge Proofs (ZKP) is applied on the VPD-Tree structure to anonymously and efficiently trace the malicious vehicles of VP. Security analysis shows that LETA achieves stronger security guarantees, thereby offering a more secure solution than existing approaches. Moreover, performance evaluations show that LETA achieves lower computation and communication overheads through the VPD-tree structure and efficient batch authentication scheme.

The development of wireless industrial internet of things (IIoT) has facilitated the widespread deployment of dynamic digital twins across multiple factories and regions. However, the wireless communication between digital twins and their physical devices takes place over public channels, and the dynamic behavior of digital twins makes them susceptible to forgery and impersonation. As a result, data exchanged between digital twins is exposed to risks of tampering, fabrication and corruption. Moreover, real-time data sharing among a large number of digital twins incurs significant computational and communication overheads, thereby necessitating a secure and efficient cross-domain authentication scheme to ensure trustworthy identities and data integrity. To address these challenges, we propose ECroA, an efficient cross-domain authentication in dynamic digital twins of wireless IIoT. Specifically, we employ a distributed key management mechanism to establish trust across domains while reducing communication overhead. Additionally, we design an identity-based signature and pseudonym-based authentication scheme to provide conditional privacy preservation, ensuring secure data exchange without exposing sensitive data. To further enhance efficiency, we integrate signature aggregation and batch verification techniques, significantly reducing computational costs for large-scale cross-domain authentication. Performance analysis demonstrates that ECroA effectively enhances authentication efficiency while ensuring secure cross-domain authentication in dynamic digital twins of wireless IIoT.

In vehicular ad-hoc networks (VANETs), certificate revocation is a critical technique to synchronize the invalid vehicle information accurately and timely across the network, especially in scenarios involving multiple edge domains. In order to achieve efficient and privacy-preserving certificate revocation list (CRL) synchronization, we propose ESGA, an efficient and secure geo-aware certificate revocation scheme for blockchain-based multi-domain VANETs. Since invalid vehicles are driving within specific edge domains during certain time periods, we design a hierarchical Geo-CRL structure comprising a blockchain-based global CRL and geographically distributed local CRLs. Furthermore, we design an adaptive geo-aware synchronization mechanism which enables timely local CRLs update, while ensuring periodic or event-driven updates of the global CRL on the blockchain. Finally, to protect the CRL information from pseudonym linking attacks, we design linkage values chain-based certificate revocation method for privacy preservation. The experimental results demonstrate that ESGA can significantly improve CRL synchronization efficiency with lower CRL query latency and storage overhead, and to protect the privacy of certificate revocation in blockchain-based multi-domain VANETs.

The Industrial Internet of Things (IIoT) represents a sophisticated technology designed to enhance production management and predict output in industrial settings, including machinery fault diagnostics. The precision of fault diagnosis is contingent upon the training efficacy of diagnostic models and their interoperability with models from other industrial facilities. Nonetheless, several critical challenges persist in maintaining these diagnostic models: 1) machinery sensors may generate abnormal data, resulting in suboptimal quality in model training; 2) sensor malfunctions may lead to interruptions in continuous data flow, thus impeding model training; and 3) collaborative interactions with other factories aiming at improving model performance may pose risks of privacy breaches. In this study, we introduce the FCLLM-DT scheme, which integrates the digital twin (DT) methodology to create a physical model of bearing for fixing abnormal sensor data. Additionally, retrieval-augmented generation (RAG)-assisted large language models (LLMs) are utilized to generate virtual datasets in instances of sensor failure. Moreover, for IIoT applications across distributed industrial environments, federated continual learning (FCL) is employed to enhance global model training by aggregating localized models from diverse facilities, thereby improving the accuracy of bearing fault diagnosis while safeguarding data privacy. The experiments on the accuracy of DT for abnormal data fix, RAG-assisted LLM for virtual data generation, and FCL for bearing fault diagnosis are conducted in comparison with three alternative methods across two datasets. The results indicate that our proposed scheme surpasses existing methods in both the enhancement of sensing data quality and the accuracy of bearing fault diagnosis.

Spatial crowdsourcing is widely used in various applications in vehicular ad hoc networks (VANETs), such as navigation system, traffic control, and event reporting. However, exposing and disclosing the spatial-temporal features of vehicles in the crowdsourcing services will definitely raise serious privacy issues. Existing unified privacy-preserving strategy for vehicles will cause excessive or insufficient preservation, thus result in relatively low-quality services. To solve these problems, we propose personalized privacy preserving for spatial crowdsourcing by reinforcement learning in VANETs. First, we propose a multifactor-based personalized privacy-preserving model to adjust the vehicles’ privacy-preserving level in the scenario of spatial crowdsourcing. And, we employ reinforcement learning to dynamically adjust the model in different situations. Furthermore, we propose an optimal local differential privacy mechanism to maintain the optimal tradeoff between data privacy and data utility, which can achieve personalized privacy preserving in the task allocation. We conduct extensive simulations with the real-world traffic trajectory dataset T-drive, and use the Q-learning algorithm to dynamically adjust the model. The experiments demonstrate that our scheme can enhance data utility by 78.5% with personalized privacy settings.

With the rapid expansion of vehicular ad-hoc networks (VANETs), the dynamic traffic environment raises concerns regarding accurate and secure data sharing. Unauthorized entities may exploit vulnerabilities to access sensitive information within shared data. To address these challenges, we propose DRL-APG, a deep reinforcement learning based adaptive policy generation scheme, to enable smarter security policies that can better handle complex changes in data sharing among vehicles. DRL-APG adopts hybrid modeling to capture environment states and fine-grained policy optimization using an ARIMA (Autoregressive Integrated Moving Average)-based reward mechanism to generate adaptive policies. Extensive simulations demonstrate DRL-APG outperforms existing schemes in different kinds of VANET situations including peak/off-peak hours and varying speed limits. In traffic congestion scenario across three simulation typical road networks, average accident zone speed increases 21.7%, 25.6% and 27.8% respectively after data sharing with the policy generated by our proposed DRL-APG, compared to existing schemes.

The vehicle platooning (VP) going across different regions together, plays an important role in improving traffic efficiency. Then how to perform authentication efficiently for the vehicle platoons becomes the basic requirements in the Internet of Vehicles, especially cross different trust domains. In this paper, we propose an efficient and privacy-preserving cross trust-domain group authentication scheme for VP based on blockchain, named EPP-GAS. We design an extended blockchain model, which encodes the VP authentication parameters into the BM-Tree block, to support the sharing of VP authentication parameters cross multiple trust-domains. Then, we construct group pseudonym based on Shamir’s threshold scheme, to realize anonymous group authentication efficiently. Also, we propose a group authentication scheme with batch identity authentication through dynamic adaption of VP adjustment and efficient re-authentication in the case of authentication failure. Compared with the existing authentication schemes, our scheme has better performance in terms of security, authentication efficiency, communication overhead, and increase efficiency for cross trust-domain group authentication by 31% on average and reduce the communication overhead by 43.9% on average.

Authentication plays an important role in verifying the integrity of messages and the identity of message senders in vehicular ad-hoc networks (VANET) applications. A significant challenge in expansive regions pertains to the efficient authentication of multiple vehicles traversing trust domains. This paper introduces a novel cross-domain batch authentication scheme for VANETs, denominated as CD-BASA, leveraging blockchain technology in conjunction with accumulator. The CD-BASA scheme is built upon blockchain with accumulator and interplanetary file systems for streamlined storage and batch computation. Subsequently, a proficient cross-domain batch authentication protocol for CD-BASA is devised, encompassing the establishment of domain-specific and global parameters, which greatly reduces the identity verification cost for multiple vehicles. To further minimize the overhead associated with batch validity proofing, a novel multi-accumulator batch proof algorithm is designed to greatly reduce the proof cost of multiple vehicle identities. Experimental evaluations and theoretical security analysis show that CD-BASA has better performance than other existing schemes, in terms of cross-domain batch authentication overhead (improving at least 18.2%) and accumulator proof overhead (reducing at least 69.9%).

Ciphertext policy attribute-based encryption (CP-ABE) is popularly used to implement secure and accurate access control of disseminated information in vehicular ad hoc networks (VANETs). Nevertheless, how to improve the policy generation of CP-ABE for accurate information dissemination in the dynamic VANETs remains a challenge, as there are several access control policies rising from moving vehicles and road side units (RSUs) with different sensing boarder regarding to a specific event, such as moving vehicles and road side units (RSUs). To solve this problem, this paper proposes a reinforcement learning-based information dissemination policy generation scheme in VANETs, named RLID-V. The scheme firstly combines multiple attribute-based access control policies and resolves policy conflicts between vehicles and RSUs. Then, a manual feedback policy construction method is designed by applying decision tree to the collected feedback from all receivers. Finally, we employ reinforcement learning to dynamically update the confidence weights of different policy sources. The experiments are conducted in two classic VANETs scenarios, traffic guidance and accident warning, demonstrating that RLID-V achieves better performance in the accuracy and effectiveness of information dissemination compared with three existing schemes. Otherwise, RLID-V outperforms the compared schemes in robustness with 20% error feedback and takes a negligible cost of less than 1% of the overall delay overhead for policy generation.

Securing information dissemination is extremely important for various vehicular ad hoc networks (VANETs) applications. However, most of the applications require disseminating the critical information only to the authorized vehicles through V2I (Vehicle to Infrastructure) communications. Moreover, V2I communications usually suffer from incomplete information to vehicles in one RSU’s transmission range. Therefore, it is an even challenging task that how to ensure reliable dissemination of encrypted data to the recipient vehicles in multi-RSU settings. We propose a security-aware information dissemination scheme with fine-grained access control in cooperative multi-RSU of VANETs. Our proposed scheme uses ciphertext-policy attribute-based encryption (CP-ABE) to ensure confidential communication in a broadcasting way, which ensures that only the vehicles that satisfy the access control policy can have the ability to access the information; and we employ proxy re-encryption in the communication protocol to make sure the moving vehicles in high-speed can get the whole encrypted information. Performance analysis shows that our scheme can enable fine-grained access control for the broadcasted information, and make sure the vehicles receive reliable information in the whole disseminating process. And our scheme is applicable and efficient in various scenarios of information dissemination, especially in cooperative multi-RSU of VANETs.

Recently, vehicular crowdsensing networks have attracted much attention because of their ability to provide efficient and convenient information services for the Internet of Vehicles. How to achieve on-demand message authentication and provide privacy protection of sensing vehicles are challenging in accurate sensing tasks. We propose a secure vehicular crowdsensing scheme based on multi-authority attribute-based signature (TRAMS), which allows the publisher to flexibly customize a fine-grained policy that the potential participants must satisfy and uses attribute-based signature to authenticate sensed messages while protecting the privacy of the sensing vehicle. Also, we propose a multi-authority key management scheme, which can improve vehicle-based sensing efficiency in the Internet of Vehicles. Performance analysis shows that our scheme can not only achieve massage authentication while protecting the privacy of the sensing vehicle, but also ensure fine-grained message authentication to meet the expectation of the publisher on demand. And compared with the single-authority schemes in vehicular communication, our multi-authority TRAMS can achieve efficient message authentication for vehicular crowdsensing applications which require timely task feedback.

The reputation-based scheme is a promising solution to prevent malicious behaviors in Vehicular Ad-hoc Networks (VANETs). However, traditional centralized reputation schemes are not suited for distributed networks, while decentralized reputation schemes are vulnerable to malicious vehicles spreading false messages. Most of these schemes assume that the behavior of vehicles can be accurately measured as reputation from the communication, ignoring that malicious vehicles may behave intelligently to avoid being detected. In this paper, we propose a hybrid reputation system (HDRS) which allows vehicles and roadside units (RSU) to complete reputation evaluations separately and provide references to each other. HDRS utilizes a reliability evaluation module to filter out unreliable calculation results and reference records. Furthermore, HDRS includes a dynamic adjustment mechanism for the reputation update interval, employing Analytic Hierarchy Process (AHP) and reliability evaluation results to resist intelligent attacks. Simulation results illustrate that HDRS can maintain a high detection rate and low false-positive rate for detecting malicious vehicles in different environments. Compared with existing schemes, HDRS increases the detection rates of collusion and intelligent attacks by 30% and 16%, respectively.

Fog-aided computing is an emerging computing paradigm developed for providing various computation services to end users in vehicular fog computing. However, fog vehicles may be untrusted, and results from malicious operations may cause serious accidents. Therefore, we propose a secure and efficient outsourcing computing scheme in vehicular fog computing (SE-VFC), which performs outsourcing computing through fog vehicles with computing resources, and combines lightweight Boneh-Lynn-Shacham (BLS) signature and group signature to achieve batch anonymous authentication of fog vehicles while protecting their privacy in multiple outsourcing tasks. We also verify the correctness of the outsourcing computing results. Security analysis shows that our scheme can authenticate the fog vehicles in the outsourcing computation and preserve their privacy, it can also trace the malicious ones if necessary. Compared with the existing schemes, our scheme has relatively low communication and computation overhead, thus it is quite efficient for batch authentication especially in multiple computing tasks. Extensive simulation results verify the effectiveness and practicality of our proposed scheme in vehicular fog computing.

With the development of smart phones, more and more mobile phone malwares have came out in the market especially on the popular platforms such as Android, which can potentially cause harm to users' information. But how to effectively detect the new malwares and malicious software variants has been a difficult problem. In view of the traditional feature extraction method based on binary program, this paper presents a method for feature extraction of JAVA source code. The method uses the Keywords Correlation Distance to compute the correlation between key codes such as API calls, Android permissions, the common parameters, and the common key words in Android malware source code. Then SVM is applied to make the system gain to accommodate the function of the new malicious software sample, so as to detect new malicious software and existing malwares. This method is different from the conventional methods which are based on the context of the text. This method combines the characteristics of the malicious software categories and operating environment to record the behavior of the malicious software. Experiments show that the method is efficient and effective in detecting malwares on Android platform.

Vehicular ad-hoc networks (VANETs) have drawn much attention of researchers. The vehicles in VANETs frequently join and leave the networks, and therefore restructure the network dynamically and automatically. Forwarded messages in vehicular ad-hoc networks are primarily multimedia data, including structured data, plain text, sound, and video, which require access control with efficient privacy preservation. Ciphertext-policy attribute-based encryption (CP-ABE) is adopted to meet the requirements. However, solutions based on traditional CP-ABE suffer from challenges of the limited computational resources on-board units equipped in the vehicles, especially for the complex policies of encryption and decryption. In this paper, we propose a CP-ABE delegation scheme, which allows road side units (RSUs) to perform most of the computation, for the purpose of improving the decryption efficiency of the vehicles. By using decision tree to jointly optimize multiple factors, such as the distance from RSU, the communication and computational cost, the CP-ABE delegation scheme is adaptively activated based on the estimation of various vehicles decryption overhead. Experimental results thoroughly demonstrate that our scheme is effective and efficient for multimedia data forwarding in vehicular ad-hoc networks with privacy preservation.

ABSTRACT A challenge faced by many system administrators in utilizing the intrusion detection system (IDS) is to sift out genuine alerts buried with overwhelming alerts of benign activities generated by the IDS, especially for IDS deployed in large networks. Existing methods propose to identify the real alerts to aid the administrators. In our paper, we extend the idea of filtering irrelevant alerts based on alert volumes. And we formulate the flow estimation of abrupt changes in feature distribution caused by anomalies, by computing Kullback–Leibler distance of alert feature values under observation in comparison with a reference distribution, which is the mixture of a distribution drawing a tread from historical alerts, and a distribution derived from expertise provided by administrators. Experimental studies on the Defense Advanced Research Projects Agency dataset as well as real‐life data gathered from the IDS of a large network show that our method is able to distinguish and highlight genuine anomalies arising from the tremendous number of intrusion alerts, including different kinds of attacks and network failures. Application of this technique to alerts greatly helps the administrators in identifying real alerts and then reduces the alert load in the future. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract Network diagnosis and attack prediction can help the network administrator to take timely actions to defend against well‐planned attacks that exploit a chain of vulnerabilities. One important data source for such analysis is the alerts generated by intrusion detection systems (IDS) deployed over the network. However, IDS typically generates overwhelming amount of alerts, where one cannot simply aggregate or discard. In addition, the chance of a successful exploit depends on many hidden factors such as system status and attacker power, and thus the dependencies among exploits and conditions are typically too complicated to analyze under probability framework. In this paper, we employ expert system to deal with such uncertainties and conduct certainty factor inference. We show that analysis in fuzzy system is tractable and we propose an algorithm to analyze the network status and predict the potential attacks. Finally, we give a case study to illustrate our algorithm and evaluate the effectiveness of our approach on the DARPA data sets. Copyright © 2011 John Wiley & Sons, Ltd.