Despite the widespread deployment of various Intrusion Detection Systems (IDSs) in industrial Cyber-Physical Systems (CPS), significant challenges such as class imbalance, zero-day attacks, and privacy vulnerabilities persist. These issues underline the critical need for a more robust IDS solution that not only improves detection and generalization capabilities across different scenarios but also ensures stringent data privacy. In this paper, a novel IDS solution tailored for industrial CPS is proposed, designated as a Transformer-enhanced External Classifier-Generative Adversarial Network (TransEC-GAN). This innovative model extends the External Classifier-Generative Adversarial Network (EC-GAN) by integrating Transformer encoders, which leverage Wasserstein distance and label conditioning to enable stable gradient descent within the semi-supervised learning environment. To further fortify the system, Adaptive Differential Privacy (ADP) is incorporated, dynamically adjusting privacy settings to effectively prevent adversaries from exploiting sensitive information. Additionally, the proposed TransEC-GAN features a meticulously designed two-stage detection architecture that proficiently distinguishes between In-Distribution (InD) and Out-Of-Distribution (OOD) samples, enhancing its ability to identify and react to novel and evolving threats. Comprehensive experimental evaluations and theoretical analysis validate that the proposed TransEC-GAN not only safeguards against privacy breaches but also excels in detecting a wide array of attack types in industrial CPS settings.

Intrusion Detection System (IDS) is an effective way to detect both internal and external abnormal behaviors, which has been widely deployed in industrial Cyber-Physical Systems (CPSs). However, due to the data island problem caused by the imperativeness of confidentiality of sensitive information, most existing IDSs are limited to be trained and evaluated in isolated CPSs, resulting in the cyber systems vulnerable to various newly-emerging attacks. In this article, a secure and collaborative IDS solution, called PFD-GAN, is proposed. Specifically, we firstly develop a novel semi-supervised IDS model by improving External Classifier (EC)-Generative Adversarial Network (GAN) with Wasserstein distance and label condition, to strengthen the classification performance through the use of synthetic data. Furthermore, Local Differential Privacy (LDP) is adopted to prevent against adversaries learning sensitive information in collaboration. Moreover, a Decentralized Federated Distillation (DFD)-based collaboration is designed, allowing multiple industrial CPSs to collectively build a comprehensive IDS to recognize the threats under the entire cyber systems without sharing a uniform template model. Experimental evaluation and theory analysis demonstrate that the proposed PFD-GAN is secure from the threats of privacy leaking and highly effective in detecting various types of attacks on industrial CPSs.

Recently, Vehicular Ad Hoc Networks (VANETs) have emerged as a promising approach to improve ride comfortability and safety, as well as increase the competition of highway. Certificate Revocation List (CRL)-based schemes are the most widely used recovery mechanism for VANETs to eliminate the negative effect of security and privacy attacks. However, providing an efficient and low-cost CRL-based scheme for certificate revocation is still a challenging issue since the communication resources must be capable of carrying various applications apart from the security and privacy purposes. Thus, in this paper, a Multilayer Coded Caching CRL scheme, called MCC-CRL, is proposed in VANETs. The MCC-CRL is able to distribute the minimum bits of CRLs that satisfies the revocation requirements, aiming to reduce the communication overhead and shorten the processing time cost. In addition, the efficient authentication mechanism, i.e., EAAP, is employed with our MCC-CRL scheme to realize conditional privacy preservation in a computationally efficient way. Extensive simulations demonstrate that the MCC-CRL scheme is secure and computationally efficient for vehicles’ revocation.

Because of the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face the challenge in balancing the accuracy and efficiency of detection. Two crucial problems are remained unsolved in existing studies: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a self-adaptive scheme is proposed for IDSs in VANETs based on Bayesian game theory and Deep Q-learning Network (DQN). In the scheme, the interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the proposed scheme decides either to just adjust or to completely retrain the IDS. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a DQN-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game. Simulation results show that the IDS with the proposed self-adaptive scheme has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

For Intrusion Detection Systems (IDSs) in Vehicular Ad Hoc Networks (VANETs), single-objective optimization algorithm has inherited limitations for the feature selection problem with the multiple objectives. Moreover, the imbalanced problem commonly exists in various datasets. Thus, in this paper, a feature selection algorithm based on a many-objective optimization algorithm (FS-MOEA) is proposed for IDSs in VANETs, in which Adaptive Non-dominant Sorting GeneticAlgorithm-III(A-NSGA-III) serves as the many-objective optimization algorithm. Two improvements, called Bias and Weighted (B&W) niche-preservation and Information Gain (IG)-Analytic Hierarchy Process (AHP) prioritizing, are further designed in FS-MOEA. The former is used to counterbalance the imbalanced problem in datasets by assigning rare classes higher priorities, while the latter is employed to search the optimal feature subset for FS-MOEA. In IG-AHP prioritizing, a more distinct measurement, i.e. average IG, is used as the dominant factor to guide the decision analysis of AHP. Experimental results show that the proposed FS-MOEA can not only improve the performance of IDSs in VANETs but also alleviate the negative impact of the imbalanced problem.

Due to the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face lots of challenges, especially in balancing the accuracy and efficiency of detection. Current researches about the deployment of IDSs in VANETs mainly focus on a tradeoff between the effectiveness and efficiency, but few efforts have been done about the adaptability of the tradeoff in the changeable networks. Thus, we address two crucial problems: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a Bayesian Game theory and Deep Q-learning Network-based IDS is proposed for VANETs, called GaDQN-IDS. The interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the IDS decides either to just adjust the tradeoff between the accuracy and efficiency or to be retrained completely when its detection capacity has declined. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a Deep Q-learning Network (DQN)-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game, while an Error Priority Learning (EPL) is further designed for IDS retraining in changing VANETs. Simulation results show that the GaDQN-IDS has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

Vehicle Ad Hoc Networks (VANETs) are considered to be a next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Intrusion Detection Systems (IDS) is an important technology that can mitigate both inner and outer threats for the vulnerable networks like VANETs. However, it is difficult to adopt the same IDSs that have been appropriately used in wired networks, due to the fast moving and highly dynamic nature of VANETs. Thus, in this paper, an Efficient IDS with a Markov-based Reputation Scheme is proposed, called EIDS-MRS. In EIDS-MRS, the Non-Linear Programming (NLP)-Optimization is designed as the efficient mechanism to reduce the execution time of IDSs in VANETs. Moreover, considering the security risks of NLP-Optimization, a Reputation Scheme based on the Hidden Generalized Mixture Transition Distribution (HgMTD) model, namely RS-HgMTD, is proposed for each vehicle in VANETs to evaluate the creditworthiness of their neighbors. Experiments show that the EIDS-MRS has better performance than other available IDSs in terms of detection rate, detection time and overhead.

Vehicle Ad Hoc Networks (VANETs) are considered to be a next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Intrusion Detection Systems (IDS) is an important technology that can mitigate both inner and outer threats for the vulnerable networks like VANETs. However, it is difficult to adopt the same IDSs that have been appropriately used in wired networks, due to the fast moving and highly dynamic nature of VANETs. Thus, in this article, an Efficient and Collaborative Framework with a Markov-based Reputation Scheme is proposed, namely ECF-MRS. In the proposed framework, the collaborative mechanism is achieved by using Non-dominant Sorting Genetic Algorithm-III (NSGA-III)-Collaboration to merge the advantages of IDSs in VANETs to generate a more superior IDS, while Non-Linear Programming (NLP)-Optimization is designed as the efficient mechanism to reduce the execution time of IDSs in VANETs. Moreover, considering the security risks of collaboration, a Reputation Scheme based on the Hidden Generalized Mixture Transition Distribution (HgMTD) model, namely RS-HgMTD, is proposed for each vehicle in VANETs to evaluate the creditworthiness of their neighbors. Experiments show that the IDS with ECF-MRS has better performance than other existing IDSs in terms of detection rate, detection time and overhead.

Intrusion Detection System (IDS) is one of the most important approaches in cyber security to protect networks against both inner and outer threats. Apart from traditional networks, IDSs have been implemented in various emerging networks, such as mobile networks and Vehicle Ad hoc Networks (VANETs). However, a critical problem in IDSs is that the detection capacity is gradually decaying with the emergence of unknown attacks. It is necessary to constantly retrain IDSs with a more extensive database, but the security institutes usually lack the motivation to persistently update and maintain the database for public. Thus, in this paper, a lifetime learning framework is proposed for IDSs with a blockchain-based database (bc-DB). In the proposed framework, the blockchain-based database is multilaterally maintained by the security institutes and universities using Data Coins (DCoins) as the incentives. In addition, a Lifetime Learning IDS (LL-IDS) is further designed as the supplement of the bc-DB for common IDS users. For the LL-IDS, the Growing Hierarchical Self-Organizing Map with probabilistic relabeling (GHSOM-pr) having flexible and hierarchical architecture is employed as the classifier, which grows to make itself perfectly fit the changeable bc-DB. Security analysis and simulation experiments show that the proposed lifetime learning framework are both secure and effective in attacks detection.

Vehicle ad hoc networks (VANETs) are considered to be the next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Due to the very fast move and high dynamic in VANETs, it is important to quickly ascertain the reliability of information. Although intrusion detection system (IDS) has been proposed as a reliable approach to protect VANETs against attacks, its overhead is serious, which spends too much time on detection, especially when the number of vehicles increases. Thus, in this paper, we propose a novel filter model based on a hidden generalized mixture transition distribution model (HgMTD) in VANETs, called FM-HgMTD, which can quickly filter the messages from neighboring vehicles so as to reduce the overhead and detection time. It adopts a well-known multi-objective optimization (NSGA-II) algorithm combined with an expectation-maximization (EM) algorithm to forecast the future states of neighboring vehicles and then to filter out malicious messages, by monitoring the change of the state pattern of each neighboring vehicle. In addition, a timeliness method is used to maintain the accuracy of the forecast. The experiments show that IDS with the proposed FM-HgMTD has better performance than other available IDSs in terms of detection rate, detection time, and overhead.