It is challenging to ensure car driving comfort and safety under denial-of-service (DoS) jamming attacks. For this reason, this article proposes a resilient-based and attack-intensity adaptive event-triggered control scheme to study the $\mathcal{H}_{\infty}$ controlled networked active quarter-vehicle suspension systems (QVSSs), which reduces the frequency of controller updates and provides robust resistance to DoS attacks. It is shown that if the tendency of DoS attack intensity increases, more communication signals can be transmitted to the controller. Conversely, if the tendency of DoS attack intensity decreases, fewer communication signals are being utilized. Using the novel two-side looped-functional method and some technical lemmas, sufficient conditions for the exponential stability and $\mathcal{H}_{\infty}$ control performance of QVSSs are obtained. Simulation results indicate that the suggested control approach enhances driving comfort and safety while reducing the data transmission burden.

With the wide use of Cyber-Physical Systems (CPS) in many applications, targets of advanced persistent threats (APTs) have been extended to the IoT and industrial control systems. Provenance graph analysis based on system audit logs has become a promising way for APT detection and investigation. However, we cannot afford to ignore that existing provenance-based APT detection systems lack the process–context information at system runtime, which seriously limits detection performance. In this paper, we proposed ConGraph, an approach for detecting APT attacks using provenance graphs combined with process context; we presented a module for collecting process context to detect APT attacks. This module collects file access behavior, network access behavior, and interactive relationship features of processes to enrich semantic information of the provenance graph. It was the first time that the provenance graph was combined with multiple process–context information to improve the detection performance of APT attacks. ConGraph extracts process activity features from the provenance graphs and submits the features to a CNN-BiLSTM model to detect underlying APT activities. Compared to some state-of-the-art models, our model raised the average precision rate, recall rate, and F-1 score by 13.12%, 25.61%, and 24.28%, respectively.

Negative selection algorithm (NSA) is an important detector generation algorithm of artificial immune system (AIS). Traditional NSAs randomly generate detectors in the whole antigen space without considering the distribution of self/non-self antigens, therefore it is difficult for detectors to cover the whole antigen space evenly and a seriously time-consuming self-tolerance process is required. Aiming at the problem, we proposed a novel NSA based on antigen space triangulation coverage (ASTC) called ASTC-RNSA. In order to avoid the randomness in traditional NSAs, the proposed algorithm employed Delaunay Triangulation method from computational geometry to divide the self space into simplicial cells, which are utilized to determine the detector positions. Then, the overlaps between simplicial cells and self-antigens are removed to form a set of triangulation coverage areas. Finally, immune detectors which only covered non-self antigens space are generated in each triangulation coverage area. After Delaunay triangulation of antigen space, ASTC-RNSA can directly determine sui detector positions and avoid the time-consuming self-tolerance process of traditional NSAs. The analysis result shows that the time complexity of ASTC-RNSA is reduced from traditional exponential level to logarithmic level. The experimental results on several UCI datasets and artificial datasets show that the proposed algorithm can achieve more than 10 times the detector generation efficiency while maintaining similar detection performance with the most widely used representative typical algorithm, V-Detector.

In recent years, palmprints have been extensively utilized for individual verification. The abundance of sensitive information in palmprint data necessitates robust protection to ensure security and privacy without compromising system performance. Existing systems frequently use cancelable transformations to protect palmprint templates. However, if an adversary gains access to the stored database, they could initiate a replay attack before the system detects the breach and can revoke and replace the reference template. To address replay attacks while meeting template protection criteria, we propose a dual-level cancelable palmprint verification framework. In this framework, the reference template is initially transformed using a cancelable competition hashing network with a first-level token, enabling the end-to-end generation of cancelable templates. During enrollment, the system creates a negative database (NDB) using a second-level token for further protection. Due to the unique NDB-to-vector matching characteristic, a replay attack involving the matching between the reference template and a compromised instance in NDB form is infeasible. This approach effectively addresses the replay attack problem at its root. Furthermore, the dual-level protected reference template enjoys heightened security, as reversing the NDB is NP-hard. We also propose a novel NDB-to-vector matching algorithm based on matrix operations to expedite the matching process, addressing the inefficiencies of previous NDB methods reliant on dictionary-based matching rules. Extensive experiments conducted on public palmprint datasets confirm the effectiveness and generality of the proposed framework. Upon acceptance of the paper, the code will be accessible at https://github.com/Zi-YuanYang/DCPV.

Negative selection algorithm (NSA) is an important kind of the one‐class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional “Random‐Discard” model to the “Computing‐Designated” model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one‐class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.