Euler-Lagrange multi-agent systems (MASs), which are typical IoT systems, have wide applications including autonomous underwater vehicles, mobile robots and robot manipulators. As a crucial aspect of Euler-Lagrange MASs, the consensus becomes a research priority. However, most of the existing works on consensus of Euler-Lagrange MASs are based on the assumption of linearized parameters, and the designed distributed event-triggered (ET) control protocols are only applicable to the leader-following consensus (LFC) of uncertain Euler-Lagrange MASs with undirected graphs. Hence, the Leaderless consensus (LLC) of uncertain Euler-Lagrange MASs with digraphs is studied in this paper. Based on fuzzy logic system (FLS), a fully distributed ET adaptive control protocol is designed, and new theoretical analysis results are proposed in Lemmas 3–5. By the proposed fully distributed ET fuzzy control protocol, the communication resources are effectively saved, and the LLC for uncertain Euler-Lagrange MASs without the assumption of linearized parameters is achieved. In the end, based on networked two-linked robot manipulators, simulations are given to show the validity of the proposed results. In comparison with the method of [28], our method with σi= 0.005 provides 263.48%, 245.87%, 237.15%, 210.46% and 210.72% less information exchange on manipulators 1-5, respectively.

This article is focused on the secure consensus of multi-agent systems (MASs) with Euler-Lagrange (EL) dynamics and multiple DoS attacks. First, a new model of the multiple DoS attacks is built, which is based on discrete sampled-data communication and considers the joint impact of the multiple DoS attacks. Second, under multiple DoS attacks, two new technical results are proposed, which lay a good foundation for consensus analysis. Then, by designing an adaptive distributed control (DC) protocol combining with two new auxiliary systems, secure consensus results are derived for MASs with EL dynamics and multiple DoS attacks. Finally, simulations based on networked two-linked robot manipulators are presented to show the effectiveness of the theoretical results.

This article investigates the secure consensus problem for nonlinear leader-following multiagent systems (MASs) under denial-of-service (DoS) attacks. First, an improved memory-based adaptive event-triggered mechanism (MAETM) is proposed to reduce data redundancy and save network resources. Unlike previous MAETMs, in order to effectively prevent excessively long data triggering periods or overly frequent triggering, the proposed MAETM introduces an upper limit and a lower limit to limit the threshold range. In this way, the communication resources can be effectively saved. In addition, considering the impact of DoS attacks, a new dynamic-memory-weight-dependent (DMW-dependent) security control protocol is proposed. Unlike control methods that use fixed weights, the protocol dynamically adjusts the weights of historically released packets according to DoS attacks, thus more fully utilizing the information of successfully transmitted packets to mitigate the impact of DoS attacks. Subsequently, sufficient conditions for the secure consensus of MASs are derived by constructing Lyapunov–Krasovskii functionals (LKFs) and using the law of large numbers and the Lagrange mean value theorem. Finally, two numerical simulations are provided to verify the effectiveness of the proposed MAETM and DMW-dependent security control protocol.

Understanding the network characteristics of IP nodes and identifying their potential usage scenarios are crucial for cyberspace applications, such as asset evaluation, fraud prevention, and network attack prevention. However, many studies related to IP nodes primarily focused on IP geolocation and anomaly detection, with very little attention given to IP usage. Identifying the usage scenarios of IP node can facilitate network optimization, enhance security and improve resource allocation, which is crucial for network management and security. In this work, we propose a novel framework named GraphCyber based on graph neural network to identify street-level IP usage for cyberspace mapping. We first design a topology rule-based approach dividing a large number of IP nodes into regional blocks. Then we devise regional blocks to fuse the self-information of IP nodes and various neighborhood relationships into the graph. Last, based on an uncertainty-aware graph neural network, we identify the usage scenarios of IP nodes within regional blocks. Extensive experiments conducted on three large-scale real-world data sets demonstrate the superiority of GraphCyber over several state-of-the-art baselines in accurately identifying the IP usage scenarios.

In this article, we focus on a wireless multiuser uplink network consisting of a single antenna access point (AP) and multiple single antenna users, in which each user transmits time-sensitive confidential message to the AP in a time-division multiple access (TDMA) manner. When a user is scheduled to transmit, the other users will be regarded as potential eavesdroppers. In practical Internet of Things (IoT) applications, different users may have different requirements for throughput, and the timeliness of information needs to be guaranteed. In order to effectively adapt to these heterogeneous application requirements, the average weighted sum Age of Information (AoI) minimization problem is formulated under the premise of satisfying the minimum sampling rate requirement, power allocation constraint, and user scheduling constraint. In order to solve this problem, we first propose two stationary randomized scheduling policies, which are modeled as D/Geom/1 and Geom/Geom/1 queueing systems, respectively, and design two algorithms to find the optimal sampling period of D/Geom/1 system, the sampling probability of Geom/Geom/1 system, the power ratio allocated to confidential information, and the user scheduling probability. Second, an AoI-aware adaptive secure transmission scheme (AASTS) is proposed under Lyapunov optimization framework by transforming the original time-average weighted sum AoI minimization problem into a real-time optimization problem related to data queue state and AoI evolution of every time slot. Numerical results show that the proposed AASTS scheme can achieve better average AoI performance, and the D/Geom/1 system is superior to the Geom/Geom/1 one.

Recent studies have demonstrated the potentials of federated learning (FL) in achieving cooperative and privacy-preserving data analytics. It would also be promising if FL can be employed in vehicular ad hoc networks (VANETs) for cooperative learning tasks, such as steering angle prediction, trajectory prediction, drivable road detection, etc., among integrated vehicles. However, since VANETs are characterized by ad hoc cooperating vehicles with non-independent and identically distributed (Non-IID) data, directly employing existing FL frameworks to VANETs may cause extensive communication overhead and compromised model performance. Further, most of the existing deep learning models incorporated in FL frameworks rely heavily on data with manual annotations, leading to a huge labor cost. To address these issues, in this paper we propose an efficient and effective Federated End-to-End Learning framework for cooperative learning tasks in VANETs, named FEEL. Specifically, we first formulate a distributed optimization problem for cooperative deep learning tasks with Non-IID data in multi-hop cluster VANETs. Second, two algorithms for inter-cluster learning and inner-cluster learning are respectively designed, to reduce the communication overhead and fit Non-IID data. Third, a Paillier-based communication protocol is crafted, allowing secure model parameter updates at the central server without knowing the real updates at each cooperating base station. Extensive experiments on two real-world datasets are conducted by considering various data distributions and VANET topologies, demonstrating the high efficiency and effectiveness of the proposed FEEL framework in both regression and classification tasks.

The explosive growth in network traffic in recent times has resulted in increased processing pressure on network intrusion detection systems. In addition, there is a lack of reliable methods for preprocessing network traffic generated by benign applications that do not steal users’ data from their devices. To alleviate these problems, this study analyzed the differences between benign and malicious traffic produced by benign applications and malware, respectively. To fully express these differences, this study proposed a new set of statistical features for training a clustering model. Furthermore, to mine the communication channels generated by benign applications in batches, a semisupervised clustering method was adopted. Using a small number of labeled samples, our method aggregated historical network traffic into two types of clusters. The cluster that did not contain labeled malicious samples was regarded as a benign traffic cluster. The experimental results were compared using four types of clustering algorithms. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm was selected to mine benign communication channels. We also compared our method with two other methods, and the results demonstrated that the benign channels mined through our method were more reliable. Finally, using our method, 1,811 benign transport layer security (TLS) channels were mined from 18,357 TLS communication channels. The number of flows carried by these benign channels comprised 65.37% of the entire network flows, and no malicious flow was included in our results, which proves the effectiveness of our method.

Bitrate is generally regarded as an important criterion of video quality. However, with sophisticated video editing software, forgers can create fake bitrate videos by up-converting the bitrate of original videos with lower video quality to attract more viewers on video sharing websites. In this work, we first model the generation process of fake bitrate videos and analyze the dominant sources of information loss. It is found that the recompression error generated by the proposed one-step-further recompression operation is an efficient measurement to expose distinguishable quality variation tendencies between true and fake bitrate videos. Based on this analysis, we propose a detection method for fake bitrate videos using a hybrid deep-learning network from recompression error. For an input video, the patch-wise recompression errors are first calculated to increase the learning capability of the network. To learn robust representations of recompression errors in local regions with different degrees of predictability, a hybrid deep-learning network that contains two branches with heterogeneous structures is designed. For noise-like recompression errors, the first branch has a shallow CNN structure initialized with an Inception-like module using multisize convolutional kernels. For zero-element clustered recompression errors, the second branch has a multi-layer perceptron structure equipped with a unique layer that extracts the histogram of zero-element clustered square regions. The output vectors of different branches are concatenated and then jointly optimized to obtain the patch-wise detection results. Finally, the majority voting (local-to-global) strategy is applied to obtain the final detection result. Extensive experiments are conducted to evaluate the detection performance under various coding parameter settings, such as different bitrates, rate-distortion optimization strategies and so on. The experimental results demonstrate the superiority of the proposed method compared with several state-of-the-art methods to provide more fine-grained forensic clues.