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Unsupervised intrusion detection system for in-vehicle communication networks

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Journal of Safety Science and Resilience 2024年第2期5卷 119-129页

作者： Kabilan N Vinayakumar Ravi V Sowmya center for Computational Engineering and networking(CEN) Amrita School of Artificial InteligenceAmrita Vishwa VidyapeethamCoimbatoreIndia center for Artificial Intlligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia

In-vehicle communication has been optimized day to day to keep updated of the *** area network(CAN)is used as a standard communication method because of its efficient and reliable ***,CAN is prone to several network level attacks because of its lack in security *** methods have been introduced to incorporate this in *** proposed an unsupervised method of intrusion detection for in-vehicle communication networks by combining the optimal feature extracting ability of autoencoders and more precise clustering using fuzzy C-means(FCM).The proposed method is light weight and requires less computation *** performed an extensive experiment and achieved an accuracy of 75.51%with the ML35o in-vehicle intrusion *** experimental result,the proposed method also works better for other intrusion detection problems like wireless intrusion detection datasets such as WNS-DS with accuracy of 84.05%and network intrusion detection datasets such as KDDCup with accuracy 60.63%,UNSW_NB15 with accuracy 73.62%and Information Security center of Excellence(Iscx)with accuracy 74.83%.Overall,the proposed method outperforms the existing methods and avoids labeled datasets when training an in-vehicle intrusion detection *** results of the experiment of our proposed method performed on various intru-sion detection datasets indicate that the proposed approach is generalized and robust in detecting intrusions and can be effectively deployed in real time to monitor CAN traffic in vehicles and proactively alert during attacks.

关键词： Controlarea network Intrusiondetection Unsupervised learning Autoencoders Fuzzy C-means

Distributed adaptive prescribed-time orbit containment control for satellite cluster flight

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Chinese Journal of Aeronautics 2024年第8期37卷 342-357页

作者： Tingting ZHANG Shijie ZHANG Huayi LI Xiangtian ZHAO Research center of Satellite Technology Harbin Institute of TechnologyHarbin 150080China State Key Laboratory of networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

The distributed prescribed-time orbit containment control for the satellite cluster flight with multiple dynamic leaders is *** directed information communication topology between followers is taken into account in the overall *** the satellite mass is assumed to be constant,a distributed prescribed-time orbit containment controller is,firstly,presented to drive the followers into the dynamic convex hull produced by multiple ***,the parameter uncertainty is considered,and a prescribed-time sliding mode estimator is introduced to estimate the desired velocity of each *** on the estimated state,a novel distributed adaptive prescribed-time orbit containment control scheme is *** Lyapunov stability theory is utilized to prove the prescribed-time stability of the closed-loop ***,several numerical simulations and comparison of different control methods are provided to verify the effectiveness and superiority of the proposed control method.

关键词： Satellite cluster Orbit containment control Prescribed-time control Parameter uncertainty Directed communication graph

QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

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Computers, Materials & Continua 2024年第5期79卷 3007-3021页

作者： Yu Song Xusheng Qian Nan Zhang Wei Wang Ao Xiong State Key Laboratory of networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Marketing Service center State Grid Jiangsu Electric Power Co.Ltd.Nanjing220000China Customer Service center State Grid Co.Ltd.Nanjing211161China

To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods.

关键词： Deep reinforcement learning SDN route optimization QoS

An attribute-based access control scheme using blockchain technology for IoT data protection

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High-Confidence Computing

High-Confidence Computing 2024年第3期4卷 41-50页

作者： Zenghui Yang Xiubo Chen Yunfeng He Luxi Liu Yinmei Che Xiao Wang Ke Xiao Gang Xu Information Security center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China Information center of China North Industries Group Corporation Beijing 100089China School of Information Science and Technology North China University of TechnologyBeijing 100144China

With the wide application of the Internet of Things(IoT),storing large amounts of IoT data and protecting data privacy has become a meaningful *** general,the access control mechanism is used to prevent illegal users from accessing private ***,traditional data access control schemes face some non-ignorable problems,such as only supporting coarse-grained access control,the risk of centralization,and high trust *** this paper,an attribute-based data access control scheme using blockchain technology is *** address these problems,attribute-based encryption(ABE)has become a promising solution for encrypted data access ***,we utilize blockchain technology to construct a decentralized access control scheme,which can grant data access with transparency and ***,our scheme also guarantees the privacy of policies and attributes on the blockchain ***,we optimize an ABE scheme,which makes the size of system parameters smaller and improves the efficiency of *** optimizations enable our proposed scheme supports large attribute universe requirements in IoT ***,to prohibit attribute impersonation and attribute replay attacks,we design a challenge-response mechanism to verify the ownership of ***,we evaluate the security and performance of the *** comparisons with other related schemes show the advantages of our proposed *** to existing schemes,our scheme has more comprehensive advantages,such as supporting a large universe,full security,expressive policy,and policy hiding.

关键词： Attribute-based encryption Access control Blockchain IoT data protection

Intelligent Internet of Things with Reliable Communication and Collaboration Technologies

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China Communications 2024年第8期21卷 I0002-I0006页

作者： Zhao Junhui Wu Celimuge Xu Wenjun Qi Chenhao Bu Shengrong Zhang Shuowen Zhang Qingmiao the School of Electronics and Information Engineering Beijing Jiaotong University the School of Information Engineering in East China Jiaotong University Meta-networking Research center the University of Electro-Communications the School of Artificial Intelligence BUPT State Key Laboratory of networking and Switching Technology the Peng Cheng Laboratory the faculty of the School of Information Science and Engineering Southeast University Brock University the Hong Kong Polytechnic University East China Jiaotong University

The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information *** is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and *** these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.

关键词： interaction Internet IoT

Cooperative localization methods of autonomous underwater vehicle swarm integrated with inverted ultrashort baseline

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Chinese Journal of Acoustics 2024年第1期43卷 1-22页

作者： LIU Feng WANG Yuxiong CHEN Huifang XIE Lei College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027 Zhejiang Provincial Laboratory of Information Processing Communication and NetworkingHangzhou 310027 Zhoushan Ocean Research center Zhejiang UniversityZhoushan 316021

A cooperative localization factor graph for the autonomous underwater vehicle(AUV) swarm integrated with inverted ultrashort baseline(iUSBL) is constructed. Two cooperative localization methods of slave AUV in an AUV swarm are proposed, namely the extended Kalman filter(EKF)-based cooperative localization method and particle belief propagation(BP)-based cooperative localization method, where the effect of the positioning error of leader AUV is mitigated. For the EKF-based cooperative localization method, the state of the slave AUV is reconstructed with the positioning information of leader AUV to achieve cooperative localization. In the particle-BP-based cooperative localization method,the measurement particles are generated to realize cooperative localization. The proposed methods can solve the poor real-time problem of traditional methods, and only require oneway acoustic positioning transmission measurement to realize the localization of the slave AUV. Simulation results show that the two proposed cooperative localization methods work well for slave AUVs with different localization abilities. Compared with the dead-reckoning method, the localization errors of two proposed methods are reduced by more than 80%,and the positioning error accumulation is effectively suppressed. Furthermore, the proposed cooperative localization methods can operate normally under a high packet loss rate of the underwater acoustic channel, and the localization performance is not significantly degraded.

关键词： underwater autonomous cooperative

A survey on blockchain-enabled federated learning and its prospects with digital twin

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Digital Communications and Networks 2024年第2期10卷 248-264页

作者： Kangde Liu Zheng Yan Xueqin Liang Raimo Kantola Chuangyue Hu State Key Lab on Integrated Services Network School of Cyber EngineeringXidian UniversityXi'anChina Department of Communications and networking Aalto UniversityEspooFinland Yangtze Delta Region Blockchain Technology Research Institute JiaxingChina

Digital Twin(DT)supports real time analysis and provides a reliable simulation platform in the Internet of Things(IoT).The creation and application of DT hinges on amounts of data,which poses pressure on the application of Artificial Intelligence(AI)for DT descriptions and intelligent *** Learning(FL)is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model ***,DT can benefit by combining with FL,successfully solving the"data island"problem in traditional ***,FL still faces serious challenges,such as enduring single-point failures,suffering from poison attacks,lacking effective incentive *** the successful deployment of DT,we should tackle the issues caused by *** from industry and academia have recognized the potential of introducing Blockchain Technology(BT)into FL to overcome the challenges faced by FL,where BT acting as a distributed and immutable ledger,can store data in a secure,traceable,and trusted ***,to the best of our knowledge,a comprehensive literature review on this topic is still *** this paper,we review existing works about blockchain-enabled FL and visualize their prospects with *** this end,we first propose evaluation requirements with respect to security,faulttolerance,fairness,efficiency,cost-saving,profitability,and support for ***,we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation ***,we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL,based on which we further propose some directions for future research.

关键词： Digital twin Artificial intelligence Federated learning Blockchain

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

GUEST EDITORIAL Intelligent Internet of Things with Reliable Communication and Collaboration Technologies

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China Communications 2024年第8期 4-8页

The Internet of Things(IoT) connects objects to Internet through sensor devices, radio frequency identification devices and other information collection and processing devices to realize information interaction. IoT is widely used in many fields, including intelligent transportation, intelligent healthcare, intelligent home and industry.

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