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Finite-time fault-tolerant control of teleoperating cyber physical system against faults

Journal of systems engineering and Electronics 2023年第2期34卷 469-478页

作者： PAN Chengwei LIU Xia CHEN Yong LI Meng School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Institute of Electric Vehicle Driving System and Safety Technology University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical Engineering and Electronic Information Xihua UniversityChengdu 610039China

This paper studies a finite-time adaptive fractionalorder fault-tolerant control(FTC)scheme for the slave position tracking of the teleoperating cyber physical system(TCPS)with external disturbances and actuator *** on the fractional Lyapunov stability theory and the finite-time stability theory,a fractional-order nonsingular fast terminal sliding mode(FONFTSM)control law is proposed to promote the tracking and fault tolerance performance of the considered ***,the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external ***,the finite-time stability of the closed-loop system is ***,comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.

关键词： teleoperating cyber physical system(TCPS) fractional Lyapunov stability fault-tolerant control(FTC) finite-time stability

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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On the Security of Smart Home systems:A Survey

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Journal of Computer science & technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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Wireless Interference Classification with Low Complexity Multi-Branch Networks

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China Communications 2023年第4期20卷 382-394页

作者： Song Ma Yufan Cheng Ying Mou Pengyu Wang Qihang Peng Jun Wang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential ***,deep learning(DL)based WIC methods have been ***,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is *** this end,we propose three effective ***,we introduce multibranch convolutional neural networks(CNNs)for interference *** multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for *** design avoids the carefully crafted selection of each ***,multi-branch CNNs are computationally expensive and *** this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient ***,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of *** results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.

关键词： electromagnetic interference wireless interference identification deep learning multi-branch architectures

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. Security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling Data privacy Protocols Databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Railway point machines Fault diagnosis Multi-time-scale Variational mode decomposition

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IMCSCL: Image-Based Malware Classification using Self-Supervised and Contrastive Learning 23

IMCSCL: Image-Based Malware Classification using Self-Superv...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Jia, Yetao Meng, Yangyang Zhuang, Honglin National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9798350319583

The use of malware for illicit cyber activities, including network attacks and information theft, poses a severe threat to cybersecurity. In comparison to traditional malware detection methods based on signature and heuristics, machine learning and deep learning-based malware detection methods demonstrate superior generalization ability. However, existing research still faces challenges such as reliance on relatively single malware features, inadequate ability to describe malware features, and overdependence on labeled data. In this paper, we propose an image-based malware classification method using self-supervised and contrastive learning, named IMCSCL. We visualize malware using opcode semantic features, and then detect malware using a contrastive learning method with improved feature encoder network. Experimental results demonstrate that IMCSCL achieves higher detection accuracy compared to supervised malware detection methods, achieving 98.85% accuracy on the Microsoft Malware Classification Challenge dataset. Fine-tuning the model using randomly selected 5% labeled samples from the training set still achieved high accuracy of 94.22%. IMCSCL exhibits superior generalization ability, faster convergence speed, and better training stability. Moreover, contrastive learning significantly reduces malware labeling costs while effectively enhancing detection performance. © 2023 IEEE.

关键词： Image classification

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Service Function Chain Migration in LEO Satellite Networks

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China Communications 2024年第3期21卷 247-259页

作者： Geng Yuhui Wang Niwei Chen Xi Xu Xiaofan Zhou Changsheng Yang Junyi Xiao Zhenyu Cao Xianbin School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of ChinaBeijing 100191China Shanghai Key Laboratory of Satellite Network Shanghai 201204China Shanghai Satellite Network Research Institute Co. Ltd.Shanghai 201204China

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite *** due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service *** this paper,we consider migration and establish an online migration model,especially considering the dynamic *** the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as *** to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are *** results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

关键词： network function virtualization(NFV) resource allocation satellite networks service function chain(SFC) SFC migration SFC placement soft-ware defined network(SDN)

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Overcoming bubble formation in polydimethylsiloxane-made PCR chips: mechanism and elimination with a high-pressure liquid seal

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Microsystems & Nanoengineering 2024年第5期10卷 209-219页

作者： Shiyuan Gao Tiegang Xu Lei Wu Xiaoyue Zhu Xuefeng Wang Xiaohong Jian Xinxin Li State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of SciencesBejing 100049China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology Metabolomics CenterHaixia Institute of Science and TechnologySchool of Future TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China School of Biological Engineering Sichuan University of Science and EngineeringYibin 644000China

The thermal expansion of gas and the air permeability of polydimethylsiloxane(PDMS)were previously thought to be the main causes of bubbles and water loss during polymerase chain reaction(PCR),resulting in a very complex chip design and ***,by calculating and characterizing bubble formation,we discovered that water vapor is the main cause of *** PCR,heat increases the volume of the bubble by a factor of only~0.2 in the absence of water vapor but by a factor of~6.4 in the presence of water *** addition,the phenomenon of"respiration"due to the repeated evaporation and condensation of water vapor accelerates the expansion of bubbles and the loss of water.A water seal above 109 kPa can effectively prevent bubbles in a bare PDMS chip with a simple structure,which is significant for the wide application of PDMS chips.

关键词： bubble expansion elimination

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