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Chess Puzzle: Evolution of Cyber Deception Strategies for IoT Security Enhancement

IEEE Internet of Things Journal 2025年第11期12卷 16668-16680页

作者： Wang, Xiran Shi, Leyi Zhao, Zhihao Luo, Wenbin Ding, Bingtao School of Computer Science and Technology Qingdao266580 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

Nowadays, the proliferation of open Internet of Things (IoT) devices has made IoT systems increasingly vulnerable to cyber attacks. It is of great practical significance to solve the security issues of IoT systems. Drawing inspiration from the concept of "Pawn Promotion" in chess, this article proposes a cyber deception defense system based on "Chess Puzzle" (CP-CDD), which enhances the security of IoT systems by continuously changing the real and fake attributes of services. First, we design a distributed election control device scheme to address the security concerns arising from centralized control in the IoT system. Second, we introduce an adaptive differential evolution cyber deception strategy (ADECDS) optimization method, aiming to derive effective cyber deception defense strategies for the CP-CDD system. Specifically, we design two key components of cyber deception strategy evolution: 1) service strategy coding and 2) fitness function, and describe the ADECDS algorithm. Finally, the security and effectiveness of the CP-CDD system is proven through attack and defense experiments conducted in a real-world environment. © 2014 IEEE.

关键词： Cyber attacks

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Event-based nonsingular fixed-time containment control for nonlinear multiagent systems with dynamic uncertainties

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science China(Information sciences) 2025年第5期68卷 413-414页

作者： Yuanbo SU Qihe SHAN Tieshan LI C.L.Philip CHEN Navigation College Dalian Maritime University School of Automation Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering South China University of Technology

Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.

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Predictive Modelling of Diabetes Complications: Insights from Binary Classifier on Chronic Diabetic Mellitus

Predictive Modelling of Diabetes Complications: Insights fro...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Panigrahy, Smita Dash, Sachikanta Padhy, Sasmita Kumar, Naween Dash, Yajnaseni Computer Science And Engineering Giet University Odisha Gunupur India School Of Computing Science And Engineering Vit Bhopal University MP Bhopal India School Of Computer Science Engineering And Technology Bennett University Uttar Pradesh Greater Noida India School Of Computer Science Engineering And Technology Bennett University Uttar Pradesh India

ISBN: (纸本)9798350366846

Chronic diabetes Mellitus (DM) leads to severe consequences such as nephropathy, neuropathy, and retinopathy. Various learning algorithms are employed to forecast and categorize eight issues, encompassing metabolic syndrome and dyslipidemia. Machine learning is revolutionizing various domains by enabling algorithms to extract models from data. The main objective of this proposed study is to utilize binary classifiers to predict and categorize eight problems associated with DM. For this study we have used the dataset obtained from the Jeevandeep Hospital and Research Centre (JHRC) in Baripada, Mayurbhanj, Odisha, India. It includes 1296 records and has nine input variables and eight output categories that indicate different issues. Multiple models were constructed to address problems like Nephropathy, neuropathy, dyslipidemia, diabetic foot, metabolic syndrome, retinopathy, hypertension, and obesity. Different preprocessing operations on missing data, unbalanced data, and feature selection are employed. Binary classifiers were developed for each type of complication, with the number of records varied for each. Subsequently, an evaluation was conducted. The models achieved a maximum of 95.7% accuracy and 97.9% F1 score. The study demonstrates the effectiveness of using selected features to build accurate classifiers for predicting diabetes complications. © 2024 IEEE.

关键词： Learning algorithms

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Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind

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Multimedia Tools and Applications 2025年第11期84卷 9591-9665页

作者： Mohanty, Lopamudra Kumar, Ashish Mehta, Vivek Agarwal, Mohit Suri, Jasjit S. School of Computer Science Engineering and Technology Bennett University Gr. Noida India Department of Computer Science ABES Engineering College UP Ghaziabad India Stroke Diagnostic and Monitoring Division AtheroPoint™ RosevilleCA United States

Trained Artificial Intelligence (AI) models are challenging to install on edge devices as they are low in memory and computational power. Pruned AI (PAI) models are therefore needed with minimal degradation in performance. This is the first study of its kind where we have characterized three types of PAI paradigms, namely, weight pruning (WP), channel pruning (CP), and hybrid pruning (HP). Further, the study also analyses the risk of bias (RoB) in PAI models, and explainable PAI (XPAI) networks. PRISMA model was adapted for the selection of the best 145 studies. The review had two hypotheses: (i) PAI had bias and (ii) the order of performance for the three PAI is: HP > CP > WP in terms of performance. Thirty-two AI attributes using each of two experts were categorized into four clusters, namely, architecture, optimization, dataset design, and scientific validation. The RoB was analyzed in these PAI studies using AP(ai)Bias 2.0 (AtheroPoint™, CA, USA), which was based on a scoring-and-ranking strategy. This was then compared against the Butterfly model, composed of radial and regional biases, and analyzed using a Venn diagram in three independent bins based on low-moderate and moderate-high cut-offs. Recommendations were laid out for low-bias studies. PAI studies that lack adequate AI attributes were more inclined to the RoB. It has been observed that 19.64%, 21.42%, and 58.92% studies were in low-bias, moderate-bias, and high-bias bins, respectively. Pruning made inference faster and reduced sparsity. We have observed low participation of the PAI studies in the XPAI framework. The study presented the challenges and five-step recommendations for mitigating bias in PAI models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Risk assessment

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Defending Federated Learning System from Poisoning Attacks via Efficient Unlearning

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computers, Materials & Continua 2025年第4期83卷 239-258页

作者： Long Cai Ke Gu Jiaqi Lei School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China

Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed ***,the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential *** attacks turn into a major menace to federated learning on account of their concealed property and potent destructive *** altering the local model during routine machine learning training,attackers can easily contaminate the global *** detection and aggregation solutions mitigate certain threats,but they are still insufficient to completely eliminate the influence generated by ***,federated unlearning that can remove unreliable models while maintaining the accuracy of the global model has become a *** some existing federated unlearning approaches are rather difficult to be applied in large neural network models because of their high computational ***,we propose SlideFU,an efficient anti-poisoning attack federated unlearning *** primary concept of SlideFU is to employ sliding window to construct the training process,where all operations are confined within the *** design a malicious detection scheme based on principal component analysis(PCA),which calculates the trust factors between compressed models in a low-cost way to eliminate unreliable *** confirming that the global model is under attack,the system activates the federated unlearning process,calibrates the gradients based on the updated direction of the calibration *** on two public datasets demonstrate that our scheme can recover a robust model with extremely high efficiency.

关键词： Federated learning malicious client detection model recovery machine unlearning

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Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection

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Chinese Physics B 2025年第5期34卷 289-301页

作者： Fei Yu Dan Su Shaoqi He Yiya Wu Shankou Zhang Huige Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China

Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural *** a type of neural network whose dynamic behavior can be explained,the coupling of resonant tunneling diode-based cellular neural networks(RTD-CNNs)with memristors has rarely been reported in the ***,this paper designs a coupled RTD-CNN model with memristors(RTD-MCNN),investigating and analyzing the dynamic behavior of the *** on this model,a simple encryption scheme for the protection of digital images in police forensic applications is *** results show that the RTD-MCNN can have two positive Lyapunov exponents,and its output is influenced by the initial values,exhibiting ***,a set of amplitudes in its output sequence is affected by the internal parameters of the memristor,leading to nonlinear ***,the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy *** tests and security analyses validate the effectiveness of this scheme.

关键词： memristor hyperchaos resonant tunneling diode-based cellular neural network(RTD-CNN) dynamic analysis image encryption

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Video Colorization:A Survey

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Journal of computer science & technology 2024年第3期39卷 487-508页

作者：彭中正杨艺新唐金辉潘金山 School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China CCF IEEE ACM

Video colorization aims to add color to grayscale or monochrome *** existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more formidable obstacles due to the additional necessity for temporal ***,there is rarely a systematic review of video colorization *** this paper,we aim to review existing state-of-the-art video colorization *** addition,maintaining spatial-temporal consistency is pivotal to the process of video *** gain deeper insight into the evolution of existing methods in terms of spatial-temporal consistency,we further review video colorization methods from a novel *** colorization methods can be categorized into four main categories:optical-flow based methods,scribble-based methods,exemplar-based methods,and fully automatic ***,optical-flow based methods rely heavily on accurate optical-flow estimation,scribble-based methods require extensive user interaction and modifications,exemplar-based methods face challenges in obtaining suitable reference images,and fully automatic methods often struggle to meet specific colorization *** also discuss the existing challenges and highlight several future research opportunities worth exploring.

关键词： video colorization deep convolutional neural network spatial-temporal consistency

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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science China(Information sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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Numerical simulation for the initial state of avalanche in polydisperse particle systems

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Chinese Physics B 2024年第2期33卷 405-412页

作者：韩韧李亭迟志鹏杨晖李然 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.

关键词： avalanche initial state polydisperse particle systems propagation

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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