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4th International Conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wanglst, Fang Chen, Chunyi Yu, Haiyang Tian, Zhuozhan School of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

Extracting characteristic information from atmospheric optical channels can be utilized to generate shared keys for communication parties. However, the autocorrelation of channel feature information within the correlation time caused by atmospheric turbulence limits the number of unrelated key bits that can be extracted per second. To address this issue, a shared key extraction scheme for random modulation channels is proposed. The scheme employs a controllable shared random source model with memory (CCRSM) to quantify the observed values of CCRSM to obtain keys. During the quantization phase, an interleave block quantization algorithm is employed. Initially, legitimate parties interleave and group the obtained CCRSM observations to eliminate the impact of temporal autocorrelation;subsequently, the processed CCRSM observations are quantified using a single-threshold method per group to obtain an initial bit sequence;finally, an encoding mapping operation is applied to generate the key sequence. This algorithm ensures that the keys extracted between legitimate communication parties have a low inconsistency rate and good randomness, and the key generation rate is significantly improved. Experimental results indicate that this scheme effectively reduces the key disagreement rate and enhances the key generation rate. Under a signal-to-noise ratio (SNR) of 30dB, the scheme achieves a key disagreement rate of 4.39×10-4 and a key generation rate of 2.99×106 bits per second, and the generated shared random keys pass the randomness tests of the National Institute of Standards and technology (NIST). © 2024 IEEE.

关键词： Autocorrelation

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A Heterogeneous Federated Learning Method Based on Global Classifier and Mean Regularization 7

A Heterogeneous Federated Learning Method Based on Global Cl...

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7th International Conference on computer Information science and Application technology, CISAT 2024

作者： Shi, Yongqiang Feng, Xin Zhang, Jianfei Changchun University of Science and Technology School of Computer Science and Technology Changchun China

ISBN: (纸本)9798350375107

Federated learning is an emerging distributed machine learning paradigm that can protect data privacy security in collaborative training of shared models by multiple parties. However, the shared global model cannot satisfy the individualized needs of multiple parties under system heterogeneity, and data heterogeneity among clients also leads to the global model failing to fit the distribution of each client's dataset. The performance of the model degrades in extreme data heterogeneity and is difficult to converge. To address the above problems, this paper proposes a heterogeneous federated learning method based on Global Classifier and Mean Regularization, FedGCMR, for clients to design personalized models with different model structures to satisfy various demands. The method trains a shared global classifier on the server and uses it to replace the client classifier, transferring the learned global knowledge to the client. In addition, using the global feature mean normalizes the heterogeneous feature extractor and helps it to understand the data distribution in global view for higher quality generic representation. This helps the model converge quickly and improves its adaptability over different data distributions. In this paper, we perform comparative experiments on four image datasets in two data heterogeneous scenarios. The results show that the accuracy of FedGCMR is maximally 4.22% higher than other proposed methods in the model homogeneous setting and maximally 2.12% higher in the model heterogeneous setting. © 2024 IEEE.

关键词： Federated learning

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

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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 paper 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. Firstly, we design a distributed election control device scheme to address the security concerns arising from centralized control in the IoT system. Secondly, we introduce an adaptive differential evolution cyber deception strategy 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: service strategy coding and fitness function, and describe the adaptive differential evolution cyber deception strategy 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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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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Semantic segmentation of UAV remote sensing images based on improved U-Net 8

Semantic segmentation of UAV remote sensing images based on ...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhang, Hua Jiang, Zhengang Zheng, Guoxun Yao, Xuekun Changchun University of Science and Technology School of Computer Science and Technology Changchun China Changchun Institute of Technology School of Computer Technology and Engineering Changchun China

ISBN: (纸本)9798350302455

Semantic segmentation of unmanned aerial vehicle (UAV) remote sensing images has been widely used in various fields of remote sensing;however, manual visual interpretation methods are inefficient and highly dependent on ground investigation and a priori knowledge, which consume huge human and material resources. Therefore, we propose a deep convolutional neural network based on improved U-Net to solve the semantic segmentation problem of UAV remote sensing images. The network is a U-shaped structure with a symmetric encoder and decoder. We conducted experiments on the UAV remote sensing dataset Aeroscapes, and compared it with several other popular semantic segmentation networks, our method achieved significant improvement in segmentation accuracy. © 2023 IEEE.

关键词： Semantic Segmentation

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Effects of feature selection and normalization on network intrusion detection

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Data science and Management 2025年第1期8卷 23-39页

作者： Umar, Mubarak Albarka Chen, Zhanfang Shuaib, Khaled Liu, Yan College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Department of Computer Science Shantou University Guangdong Shantou515063 China

The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence (AI) techniques (such as machine learning (ML) and deep learning (DL)) to build more efficient and reliable intrusion detection systems (IDSs). However, the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs. Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues. While most of these researchers reported the success of these preprocessing techniques on a shallow level, very few studies have been performed on their effects on a wider scale. Furthermore, the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used, which most of the existing studies give little emphasis on. Thus, this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets: NSL-KDD, UNSW-NB15, and CSE–CIC–IDS2018, and various AI algorithms. A wrapper-based approach, which tends to give superior performance, and min-max normalization methods were used for feature selection and normalization, respectively. Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization. The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. The RF models also achieved an excellent performance compared to recent works. The results show that normalization and feature selection positively affect I

关键词： Decision trees

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