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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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A Comprehensive Survey on Advanced Persistent Threat (APT) Detection Techniques

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computers, Materials & Continua 2024年第8期80卷 2675-2719页

作者： Singamaneni Krishnapriya Sukhvinder Singh Department of Computer Science School of Engineering and TechnologyPondicherry UniversityKalapet605014India

The increase in number of people using the Internet leads to increased cyberattack *** Persistent Threats,or APTs,are among the most dangerous targeted *** attacks utilize various advanced tools and techniques for attacking targets with specific *** countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted *** is a sophisticated attack that involves multiple stages and specific ***,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security ***,APTs are generally implemented in multiple *** one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack *** detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing *** survey paper will provide knowledge about APT attacks and their essential *** follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the *** used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.

关键词： Advanced persistent threats APT cyber security intrusion detection cyber attacks

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VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

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computers, Materials & Continua 2024年第3期78卷 4205-4223页

作者： Youlin Ji Bo Yin Ke Gu School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China

With its untameable and traceable properties,blockchain technology has been widely used in the field of data *** to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data *** this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in *** the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy ***,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF *** propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF *** framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy *** data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy *** propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query *** VB-cm tree uses the vector commitment to verify the query *** fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing *** conduct an extensive evaluation of the proposed *** experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

关键词： Security data sharing blockchain data query privacy protection

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao xia, xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Android Dynamic Malware Detection Method Based on System Call Sequences 9

Android Dynamic Malware Detection Method Based on System Cal...

引用

9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Liu, xiaojian Zhang, Yifei Duan, Yaoyao Hou, Beibei School of Computer Science and Technology Xi'an University of Science and Technology Shaanxi Xi'an China

ISBN: (纸本)9798350376548

With the continuous evolution of the types and attack strategies of Android malware, the methods for detecting them must also innovate and evolve. Although existing dynamic detection methods mostly rely on machine learning, they still face numerous challenges in accurately identifying malicious software features. In order to more efficiently detect Android malware, this paper proposes an Android malware detection method based on the CNN-LSTM model. This method uses Strace to capture system call sequence logs and selects certain function parameters from the system calls to construct feature vectors. Finally, a CNN-LSTM model is employed for training and classification. Experimental analysis shows that compared to other machine learning methods and traditional CNN and LSTM networks, the classification performance of the CNN-LSTM model shows the best performance in all evaluation indicators. © 2024 IEEE.

关键词： Android malware

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Slender loris optimization algorithm for solving optimization problems

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8799-8810页

作者： Lakshmi, A. Vijaya Vaitheki, K. Joseph, K. Suresh Department of Computer Science School of Engineering and Technology Pondicherry University India

Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. © 2024 – IOS Press. All rights reserved.

关键词： Benchmarking

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of computer science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui xiang Wang Tianlong Chen Meng Wang xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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On the mechanism of Si-promoted destabilization of TiC_(x)particles in Al alloys

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Journal of Materials science & technology 2023年第34期165卷 17-26页

作者： H.Dong Y.C.Guo Y.Z.Chen F.xia Q.Q.Guo Q.Chen G.L.Li J.Y.Zhao Y.Zhong J.P.Li School of Materials and Chemical Engineering Xi’an Technological UniversityXi’an 710021China State Key Lab.of Solidification Processing Northwestern Polytechnical UniversityXi’an 710072China School of Science Xi’an University of Science and TechnologyXi’an 710054China

TiC_(x)is an excellent composite strengthening particle and grain refiner for Al ***,the stability of TiC_(x)is poor when solute Si exists in Al alloy melts,which significantly depresses its strengthening and grain refining *** this work,the destabilization mechanisms of the TiC_(x)particles in Al-Si alloy melt with a composition of Al-7Si-7.5TiC were explored via experiments,first-principles calculations and thermodynamic *** experimental results show that Si atoms diffuse into TiC_(x)and Ti atoms are released into the Al melt to form a Ti-rich transition zone during the insulation of TiC_(x)in Al-Si melt,and the TiAlySiz and Al_(4)C_(3)phases are solidified in the Ti-rich zone and at Ti-rich zone/TiC_(x)interface,*** first principles calculations show that the low formation energy of C vacancies facilitates the rapid diffusion of Si atoms in TiC_(x),while the doping of Si atoms reduces the energy barrier of diffusion of Ti atoms in TiC_(x)and promotes the formation of Ti-rich *** thermodynamic calculations show that the wide crystallization temperature range of the destabilized product TiAlySiz phase is the key to continuous decomposition of TiC_(x)*** addition,the driving force of the main destabilization reaction of TiC_(x)in the Al-Si alloys is about 44 times higher than that in the Al alloys without Si *** indicates that the presence of solute Si remarkably promotes the subsequent decomposition process of TiC_(x)in the Al-Si alloy melts.

关键词： Al-Si alloy TiC_(x)particles Destabilization First principles calculations Thermodynamics

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Dual-channel graph contrastive learning for multi-label classification with label-specific features and label correlations

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Neural Computing and Applications 2024年第23期36卷 14483-14502页

作者： Zhu, xiaoyan Zhu, Tong Li, Jiaxuan Wang, Jiayin School of Computer Science and Technology Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China

In multi-label classification scenarios, the labels have both interactive correlations and their own respective characteristics. It is a meaningful but challenging task that learning the discriminative features specific to each label while simultaneously utilizing the correlations among multiple labels. Recently, the graph-based methods that can collaboratively learn label-specific features and correlated label semantics have shown tremendous potential for multi-label classification tasks. However, these approaches only calculate the co-occurrence probabilities between pairwise labels;they ignore high-order label correlations. Moreover, the topological structure of the label relational graph employed in this type of method is completely fixed, making the label correlations heavily dataset-dependent and limiting the generalization ability of the method. To address these issues, we propose a dual-channel graph contrastive learning method named DGCL to generate label-specific features using both second-order and high-order label correlations. Specifically, a hypergraph neural network is first employed to explore high-order label correlations. Second, an adaptive graph convolutional neural network is designed to model second-order label correlations. Finally, we construct a contrastive learning objective to collaboratively update these two label correlation semantics, which are subsequently used to guide the process of generating label-specific features. Experimental results on ten benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art multi-label classification methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Graph neural networks

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circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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Frontiers of computer science 2023年第5期17卷 217-225页

作者： Yajing GUO xiujuan LEI Lian LIU Yi PAN School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

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