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Modulated symbol-based one-time pad secure transmission scheme using physical layer keys

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Science China(information Sciences) 2024年第1期67卷 221-239页

作者： Xiaoyan HU Zheng WAN Kaizhi HUANG Liang JIN Mengyao YAN Yajun CHEN Jinmei YANG information engineering University Purple Mountain Laboratories for Network and Communication security

This paper proposes a novel modulated symbols-based one-time pad(SOTP) secure transmission scheme using physical layer *** classical physical layer key generation and exclusive OR(XOR)encryption in the discrete binary space,we design a framework for modulated symbols-based one-time pad(OTP) encryption,where the cryptographic primitive and mathematical model of SOTP is given to build a practical cryptographic *** with existing physical layer encryption(PLE) schemes,we provide rigorous proof that the framework can meet perfect secrecy and correctness *** addition,we provide a specific scheme of physical layer OTP secure transmission for quadrature amplitude modulation(QAM) and phase-shift keying(PSK) symbols based on physical layer *** scheme realizes the unification of bit encryption and symbol encryption,which can adaptively select the quantization level according to the signal-to-noise ratio(SNR) to minimize the symbol error rate(SER).Further,we analyze the performance quantitatively and derive the closed-form expressions of SER,which indicates that the proposed scheme has a lower ***,simulation results verify that the proposed symbol-wise OTP secure transmission scheme can achieve perfect secrecy and high reliability.

关键词： physical layer security one-time pad wireless channel physical layer encryption perfect secrecy

Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

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Computers, Materials & Continua 2024年第5期79卷 2105-2122页

作者： Mingze Li Diwen Zheng Shuhua Lu college of information and Cyber security People’s Public Security University of ChinaBeijing102600China

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep ***,there have been stillmany challenges including crowd multi-scale variations and high network complexity,*** tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is ***,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual *** addition,a compound loss function is introduced for training themethod to improve global context information *** proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public *** accuracy is better than those of many advanced approaches,while the number of parameters is *** experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

关键词： Crowd counting Res-connection multi-branch compound loss function

Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism

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Computer Modeling in engineering & Sciences 2024年第5期139卷 1701-1731页

作者： Lanze Zhang Yijun Gu Jingjie Peng college of information and Cyber security People’s Public Security University of ChinaBeijing100038China

Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic *** GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and ***,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph *** characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned ***,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling *** the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily *** deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of ***,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks.

关键词： Heterophilic graph graph neural network graph representation learning failure of the homophily assumption

Constructing the Impossible Differential of Type-II GFN with Boolean Function and Its Application to WARP

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Chinese Journal of Electronics 2024年第1期33卷 80-89页

作者： Jiali SHI Guoqiang LIU Chao LI college of Liberal Arts and Sciences National University of Defense Technology Hunan engineering Research Center of Commercial Cryptography Theory and Technology Innovation State Key Laboratory of information security Institute of Information Engineering

Type-Ⅱ generalized Feist el network(GFN) has attracted a lot of attention for its simplicity and high *** differential attack is one of the powerful cryptanalytic approaches for word-oriented block ciphers such as Feistel-like *** deduce the impossible differential of Type-Ⅱ GFN by analyzing the Boolean function in the middle *** main idea is to investigate the expression with the variable representing the plaintext(ciphertext) difference words for the internal state *** adopting the miss-in-the-middle approach,we can construct the impossible differential of Type-Ⅱ *** an illustration,we apply this approach to WARP,a lightweight 128-bit block cipher with a 128-bit key which was presented by Banik et *** SAC *** structure of WARP is a 32-branch Type-Ⅱ ***,we find two 21-round truncated impossible differentials and implement a 32-round key recovery attack on *** the 32-round key recovery attack on WARP,some observations are used to mount an effective *** the advantage of the early abort technique,the data,time,and memory complexities are 2125.69chosen plaintexts,2126.6832-round encryptions,and 2100-bit,*** the best of our knowledge,this is the best attack on WARP in the single-key scenario.

关键词： Ciphers Boolean functions Parallel processing Complexity theory

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

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Computers, Materials & Continua 2024年第5期79卷 2253-2276页

作者： Di Wang Yuefei Zhu Jinlong Fei Maohua Guo School of Cyberspace security Information Engineering UniversityZhengzhou450000China

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity *** advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial *** a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable ***,these methods suffer from high bandwidth overhead or require access to the target model,which is *** paper proposes CMAES-WFD,a black-box WF defense based on adversarial *** process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization *** are injected into the local parts of the original traffic to control bandwidth *** to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,***,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

关键词： Traffic analysis deep neural network adversarial sample Tor website fingerprinting

HCRVD: A Vulnerability Detection System Based on CST-PDG Hierarchical Code Representation Learning

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Computers, Materials & Continua 2024年第6期79卷 4573-4601页

作者： Zhihui Song Jinchen Xu Kewei Li Zheng Shan School of Cyberspace security Information Engineering UniversityZhengzhou450001China

Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code ***,due to limitations in code representation and neural network design,the validity and practicality of the model still need to be ***,due to differences in programming languages,most methods lack cross-language detection *** address these issues,in this paper,we analyze the shortcomings of previous code representations and neural *** propose a novel hierarchical code representation that combines Concrete Syntax Trees(CST)with Program Dependence Graphs(PDG).Furthermore,we introduce a Tree-Graph-Gated-Attention(TGGA)network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection(HCRVD)*** system enables cross-language vulnerability detection at the *** experiments show that HCRVD surpasses many competitors in vulnerability detection *** benefits from the hierarchical code representation learning method,and outperforms baseline in cross-language vulnerability detection by 9.772%and 11.819%in the C/C++and Java datasets,***,HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source *** shows good validity,generality and practicality.

关键词： Vulnerability detection deep learning CST-PDG code representation tree-graph-gated-attention network cross-language

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

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Computers, Materials & Continua 2024年第4期79卷 1705-1726页

作者： Aodi Liu Na Wang Xuehui Du Dibin Shan Xiangyu Wu Wenjuan Wang He’nan Province Key Laboratory of information security Information Engineering UniversityZhengzhou450000China

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business *** BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

关键词： Big data access control data security BiLSTM

Two-Layer Attention Feature Pyramid Network for Small Object Detection

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Computer Modeling in engineering & Sciences 2024年第10期141卷 713-731页

作者： Sheng Xiang Junhao Ma Qunli Shang Xianbao Wang Defu Chen college of information engineering Zhejiang University of TechnologyHangzhou310023China Binjiang Cyberspace security Institute of ZJUT Hangzhou310056China

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian ***,small objects are difficult to detect accurately because they contain less *** current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature ***,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object *** address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different *** the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection *** extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the *** results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

关键词： Small object detection two-layer attention module small object detail enhancement module feature pyramid network

Network traffic classification:Techniques,datasets,and challenges

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Digital Communications and Networks 2024年第3期10卷 676-692页

作者： Ahmad Azab Mahmoud Khasawneh Saed Alrabaee Kim-Kwang Raymond Choo Maysa Sarsour college of information Technology and Systems Victorian Institute of TechnologyAustralia college of engineering Al Ain UniversityAbu DhabiUnited Arab Emirates information Systems and security College of ITUnited Arab Emirates UniversityAl Ain15551United Arab Emirates Department of information Systems and Cyber security University of Texas at San AntonioSan AntonioTX78260USA School of Photovoltaic and Renewable Energy engineering University of New South WalesSydneyNSW2052Australia

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior *** this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning *** also explain the implementations,advantages,and limitations associated with these *** review also extends to publicly available datasets used in the ***,we discuss existing and emerging challenges,as well as future research directions.

关键词： Network classification Machine learning Deep learning Deep packet inspection Traffic monitoring