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C2Net-YOLOv5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm

Computers, Materials & Continua 2023年第11期77卷 1949-1965页

作者： Xiujuan Wang Yiqi Tian kangfeng zheng Chutong Liu Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100048China Fan Gongxiu Honors College Beijing University of TechnologyBeijing100124China

Rapid advancement of intelligent transportation systems(ITS)and autonomous driving(AD)have shown the importance of accurate and efficient detection of traffic signs.However,certain drawbacks,such as balancing accuracy and real-time performance,hinder the deployment of traffic sign detection algorithms in ITS and AD domains.In this study,a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed.An enhanced backbone network module,called C2Net,which uses an upgraded bidirectional Res2Net,was introduced to mitigate information loss in the feature extraction process and to achieve information complementarity.Furthermore,a squeeze-and-excitation attention mechanism was incorporated within the channel attention of the architecture to perform channel-level feature correction on the input feature map,which effectively retains valuable features while removing non-essential features.A series of ablation experiments were conducted to validate the efficacy of the proposed methodology.The performance was evaluated using two distinct datasets:the Tsinghua-Tencent 100K and the CSUST Chinese traffic sign detection benchmark 2021.On the TT100K dataset,the method achieves precision,recall,and Map0.5 scores of 83.3%,79.3%,and 84.2%,respectively.Similarly,on the CCTSDB 2021 dataset,the method achieves precision,recall,and Map0.5 scores of 91.49%,73.79%,and 81.03%,respectively.Experimental results revealed that the proposed method had superior performance compared to conventional models,which includes the faster region-based convolutional neural network,single shot multibox detector,and you only look once version 5.

关键词： Target detection traffic sign detection autonomous driving YOLOv5

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Social Engineering Attack-Defense Strategies Based on Reinforcement Learning

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Computer Systems Science & Engineering 2023年第11期47卷 2153-2170页

作者： Rundong Yang kangfeng zheng Xiujuan Wang Bin Wu Chunhua Wu School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science Beijing University of TechnologyBeijing100124China

Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity,as human vulnerabilities are often the weakest link in the entire network.Such vulnerabilities are becoming increasingly susceptible to network security risks.Addressing the social engineering attack defense problem has been the focus of many studies.However,two main challenges hinder its successful resolution.Firstly,the vulnerabilities in social engineering attacks are unique due to multistage attacks,leading to incorrect social engineering defense strategies.Secondly,social engineering attacks are real-time,and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions.This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning(IQL)reinforcement learning method on human vulnerability attributes.The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy.Furthermore,the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks.The experimental results demonstrate that the proposed algorithm outperforms the traditional Qlearning(QL)and deep Q-network(DQN)approaches in terms of time efficiency,taking 9.1%and 19.4%less time,respectively.Moreover,the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes.This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain.

关键词： Social engineering game theory reinforcement learning Q-learning

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Intrusion Detection Algorithm Based on Density,Cluster Centers,and Nearest Neighbors

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China Communications 2016年第7期13卷 24-31页

作者： Xiujuan Wang Chenxi Zhang kangfeng zheng Computer Sciences Beijing University of Technology Computer Science And Technology Beijing University of Posts and Telecommunications

Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.

关键词：聚类中心基于密度入侵行为测算法入侵检测方法最近邻混合学习分类器

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Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

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Computers, Materials & Continua 2020年第10期65卷 617-638页

作者： Xiujuan Wang Qianqian zheng kangfeng zheng Yi Sui Jiayue Zhang Information Technology Institute Beijing University of TechnologyBeijing100124China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China

Malicious social robots are the disseminators of malicious information on social networks,which seriously affect information security and network environments.Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks.Supervised classification based on manual feature extraction has been widely used in social robot detection.However,these methods not only involve the privacy of users but also ignore hidden feature information,especially the graph feature,and the label utilization rate of semi-supervised algorithms is low.Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection methods,in this paper a robot detection scheme based on weighted network topology is proposed,which introduces an improved network representation learning algorithm to extract the local structure features of the network,and combined with the graph convolution network(GCN)algorithm based on the graph filter,to obtain the global structure features of the network.An end-to-end semi-supervised combination model(Semi-GSGCN)is established to detect malicious social robots.Experiments on a social network dataset(cresci-rtbust-2019)show that the proposed method has high versatility and effectiveness in detecting social robots.In addition,this method has a stronger insight into robots in social networks than other methods.

关键词： Social networks social robot detection network representation learning graph convolution network

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Strategy Selection for Moving Target Defense in Incomplete Information Game

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Computers, Materials & Continua 2020年第2期62卷 763-786页

作者： Huan Zhang kangfeng zheng Xiujuan Wang Shoushan Luo Bin Wu School of CyberSpace Security Beijing University of Posts and TelecommunicationsBeijing100088China College of Computer Sciences Beijing University of TechnologyBeijing100124China

As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attackers.Although the moving target defense(MTD)has been proposed to increase the attack difficulty for the attackers,there is no solo approach can cope with different attacks;in addition,it is impossible to implement all these approaches simultaneously due to the resource limitation.Thus,the selection of an optimal defense strategy based on MTD has become the focus of research.In general,the confrontation of two players in the security domain is viewed as a stochastic game,and the reward matrices are known to both players.However,in a real security confrontation,this scenario represents an incomplete information game.Each player can only observe the actions performed by the opponent,and the observed actions are not completely accurate.To accurately describe the attacker’s reward function to reach the Nash equilibrium,this work simulated and updated the strategy selection distribution of the attacker by observing and investigating the strategy selection history of the attacker.Next,the possible rewards of the attacker in each confrontation via the observation matrix were corrected.On this basis,the Nash-Q learning algorithm with reward quantification was proposed to select the optimal strategy.Moreover,the performances of the Minimax-Q learning algorithm and Naive-Q learning algorithm were compared and analyzed in the MTD environment.Finally,the experimental results showed that the strategy selection algorithm can enable defenders to select a more reasonable defensive strategy and achieve the maximum possible reward.

关键词： Moving target defense Nash-Q learning algorithm optimal strategy selection incomplete information game web service

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A New Network Coding Mechanism Balancing Coding Opportunities, Energy and QoS in WSNs

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China Communications 2014年第6期11卷 108-118页

作者： zheng kangfeng WANG Xiujuan ZHA Xuan XIAO Huan School of Computer Science Beijing University of Posts and Telecommunications School of Computer Science Beijing University of Technology

A new Network Coding mechanism in WSNs(Wireless Sensor Networks), which is named COEQ and aims at balancing coding opportunities, energy and QoS, is proposed and analyzed. Implemented on the basis of traditional AODV protocol, COEQ evaluates several metrics of paths comprehensively with TOPSIS method including minimum remaining energy, coding opportunities, QoS and so on, so as to select the optimal transmitting route. Experiments on NS[2] show that COEQ can improve throughput and save energy when the transmitting rate is low.

关键词：无线传感器网络 QoS机制编码机制节省能源平衡 TOPSIS法 AODV协议传输路径

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Smartphone Malware Detection Model Based on Artificial Immune System

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China Communications 2014年第A1期11卷 86-92页

作者： WU Bin LU Tianliang zheng kangfeng ZHANG Dongmei LIN Xing InformationSecurityLaboratory NationalDisasterRecoveryTechnologyEngineeringLaboratoryBeijingUniversityofPostsandTelecommunicationsBeijing100876P.R.China People'sPublicSecurityUniversityofChina Beijing100038P.R.China

In order to solve the problem that the traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model (SP-MDM) based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

关键词：人工免疫系统恶意软件检测模型智能手机克隆选择算法检测技术软件分析检测性能

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建立健全行政事业单位国有资产管理绩效评价制度:进展与建议

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财政科学 2023年第11期95卷 147-154页

作者：郑德琳刘继东王康丰广东省财政科学研究所广东省财政厅资产管理处

行政事业单位国有资产是政府履行公共职能、提供公共产品和服务的重要物质基础,在经济下行、财政减收的背景下,管好用好这部分国有资产显得尤为迫切。当前,一些地区仍然存在底数不清晰、制度不完善、执行不到位、与预算衔接不充分等困... 详细信息

行政事业单位国有资产是政府履行公共职能、提供公共产品和服务的重要物质基础,在经济下行、财政减收的背景下,管好用好这部分国有资产显得尤为迫切。当前,一些地区仍然存在底数不清晰、制度不完善、执行不到位、与预算衔接不充分等困难和问题,因此有必要建立健全行政事业单位国有资产管理绩效评价制度。近年来,中央和部分省份的实践做法为相关工作的开展提供了有益经验。为此,各地应结合本地实际,加快建立健全行政事业性国有资产管理绩效评价制度,规范制定定性指标,科学设置定量指标,逐步完善配套机制,以评促效,以评促管,有效推动国有资产管理效率的提升。

关键词：行政事业单位国有资产绩效评价

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网络空间安全中的人格研究综述

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电子与信息学报 2020年第12期42卷 2827-2840页

作者：吴桐郑康锋伍淳华王秀娟郑赫慈北京邮电大学网络空间安全学院北京100876 北京工业大学信息学部北京100124

网络空间是所有信息系统的集合,是人类赖以生存的信息环境。网络空间安全已经从物理域、信息域安全扩展到以人为中心的认知域和社会域安全,对人的安全研究已经成为了网络空间安全的必然趋势。人是复杂多变的综合体,而人格作为人的稳定... 详细信息

网络空间是所有信息系统的集合,是人类赖以生存的信息环境。网络空间安全已经从物理域、信息域安全扩展到以人为中心的认知域和社会域安全,对人的安全研究已经成为了网络空间安全的必然趋势。人是复杂多变的综合体,而人格作为人的稳定的心理特征,成为了人的安全相关研究的切入点。该文梳理当前网络空间安全中的人格研究,介绍了网络空间安全和人格的基本概念,提出了网络空间安全中人格的研究框架,涵盖理论研究、技术研究和技术应用3个层次;其中技术研究主要包括人格测量、人格的安全脆弱性和针对人的安全防护3部分;详细探讨了当前人格安全研究现状和存在的问题,最后讨论了未来的研究方向和发展趋势。

关键词：人格网络空间安全人格测量人格脆弱性安全防护

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利用希尔伯特黄变换检测DDoS攻击(英文)

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China Communications 2011年第2期8卷 126-133页

作者：郑康锋王秀娟杨义先郭世泽 Key Laboratory of Network and Information Attack & Defense Technology of MOE Beijing University of Posts and Telecommunications College of Computer Sciences Beijing University of Technology

DDoS detection has been the research focus in the field of information security. Existing detecting methods such as Hurst parameter method and Markov model must ensure that the network traffic signal f(t) is a stationary signal. But its stability is just a regular assumption and has no strict mathematical proof. Therefore methods mentioned above lack of reliable theoretical support. This article introduces Hilbert-HuangTtransformation(HHT) . HHT does not need to be based on signal stability,but it monitors the similarity between Hilbert marginal spectrums of adjacent observation sequences so as to realize DDoS detection. The method is experimented on DARPA 1999 data and simulating data respectively. Experimental results show that the method behaves better than existing Hurst parameter method in distinguishing both the normal and the attacked traffic.

关键词： HHT similarity DDoS detection marginal hilbert spectrum

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