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| 1 | Intrusion Detection Algorithm Based on Density,Cluster Centers,and Nearest Neighbors显示文摘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. | Xiujuan Wang Chenxi Zhang Kangfeng Zheng | 2016 | China Communications2016，13，7： | 5 |
| 2 | A New Network Coding Mechanism Balancing Coding Opportunities, Energy and QoS in WSNs显示文摘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. | ZHENG Kangfeng WANG Xiujuan ZHA Xuan XIAO Huan | 2014 | China Communications2014，11，6： | 3 |
| 3 | Smartphone Malware Detection Model Based on Artificial Immune System显示文摘 | WU Bin LU Tianliang ZHENG Kangfeng ZHANG Dongmei LIN Xing | 2014 | China Communications2014，11，A01： | 1 |
| 4 | Inertial and big drop fall algorithm显示文摘 | Wang Xiujuan Zheng Kangfeng Guo Jun | 2006 | Intemational Journal of Information Technology2006，12，4： | 1 |
| 5 | C2Net-YOLOv5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm显示文摘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. | Xiujuan Wang Yiqi Tian Kangfeng Zheng Chutong Liu | 2023 | Computers, Materials & Continua2023，77，11： | 0 |
| 6 | Strategy Selection for Moving Target Defense in Incomplete Information Game显示文摘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. | Huan Zhang Kangfeng Zheng Xiujuan Wang Shoushan Luo Bin Wu | 2020 | Computers, Materials & Continua2020，，2： | 0 |
| 7 | Social Engineering Attack-Defense Strategies Based on Reinforcement Learning显示文摘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. | Rundong Yang Kangfeng Zheng Xiujuan Wang Bin Wu Chunhua Wu | 2023 | Computer Systems Science & Engineering2023，47，11： | 0 |
| 8 | Semi-GSGCN: Social Robot Detection Research with Graph Neural Network显示文摘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. | Xiujuan Wang Qianqian Zheng Kangfeng Zheng Yi Sui Jiayue Zhang | 2020 | Computers, Materials & Continua2020，，10： | 0 |
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