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MMCo:using multimodal deep learning to detect malicious traffic with noisy labels

Frontiers of Computer Science 2024年第1期18卷 239-241页

作者： Qingjun YUAN gaopeng gou Yuefei ZHU Yongjuan WANG Strategic Support Force Information Engineering University Zhengzhou 450001China Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China

1 Introduction The success of a deep learning-based network intrusion detection systems(NIDS)relies on large-scale,labeled,realistic traffic[1,2].However,automated labeling of realistic traffic,such as by sandbox and rule-based approaches,is prone to errors[3],which in turn affects deep learning-based *** effective schemes for learning with noisy labels(LNL)have been proposed,among which the use of parallel networks for sample selection during training has been shown to be *** is argued that parallel networks can discriminate samples from different perspectives,thus adding additional information to the training *** centerpiece is maintaining disagreement of networks.

关键词： network networks modal

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Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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Science China(Information Sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU gaopeng gou Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

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Security and privacy of blockchain

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Blockchain(Research and Applications) 2023年第1期4卷 166-166页

作者： Meng Shen gaopeng gou Qi Xuan School of Cyberspace Science and Technology Beijing Institute of Technology100081 BeijingChina Institute of Information Engineering Chinese Academy of Sciences100085 BeijingChina Institute of Cyberspace Security Zhejiang University of Technology310023 HangzhouChina

We are glad to present this special issue named“Security and Privacy of Blockchain”of the Blockchain:Research and Applications *** papers that are directly submitted for consideration in this special issue are *** a decentralized consensus technology,blockchain has considerable potential in the development of new foundations for socio-economic systems and the decentralization of security in emerging *** to its advantages of effectively building trust between humans and machines,reducing costs,and improving resource utilization,blockchain is becoming increasingly indispensable in a diverse range of fields,such as Bitcoin,Ethereum,and ***,in the past decade,substantial advancements have been made in blockchain technology,leading to heterogeneous challenges in data security and privacy protection,such as transaction traceability,account clustering,Sybil attacks,eclipse attacks,and fraudulent smart *** attacks severely violate users’data and privacy ***,these attacks must be dealt with.

关键词： journal smart becoming

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序言:网络公害治理技术与应用

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集成技术 2024年第5期13卷 1-2页

作者：熊刚苟高鹏中国科学院信息工程研究所第五研究室中国科学院大学网络空间安全学院中国科学院中国科学院大学

以网络赌博、网络诈骗、网络暴力为代表的网络公害正严重影响国家和人民的利益。由于网络公害具有混淆对抗、动态变化、多层隐匿等特点,因此难以被有效监测和溯源。在捍卫国家网络空间主权、维护国家政治安全、营造晴朗网络空间等方面,... 详细信息

以网络赌博、网络诈骗、网络暴力为代表的网络公害正严重影响国家和人民的利益。由于网络公害具有混淆对抗、动态变化、多层隐匿等特点,因此难以被有效监测和溯源。在捍卫国家网络空间主权、维护国家政治安全、营造晴朗网络空间等方面,网络公害治理发挥着重要作用。

关键词：网络空间主权网络诈骗网络赌博网络暴力国家政治安全有效监测动态变化技术与应用

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