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ResNeSt-biGRU: An Intrusion Detection Model Based on internet of Things

Computers, Materials & Continua 2024年第4期79卷 1005-1023页

作者： Yan Xiang Daofeng Li Xinyi Meng Chengfeng Dong Guanglin Qin School of Computer and Electronics Information Guangxi UniversityNanning530004China Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing Guangxi UniversityNanning530004China

The rapid expansion of internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

关键词： internet of Things cyberattack intrusion detection internet security

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Comprehensive DDoS Attack Classification Using Machine LearningAlgorithms

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Computers, Materials & Continua 2022年第10期73卷 577-594页

作者： Olga Ussatova Aidana Zhumabekova Yenlik Begimbayeva Eric T.Matson Nikita Ussatov Al-Farabi Kazakh National University Almaty050040Kazakhstan Institute of Information and Computational Technologies Almaty050010Kazakhstan Satbayev University Almaty050013Kazakhstan Purdue University West Lafayette47907INUSA Turan University Almaty050013Kazakhstan

The fast development of internet technologies ignited the growthof techniques for information security that protect data, networks, systems,and applications from various threats. There are many types of threats. Thededicated denial of service attack (DDoS) is one of the most serious andwidespread attacks on internet resources. This attack is intended to paralyzethe victim’s system and cause the service to fail. This work is devoted tothe classification of DDoS attacks in the special network environment calledSoftware-Defined Networking (SDN) using machine learning algorithms. Theanalyzed dataset included instances of two classes: benign and malicious.As the dataset contained twenty-two features, the feature selection techniques were required for dimensionality reduction. In these experiments, theInformation gain, the Chi-square, and the F-test were applied to decreasethe number of features to ten. The classes were also not completely balanced, so undersampling, oversampling, and synthetic minority oversampling(SMOTE) techniques were used to balance classes equally. The previousresearch works observed the classification of DDoS attacks applying variousfeature selection techniques and one or more machine learning algorithms.Still, they did not pay much attention to classifying the combinations offeature selection and balancing methods with different machine learningalgorithms. This work is devoted to the classification of datasets with eightmachine learning algorithms: naïve Bayes, logistic regression, support vectormachine, k-nearest neighbors, decision tree, random forest, XGBoost, andCatBoost. In the experimental results, the Information gain and F-test featureselection methods achieved better performance with all eight ML algorithmsthan with the Chi-square technique. Furthermore, the accuracy values of theoversampled and SMOTE datasets were higher than that of the undersampledand imbalanced datasets. Among machine learning algorithms, the accuracyof support vector machine, logistic regression, and naïve Bayes f

关键词： internet security networks systems DDoS software-defined networking feature selection class balancing machine learning XGBoost CatBoost

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Preventing IP Source Address Spoofing: A Two-Level, State Machine-Based Method

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Tsinghua Science and Technology 2009年第4期14卷 413-422页

作者：毕军刘冰洋吴建平沈燕 Tsinghua National Laboratory for Information Science and Technology Network Research Center Tsinghua University China Education and Research Network (CERNET)

A signature-and-verification-based method, automatic peer-to-peer anti-spoofing （APPA）, is pro- posed to prevent IP source address spoofing. In this method, signatures are tagged into the packets at the source peer, and verified and removed at the verification peer where packets with incorrect signatures are filtered. A unique state machine, which is used to generate signatures, is associated with each ordered pair of APPA peers. As the state machine automatically transits, the signature changes accordingly. KISS ran- dom number generator is used as the signature generating algorithm, which makes the state machine very small and fast and requires very low management costs. APPA has an intra-AS （autonomous system） level and an inter-AS level. In the intra-AS level, signatures are tagged into each departing packet at the host and verified at the gateway to achieve finer-grained anti-spoofing than ingress filtering. In the inter-AS level, signatures are tagged at the source AS border router and verified at the destination AS border router to achieve prefix-level anti-spoofing, and the automatic state machine enables the peers to change signatures without negotiation which makes APPA attack-resilient compared with the spoofing prevention method. The results show that the two levels are both incentive for deployment, and they make APPA an integrated anti-spoofing solution.

关键词： source address spoofing spoofing prevention internet security

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Application of VPN in e-Business

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Journal of Measurement Science and Instrumentation 2012年第3期3卷 248-250页

作者：刘爽英李博 College of Electronics and Computer Science and Technology North University of China

With the development of internet,e-Business has gradually become a new model for business activity,however,the security of e-Business is a major bottleneck restricting the development of e-Business.Network with virtual private network(VPN)can reduce network costs and communication costs,increase flexibility and provide safe and smooth network for the corporations that do e-Business across regions.This article introduces the definition and the technological core of VPN,and researches and analyzes the VPN application in e-commerce.

关键词： e-business internet security virtual private network(VPN) tunnel

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后棱镜时代的国际网络治理

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国际法学 2014年第11期

作者：崔聪聪

美国已建立起一整套涵盖网络空间战略、法律、军事和技术保障的网络防控体系。美国对ICANN的监管权的移交不会导致其丧失对互联网的实际控制权。自上而下的方法不是互联网国际治理的最优解决方案，互联网治理需要利益相关方的共同参与... 详细信息

美国已建立起一整套涵盖网络空间战略、法律、军事和技术保障的网络防控体系。美国对ICANN的监管权的移交不会导致其丧失对互联网的实际控制权。自上而下的方法不是互联网国际治理的最优解决方案，互联网治理需要利益相关方的共同参与。美国提出的“多利益相关方模式”是其单边主义以及先发制人战略在网络空间的延伸，是对主权国家信息主权的不当溶蚀。为建立和平与公正的网络空间国际秩序，应设立网络安全与发展委员会，并将其纳入到联合国框架。互联网的国际治理应遵循主权平等原则和相对安全观，并充分发挥政府的主导作用。

关键词：网络安全信息主权网络空间国际秩序相对安全观国际网络治理 internet security information sovereignty international cyberspace order relative security outlook international network governance

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