Application of adversarial machine learning in network intrusion detection by Qixu LIU, Junnan WANG, Jie YIN, Yanhui CHEN, Jiaxi LIU

“…In recent years, machine learning (ML) has become the mainstream network intrusion detection system(NIDS).However, the inherent vulnerabilities of machine learning make it difficult to resist adversarial attacks, which can mislead the models by adding subtle perturbations to the input sample.Adversarial machine learning (AML) has been extensively studied in image recognition.In the field of intrusion detection, which is inherently highly antagonistic, it may directly make ML-based detectors unavailable and cause significant property damage.To deal with such threats, the latest work of applying AML technology was systematically investigated in NIDS from two perspectives: attack and defense.First, the unique constraints and challenges were revealed when applying AML technology in the NIDS field; secondly, a multi-dimensional taxonomy was proposed according to the adversarial attack stage, and current work was compared and summarized on this basis; finally, the future research directions was discussed.…”
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Effects of feature selection and normalization on network intrusion detection by Mubarak Albarka Umar, Zhanfang Chen, Khaled Shuaib, Yan Liu

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FWA-SVM Network Intrusion Identification Technology for Network Security by Yaohui Zhang

“…In the digital age, the increasing demand for network security has driven research on efficient network intrusion detection systems. The effectiveness of traditional network intrusion is limited in the face of complex network attacks and constantly increasing data volume. …”
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Semi-supervised tri-Adaboost algorithm for network intrusion detection by Yali Yuan, Liuwei Huo, Yachao Yuan, Zhixiao Wang

“…Network intrusion detection is a relatively mature research topic, but one that remains challenging particular as technologies and threat landscape evolve. …”
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A network intrusion detection method designed for few-shot scenarios by Weichen HU, Congyuan XU, Yong ZHAN, Guanghui CHEN, Siqing LIU, Zhiqiang WANG, Xiaolin WANG

“…Existing intrusion detection techniques often require numerous malicious samples for model training.However, in real-world scenarios, only a small number of intrusion traffic samples can be obtained, which belong to few-shot scenarios.To address this challenge, a network intrusion detection method designed for few-shot scenarios was proposed.The method comprised two main parts: a packet sampling module and a meta-learning module.The packet sampling module was used for filtering, segmenting, and recombining raw network data, while the meta-learning module was used for feature extraction and result classification.Experimental results based on three few-shot datasets constructed from real network traffic data sources show that the method exhibits good applicability and fast convergence and effectively reduces the occurrence of outliers.In the case of 10 training samples, the maximum achievable detection rate is 99.29%, while the accuracy rate can reach a maximum of 97.93%.These findings demonstrate a noticeable improvement of 0.12% and 0.37% respectively, in comparison to existing algorithms.…”
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CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network by Guojie Liu, Jianbiao Zhang

“…Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. …”
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Personalized lightweight distributed network intrusion detection system in fog computing by Tianpeng YE, Xiang LIN, Jianhua LI, Xuankai ZHANG, Liwen XU

“…With the continuous development of Internet of Things (IoT) technology, there is a constant emergency of new IoT applications with low latency, high dynamics, and large bandwidth requirements.This has led to the widespread aggregation of massive devices and information at the network edge, promoting the emergence and deep development of fog computing architecture.However, with the widespread and in-depth application of fog computing architecture, the distributed network security architecture deployed to ensure its security is facing critical challenges brought by fog computing itself, such as the limitations of fog computing node computing and network communication resources, and the high dynamics of fog computing applications, which limit the edge deployment of complex network intrusion detection algorithms.To effectively solve the above problems, a personalized lightweight distributed network intrusion detection system (PLD-NIDS) was proposed based on the fog computing architecture.A large-scale complex network flow intrusion detection model was trained based on the convolutional neural network architecture, and furthermore the network traffic type distribution of each fog computing node was collected.The personalized model distillation algorithm and the weighted first-order Taylor approximation pruning algorithm were proposed to quickly compress the complex model, breaking through the limitation of traditional model compression algorithms that can only provide single compressed models for edge node deployment due to the high compression calculation overhead when facing a large number of personalized nodes.According to experimental results, the proposed PLD-NIDS architecture can achieve fast personalized compression of edge intrusion detection models.Compared with traditional model pruning algorithms, the proposed architecture achieves a good balance between computational loss and model accuracy.In terms of model accuracy, the proposed weighted first-order Taylor approximation pruning algorithm can achieve about 4% model compression ratio improvement under the same 0.2% model accuracy loss condition compared with the traditional first-order Taylor approximation pruning algorithm.…”
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Research on distributed network intrusion detection system for IoT based on honeyfarm by Hao WU, Jiajia HAO, Yunlong LU

“…To solve the problems that the network intrusion detection system in the Internet of things couldn’t identify new attacks and has limited flexibility, a network intrusion detection system based on honeyfarm was proposed, which could effectively identify abnormal traffic and have continuous learning ability.Firstly, considering the characteristics of the convolutional block attention module, an abnormal traffic detection model was developed, focusing on both channel and spatial dimensions, to enhance the model’s recognition abilities.Secondly, a model training scheme utilizing federated learning was employed to enhance the model’s generalization capabilities.Finally, the abnormal traffic detection model at the edge nodes was continuously updated and iterated based on the honeyfarm, so as to improve the system’s accuracy in recognizing new attack traffic.The experimental results demonstrate that the proposed system not only effectively detects abnormal behavior in network traffic, but also continually enhances performance in detecting abnormal traffic.…”
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An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection by zahra asgari varzaneh, soodeh hosseini

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Network intrusion intention analysis model based on Bayesian attack graph by Zhiyong LUO, Xu YANG, Jiahui LIU, Rui XU

“…Aiming at the problem of ignoring the impact of attack cost and intrusion intention on network security in the current network risk assessment model,in order to accurately assess the target network risk,a method of network intrusion intention analysis based on Bayesian attack graph was proposed.Based on the atomic attack probability calculated by vulnerability value,attack cost and attack benefit,the static risk assessment model was established in combination with the quantitative attack graph of Bayesian belief network,and the dynamic update model of intrusion intention was used to realize the dynamic assessment of network risk,which provided the basis for the dynamic defense measures of attack surface.Experiments show that the model is not only effective in evaluating the overall security of the network,but also feasible in predicting attack paths.…”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

Subjects: “…Network intrusion detection…”
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A Profile Based Network Intrusion Detection and Prevention System for Securing Cloud Environment by Sanchika Gupta, Padam Kumar, Ajith Abraham

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Broadcasting Bidirectional Access Network Intrusion Detection System Facing Tri-Networks Integration by Han Qiu, Julong Lan, Yangfeng Ou, Chuanhao Zhang, Yanhong Wang

“…In view of this situation，a network intrusion detection system for the border safety was proposed and an example product called SunGnet703 was given. …”
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Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature by Taotao LIU, Yu FU, Kun WANG, Xueyuan DUAN

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Using WPCA and EWMA Control Chart to Construct a Network Intrusion Detection Model by Ying-Ti Tsai, Chung-Ho Wang, Yung-Chia Chang, Lee-Ing Tong

“…Artificial intelligence algorithms and big data analysis methods are commonly employed in network intrusion detection systems. However, challenges such as unbalanced data and unknown network intrusion modes can influence the effectiveness of these methods. …”
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EM-AUC: A Novel Algorithm for Evaluating Anomaly Based Network Intrusion Detection Systems by Kevin Z. Bai, John M. Fossaceca

Subjects: “…network intrusion detection…”
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Elevated few-shot network intrusion detection via self-attention mechanisms and iterative refinement by Congyuan Xu, Yong Zhan, Guanghui Chen, Zhiqiang Wang, Siqing Liu, Weichen Hu

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A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE by Vanlalruata Hnamte, Hong Nhung-Nguyen, Jamal Hussain, Yong Hwa-Kim

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Elevated few-shot network intrusion detection via self-attention mechanisms and iterative refinement. by Congyuan Xu, Yong Zhan, Guanghui Chen, Zhiqiang Wang, Siqing Liu, Weichen Hu

“…The network intrusion detection system (NIDS) plays a critical role in maintaining network security. …”
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Network intrusion detection method based on improved FCM and rule parameter optimization in cloud environment by Chunqin ZANG, Lichun XIE

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