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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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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EM-AUC: A Novel Algorithm for Evaluating Anomaly Based Network Intrusion Detection Systems by Kevin Z. Bai, John M. Fossaceca

“…To the best of our knowledge, this is the first time AUC-ROC and AUC-PR, derived without labels, have been used to evaluate network intrusion detection systems. The EM-AUC algorithm enables model training, testing, and performance evaluation to proceed without comprehensive labels, offering a cost-effective and scalable solution for selecting the most effective models for network intrusion detection.…”
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Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers by Alon Kukliansky, Marko Orescanin, Chad Bollmann, Theodore Huffmire

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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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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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PCA mix‐based Hotelling's T2 multivariate control charts for intrusion detection system by Mo Shaohui, Gulanbaier Tuerhong, Mairidan Wushouer, Tuergen Yibulayin

“…Hotelling's T2 multivariate control charts based on Principal Component Analysis mix (PCA mix) with bootstrap control limit were proposed, and applied to the network intrusion detection system. It was compared with the conventional Hotelling's T2 control chart based on PCA and the performance of the control limits obtained with the bootstrap method was compared to the ones calculated using the most commonly used kernel density estimation. …”
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Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks by Selvakumar B, Sivaanandh M, Muneeswaran K, Lakshmanan B

“…A novel ensemble of deep learning technique is proposed to enhance the efficiency of Packet Flow Classification in Network Intrusion Detection System (NIDS). The proposed work consists of three phases: (i) Feature Augmented Convolutional Neural Network (FA-CNN) (ii) Deep Autoencoder (iii) Ensemble of FA-CNN and Deep Autoencoder. …”
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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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Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine by Jialin WANG, Jiqiang LIU, Di ZHAO, Yingdi WANG, Yingxiao XIANG, Tong CHEN, Endong TONG, Wenjia NIU

“…Network intrusion detection system plays an important role in protecting network security.With the continuous development of science and technology,the current intrusion technology cannot cope with the modern complex and volatile network abnormal traffic,without taking into account the scalability,sustainability and training time of the detection technology.Aiming at these problems,a new deep learning method was proposed,which used unsupervised non-symmetric convolutional auto-encoder to learn the characteristics of the data.In addition,a new method based on the combination of non-symmetric convolutional auto-encoder and multi-class support vector machine was proposed.Experiments on the data set of KDD99 show that the method achieves good results,significantly reduces training time compared with other methods,and further improves the network intrusion detection technology.…”
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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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Identifying the Origin of Cyber Attacks Using Machine Learning and Network Traffic Analysis by Du Tianqing

“…In this paper, PCAP refers to Packet Capture, Network Intrusion Detection Systems refers to NIDS, Artificial Intelligence refers to AI, machine learning refers to ML, Computer Vision refers to CV, and Natural Language Processing refers to NLP. …”
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A Comparative Analysis of Support Vector Machine and K-Nearest Neighbors Models for Network Attack Traffic Detection by Han Zhuoxi

“…Moreover, the research highlights future directions to strengthen the resilience and precision of network intrusion detection systems, ensuring the development of more effective defenses against the ever-evolving landscape of cybersecurity risks.…”
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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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Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking Model by Godfrey A. Mills, Daniel K. Acquah, Robert A. Sowah

“…Network intrusion detection systems play a critical role in protecting a variety of services ranging from economic through social to commerce. …”
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A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security by Kashi Sai Prasad, P Udayakumar, E. Laxmi Lydia, Mohammed Altaf Ahmed, Mohamad Khairi Ishak, Faten Khalid Karim, Samih M. Mostafa

“…Numerous research works were keen to project intelligent network intrusion detection systems (NIDS) to avert the exploitation of IoT data through smart applications. …”
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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

“…Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. …”
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