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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ResInceptNet-SA: A Network Traffic Intrusion Detection Model Fusing Feature Selection and Balanced Datasets by Guorui Liu, Tianlin Zhang, Hualin Dai, Xinyang Cheng, Daoxuan Yang

Subjects: “…network intrusion detection…”
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Overview of anomaly detection techniques for industrial Internet of things by Haili SUN, Xiang LONG, Lansheng HAN, Yan HUANG, Qingbo LI

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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

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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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FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things by Tayyab Rehman, Noshina Tariq, Farrukh Aslam Khan, Shafqat Ur Rehman

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GHSOM intrusion detection based on Dempster-Shafer theory by Jie SU, Wei-wei DONG, Xuan XU, Shuai LIU, Li-peng XIE

“…On the basis of incremental GHSOM,the GHSOM neural network intrusion detection based on the theory of evidence reasoning method was put forward.It can deal with the uncertainty caused by randomness and fuzziness,as well as can constantly narrowing assumptions set by accumulate the evidence,effectively control dynamic growth of network and keep a good accuracy in noise environment.Experiments show that GHSOM intrusion detection method based on the Dempster Shafer theory realized the dynamic control for the scale of expended subnet during the process of detection.It has the better detection accuracy in the noise environment and improves the adaptability and extensibility of incremental GHSOM neural network intrusion detection method when the scale of network is expanded.…”
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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

“…Abstract Most of the data, which is in the field of network intrusion detection, have the characteristics of a mixture of high‐dimensional datasets of continuous and categorical variables. …”
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AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM by Trần Thị Hương, Phạm Văn Hạnh

“…Network intrusion detection is one of the most important issues of network security and is a research interest of many researchers. …”
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Research on intrusion detection algorithm in wireless network based on Bayes game model by CHEN Hang1, TAO Jun1

“…Bayes game theory was used to research parameter adjustment problems in wireless network intrusion detec-tion.Intrusion detection game model was designed,time interval adjust algorithm TSMA-BG and parameter correction algorithm DPMA were also designed according to the perfect equilibrium in game modeling.Simulation results show that intrusion detection systems can effectively use these algorithms to detect intrusion behavior that have been changed.…”
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A Network Traffic Classification Method for Class-Imbalanced Data by Xiaohui Guan, Yaguan Qian

“…It wi11 lead to a 1ow classification accuracy in network intrusion detection. For overcoming this class imbalance phenomenon，a pipelining ensemble approach in different feature spaces was proposed，which translates multi-class classification to two-class classification. …”
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Research on intrusion detection based on network events and deep protocol analysis by ZHU Ying-ying, WU Jin-feng, MING Zhong

“…The problems for restricting NIDS were investigated.Based on network events and deep protocol analysis,a new model MIDM analyzing and integrating network intrusion was proposed.After extending ABNF to describe network events,a new NIDS was built based on MIDM.Experimental results proved that,comparing to the current mainstream NIDS,the model MIDM can work effectively with less false positive rate and less redundancy of rule base.And if net-work stream and rule base were extended quickly,the CPU utilization of new model’s would remain low growth,which makes MIDM better adapt to high-speed network.And it’s also able to detect some unknown attacks and sustain rule gen-eralization.…”
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Research on intrusion detection for maritime meteorological sensor network based on balancing generative adversarial network by Xin SUN, Guifu ZHANG, Hongyan XING, Wang Zenghui

“…Aiming at the problem that the resources of maritime mobile terminals were limited and the network traffic was imbalanced in the MMSN (maritime meteorological sensor network) environment, which made it difficult to detect network intrusion accurately, a mobile edge computing based physical architecture of MMSN was proposed, and an intrusion detection model based on balancing generative adversarial network was proposed.First, an advanced balancing generative adversarial network was adopted to augment the imbalanced data.Then, a lightweight network based on group convolution was applied to intrusion data classification.Finally, compared with conventional data augmentation models, the computer simulation proves that the proposed model has a higher ability to recognize various attacks, especially minority class attacks on MMSN.…”
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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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Research on backup and remapping of network slice based on security classification by Zhiyong SUN, Xinsheng JI, Wei YOU, Quan YUAN

“…In the future virtual environment of 5G core network,the general X86 servers make the attackers exploit vulnerabilities more easily,the substrate network is infected with and spreads the virus more easily,and the problem of single physical node failed will affect the service performance of the network slice seriously.Based on the existing node backup and remapping solutions,considering the impact of security constraints among nodes on network security performance,a security parameter evaluation model of virtual nodes and physical nodes were proposed in the network slicing,and the security constraint relationship was established between the virtual nodes and the physical nodes.Then backup virtual nodes were selected based on the security parameters evaluation model,and backup mapping methods were designed.Finally,the node remapping mechanism was designed with satisfying the requirement of network delay.Experiments show that the proposed method can significantly improve network intrusion tolerance with satisfying the requirements of network slicing service performance.…”
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An intrusion detection method based on depthwise separable convolution and attention mechanism by Zhifei ZHANG, Feng LIU, Yiyang GE, Shuo LI, Yu ZHANG, Ke XIONG

“…In order to improve the accuracy of multi-classification in network intrusion detection, an intrusion detection method was proposed based on depthwise separable convolution and attention mechanism.By constructing a cascade structure combining depthwise separable convolution and long-term and short-term memory networks, the spatial and temporal features of network traffic data can be better extracted.A mixed-domain attention mechanism was introduced to enhance the detection performance.To solve the problem of low detection rate in some samples, a data balance strategy based on the combination of the variational auto-encoder (VAE) the generative adversarial network (GAN) and was designed, which can effectively cope with imbalanced datasets and improve the adaptability of the proposed detection method.The experimental results show that the proposed method is able to achieve 99.80%, 99.32%, and 83.87% accuracy on the CICIDS-2017, NSL-KDD and UNSW-NB15 datasets, which is improved by 0.6%, 0.5%, and 2.3%, respectively.…”
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SVM Intrusion Detection Model Based on Compressed Sampling by Shanxiong Chen, Maoling Peng, Hailing Xiong, Xianping Yu

“…Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. …”
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Intrusion detection method for IoT in heterogeneous environment by LIU Jing, MU Zelin, LAI Yingxu

“…Ultimately, experimental results derived from the network intrusion dataset BoT-IoT substantiate that, relative to existing methods, the proposed method notably curtails the time expenditure of resource-constrained clients, and improves processing speed by 20.82%, while enhancing the accuracy of intrusion detection by 0.86% in Non-IID conditions.…”
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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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Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT by Sepehr Sharifi, Soulmaz Gheisari

“…Like any computer network, it faces its own challenges and problems, one of which is the issue of network intrusion and disruption. This dissertation focuses on detecting anomaly-based intrusion into the Internet of Things using data mining. …”
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