A weighted intrusion detection model of dynamic selection

Feng, Tao; Dou, Manfang

doi:10.1007/s10489-020-02090-8

Cited by 6 publications

(9 citation statements)

References 40 publications

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“…We denote X, Y as the two discrete random variables, the joint probability mass function p (x, y) and marginal probability p(x), p(y), and the antibody information entropy of X is defined as (Feng & Dou, 2021):…”

Section: The Antibody Information Entropy H (X)mentioning

confidence: 99%

“…The cooperative evolution mechanism of multiple operators includes antibody similarity evaluation operator, antibody fitness evaluation operator and population variation adjustment operator to determine the current populationism of multiple operators includes anti (Feng & Dou, 2021;Xi et al, 2021).…”

Section: The Cooperative Evolution Mechanism Of Multiple Operatorsmentioning

confidence: 99%

See 1 more Smart Citation

An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

Zhang

Hao

2022

International Journal on Semantic Web and Information Systems

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The intrusion detection system (IDS) has lower speed, less adaptability and lower detection accuracy especially for small samples sets. This paper presents a detection model based on normalized mutual antibodies information feature selection and adaptive quantum artificial immune with cooperative evolution of multiple operators (NMAIFS MOP-AQAI). First, for a high intrusion speed, the NMAIFS is used to achieve an effective reduction for high-dimensional features. Then, the best feature vectors are sent to the MOP-AQAI classifier, in which, vaccination strategy, the quantum computing, and cooperative evolution of multiple operators are adopted to generate excellent detectors. Lastly, the data is fed into NMAIFS MOP-AQAI and ultimately generates accurate detection results. The experimental results on real abnormal data demonstrate that the NMAIFS MOP-AQAI has higher detection accuracy, lower false negative rate and a higher adaptive performance than the existing anomaly detection methods, especially for small samples sets.

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Section: The Antibody Information Entropy H (X)mentioning

confidence: 99%

Section: The Cooperative Evolution Mechanism Of Multiple Operatorsmentioning

confidence: 99%

An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

Zhang

Hao

2022

International Journal on Semantic Web and Information Systems

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“…H2ID [31] presents a two-stage hierarchical hybrid intrusion detection algorithm by incorporating multimodel deep autoencoder and soft-output classifiers to detect attacks in IoT and preserve privacy. WIDMoDS [32] aims to customize multiple intrusion detection models for the property of networks.…”

Section: Related Workmentioning

confidence: 99%

EFS-DNN: An Ensemble Feature Selection-Based Deep Learning Approach to Network Intrusion Detection System

Wang

Liu

Sun

2022

Security and Communication Networks

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In recent years, the scale of networks has substantially evolved due to the rapid development of infrastructures in real networks. Under the circumstances, intrusion detection systems (IDSs) have become the crucial tool to detect cyberattacks, malicious actions, and anomaly behaviors that threaten the credibility and integrity of information services in networks. The feature selection technologies are commonly applied in various intrusion detection algorithms owing to the potential of improving performance and speeding up decision-making. However, existing feature selection-based intrusion detection methods still suffer from high computational complexity or the lack of robustness. To mitigate these challenges, we propose a novel ensemble feature selection-based deep neural network (EFS-DNN) to detect attacks in networks with high-volume traffic data. In particular, we leverage light gradient boosting machine (LightGBM) as the base selector in the ensemble feature selection module to enhance the robustness of the selected optimal subset. Besides, we utilize a deep neural network with batch normalization and embedding technique as the classifier to improve the expressiveness. We conduct extensive experiments on three public datasets to demonstrate the superiority of the EFS-DNN compared with baselines.

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“…The network intrusion detection can actively monitor network events to identify security issues occured in the network [3]. Therefore, intrusion detection is one of key techniques for network security research [4].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many intrusion detection methods have been proposed, such as expert system detection intrusion technology, which matches the observed data with the predefined descriptions of intrusion features [6]. It can recognize attacks in the signature database, but ineffective for detecting unknown attacks.…”

Section: Introductionmentioning

confidence: 99%

Multiple Classification Algorithm Based on Graph Convolutional Neural Network for Intrusion Detection

Liu

Zhang

et al. 2021

Preprint

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In order to improve the recognition performance of intrusion detection, a graph convolutional neural network for multiple classification intrusion detection is proposed, named as GCNID. Firstly, the detection data are preprocessed by numerical and normalized, so that the disadvantage effect of numerical differences among various features can be reduced. Then, the adjacency matrix for intrusion data in GCIND is constructed by the k-nearest neighbor method, which utilizes the Euclide distance between different features as evaluation criterion. Besides, the adjacency matrix and the modified detection data are set as the input of GCNID framework. Furthermore, the neighbors information of each detection data are added into itself by the graph convolutional layer, such that more effective intrusion features can be extracted by GCNID. What s more, the parameters in the proposed method are optimized by back propagation until the network convergence. Finally, the Softmax logistic regression model is used to classify the types of intrusion. Simulation results show that the proposed method can not only improve the detection accuracy, but also classify the unknown attack types. The performance shows that the GCNID has a good ability of accuracy and generalization.

show abstract

A weighted intrusion detection model of dynamic selection

Cited by 6 publications

References 40 publications

An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

EFS-DNN: An Ensemble Feature Selection-Based Deep Learning Approach to Network Intrusion Detection System

Multiple Classification Algorithm Based on Graph Convolutional Neural Network for Intrusion Detection

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