Sheikhi, S.; Kostakose, P. A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection. Sensors 2022, 22, 9318. https://doi.org/10.3390/s22239318
A novel anomaly-based intrusion detection model using PSOGWO-optimized BP neural network and GA-based feature selection
|Author:||Sheikhi, Saeid1; Kostakos, Panos1|
1Center for Ubiquitous Computing, University of Oulu, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023052547946
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2023-05-25
Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional datasets that simulate real-world network data increase the complexity and processing time of IDS system training and testing, while irrelevant features waste resources and reduce the detection rate. In this paper, a new intrusion detection model is presented which uses a genetic algorithm (GA) for feature selection and optimization algorithms for gradient descent. First, the GA-based method is used to select a set of highly correlated features from the NSL-KDD dataset that can significantly improve the detection ability of the proposed model. A Back-Propagation Neural Network (BPNN) is then trained using the HPSOGWO method, a hybrid combination of the Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Finally, the hybrid HPSOGWO-BPNN algorithm is used to solve binary and multi-class classification problems on the NSL-KDD dataset. The experimental outcomes demonstrate that the proposed model achieves better performance than other techniques in terms of accuracy, with a lower error rate and better ability to detect different types of attacks.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was funded by the European Commission grants IDUNN (grant no. 101021911) and Academy of Finland 6Genesis Flagship (grant no. 318927).
|EU Grant Number:||
(101021911) IDUNN - A Cognitive Detection System for Cybersecure Operational Technologies
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).