Intrusion Detection System Based on an Updated ANN Model

Xue, Yu; Onzo, Bernard-marie; Neri, Ferrante

Abstract

An intrusion detection system (IDS) is a software application or hardware appliance that monitors traffic on networks and systems to search for suspicious activity and known threats, sending up alerts when it finds such items. In these recent years, attention has been focused on artificial neural networks (ANN) techniques, especially Deep Learning approach on anomaly-based detection techniques; because of the huge and unbalanced datasets, IDS encounters real data processing problems. Thus, different techniques have been presented which can handle this problem. In this paper, a deep learning model or technique based on the Convolutional Neural Network (CNN) is proposed to improve the accuracy and precisely detect intrusions. The entire proposed model is divided into four stages: data collection, data pre-processing, the training and testing stage, and performance evaluation.

• Conference Name: 12th International Conference, ICSI 2021
• Conference Location: Qingdao, China
• Start Date: Jul 17, 2021
• End Date: Jul 21, 2021
• Acceptance Date: Apr 19, 2021
• Online Publication Date: Jul 7, 2021
• Publication Date: Jul 7, 2021
• Deposit Date: Jul 10, 2021
• Publicly Available Date: Jul 8, 2022
• Publisher: Springer
• Pages: 472-479
• Series Title: Lecture Notes in Computer Science
• Series Number: 12690
• Series ISSN: 0302-9743
• Book Title: Advances in Swarm Intelligence : 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part II
• ISBN: 9783030788100
• DOI: https://doi.org/10.1007/978-3-030-78811-7_44
• Public URL: https://nottingham-repository.worktribe.com/output/5768538
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-030-78811-7_44