Authors:
El Mahdi Mercha
1
;
2
;
El Mostapha Chakir
2
and
Mohammed Erradi
1
Affiliations:
1
ENSIAS, Mohammed V University, Rabat, Morocco
;
2
HENCEFORTH, Rabat, Morocco
Keyword(s):
Cyber Security, Intrusion Detection System, Deep Learning, Transformer.
Abstract:
The increasing number of online systems and services has led to a rise in cyber security threats and attacks, making Intrusion Detection Systems (IDS) more crucial than ever. Intrusion Detection Systems (IDS) are designed to detect unauthorized access to computer systems and networks by monitoring network traffic and system activities. Owing to the valuable values provided by IDS, several machine learning-based approaches have been developed. However, most of these approaches rely on feature selection methods to overcome the problem of high-dimensional feature space. These methods may lead to the exclusion of important features or the inclusion of irrelevant ones, which can negatively impact the accuracy of the system. In this work, we propose Trans-IDS (transformer-based intrusion detection system), a transformer-based system for intrusion detection, which does not rely on feature selection methods. Trans-IDS learns efficient contextualized representations for both categorical and n
umerical features to achieve high prediction performance. Extensive experiments have been conducted on two publicly available datasets, namely UNSW-NB15 and NSL-KDD, and the achieved results show the efficiency of the proposed approach.
(More)