2024-03-29T06:36:50Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1350402022-02-07T15:36:17Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811
Intrusion Detection at Packet Level by Unsupervised Architectures
Herrero Cosío, Álvaro
Corchado Rodríguez, Emilio Santiago
Gastaldo, Paolo
Leoncini, Davide
Picasso, Francesco
Zunino, Rodolfo
Computer Science
Intrusion Detection Systems (IDS’s) monitor the traffic in computer networks for detecting suspect activities. Connectionist techniques can support the development of IDS’s by modeling ‘normal’ traffic. This paper presents the application of some unsupervised neural methods to a packet dataset for the first time. This work considers three unsupervised neural methods, namely, Vector Quantization (VQ), Self-Organizing Maps (SOM) and Auto-Associative Back-Propagation (AABP) networks. The former paradigm proves quite powerful in supporting the basic space-spanning mechanism to sift normal traffic from anomalous traffic. The SOM attains quite acceptable results in dealing with some anomalies while it fails in dealing with some others. The AABP model effectively drives a nonlinear compression paradigm and eventually yields a compact visualization of the network traffic progression.
2017-09-06T09:15:59Z
2017-09-06T09:15:59Z
2007
info:eu-repo/semantics/article
Intelligent Data Engineering and Automated Learning - IDEAL 2007. Lecture Notes in Computer Science. Volumen 4881, pp. 718-727.
978-3-540-77225-5 (Print) / 978-3-540-77226-2 (Online)
0302-9743 (Online)
http://hdl.handle.net/10366/135040
en
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Unported
Springer Science + Business Media