IDS Based on Bio-inspired Models

Authors:
Paolo Gastaldo;Francesco Picasso;Rodolfo Zunino;Álvaro Herrero;Emilio Corchado;José Manuel Sáiz
Affiliations:
Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145 Genoa, Italy;Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145 Genoa, Italy;Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145 Genoa, Italy;Department of Civil Engineering, University of Burgos, C/ Francisco de Vitoria s/n, 09006 Burgos, Spain;Department of Civil Engineering, University of Burgos, C/ Francisco de Vitoria s/n, 09006 Burgos, Spain;Department of Civil Engineering, University of Burgos, C/ Francisco de Vitoria s/n, 09006 Burgos, Spain
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

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Abstract

Unsupervised projection approaches can support Intrusion Detection Systems for computer network security. The involved technologies assist a network manager in detecting anomalies and potential threats by an intuitive display of the progression of network traffic. Projection methods operate as smart compression tools and map raw, high-dimensional traffic data into 2-D or 3-D spaces for subsequent graphical display. The paper compares three projection methods, namely, Cooperative Maximum Likelihood Hebbian Learning, Auto-Associative Back-Propagation networks and Principal Component Analysis. Empirical tests on anomalous situations related to the Simple Network Management Protocol (SNMP) confirm the validity of the projection-based approach. One of these anomalous situations (the SNMP community search) is faced by these projection models for the first time. This work also highlights the importance of the time-information dependence in the identification of anomalous situations in the case of the applied methods.