IDS Based on Bio-inspired Models
Fecha de publicación
Springer Science + Business Media
Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 4693, pp. 133-140.
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.
978-3-540-74826-7 (Print) / 978-3-540-74827-4 (Online)
0302-9743 (Print) / 1611-3349 (Online)
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