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EnglishUnsupervised 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.
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| Titolo: | IDS Based on Bio-Inspired Models |
| Autori: | |
| Data di pubblicazione: | 2007 |
| Handle: | http://hdl.handle.net/11567/260579 |
| ISBN: | 978-3-540-74826-7 |
| Appare nelle tipologie: | 04.01 - Contributo in atti di convegno |
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http://hdl.handle.net/11567/260579
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