Abstract
One of the main challenges in the information security concerns the introduction of systems able to identify intrusions. In this ambit this work takes place describing a new Intrusion Detection System based on anomaly approach. We realized a system with a hybrid solution between host-based and network-based approaches, and it consisted of two subsystems: a statistical system and a neural one. The features extracted from the network traffic belong only to the IP Header and their trend allows us detecting through a simple visual inspection if an attack occurred. Really the two-tier neural system has to indicate the status of the system. It classifies the traffic of the monitored host, distinguishing the background traffic from the anomalous one. Besides, a very important aspect is that the system is able to classify different instances of the same attack in the same class, establishing which attack occurs.
Keywords
- Intrusion Detection
- Anomaly Detection
- Intrusion Detection System
- Simple Visual Inspection
- Statistical Discriminator
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ghosh, A.K., Wanken, J., Charron, F.: Detection Anomalous and Unknown Intrusions Against Programs. In: Proceedings of IEEE 14th Annual Computer Security Applications Conference, pp. 259–267 (1998)
Haines, J.W., Lippmann, R.P., Fried, D.J., Tran, E., Boswell, S., Zissman, M.A.: 1999 DARPA Intrusion Detection System Evaluation: Design and Procedures, MIT Lincoln Laboratory Technical Report (1999)
Horeis, T.: Intrusion Detection with Neural Networks - Combination of Self-Organizing Maps and Radial Basis Function Networks for Human Expert Integration. Computational Intelligence Society Student Research Grants (2003)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Labib, K., Vemuri, V.R.: Detecting and Visualizing Denial-of-Service and Network Probe Attacks Using Principal Component Analysis. In: The 3rd Conference on Security and Network Architectures, La Londe, Cote d’Azur, France (2004)
Lee, W., Stolfo, S.J., Mok, K.: A Data Mining Framework for Building Intrusion Detection Models. In: Proceedings of 1999 IEEE Symposium of Security and Privacy, pp. 120–132 (1999)
Lichodzijewski, P., Zincir-Heywood, A.N., Heywood, M.I.: Dynamic Intrusion Detection Using Self-Organizing Maps. In: The 14th Annual Canadian Information Technology Security Symposium (2002)
Mahoney, M.V., Chan, P.K.: An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection. In: Vigna, G., Krügel, C., Jonsson, E. (eds.) RAID 2003. LNCS, vol. 2820, pp. 220–237. Springer, Heidelberg (2003)
Ramadas, M., Ostermann, S., Tjaden, B.C.: Detecting Anomalous Network Traffic with Self-organizing Maps. In: Vigna, G., Krügel, C., Jonsson, E. (eds.) RAID 2003. LNCS, vol. 2820, pp. 36–54. Springer, Heidelberg (2003)
Rhodes, B.C., Mahaffey, J.A., Cannady, J.D.: Multiple Self-Organizing Maps for Intrusion Detection. In: Proceedings of the 23rd National Information Systems Security Conference, Baltimore, MD (2000)
Vigna, G., Kemmerer, R.A.: NetSTAT a network-based Intrusion Detection Approach. In: Proceedings of 14th Annual Computer Security Applications Conference, Scottsdale, AZ, USA, pp. 25–34 (1998)
wincap, http://winpcap.polito.it/
Zhang, Z., Li, J., Manikopoulos, C.N., Jorgenson, J., Ucles, J.: HIDE: a Hierarchical Network Intrusion Detection System Using Statistical Preprocessing and Neural Network Classification. In: Proceeding of the 2001 IEEE Workshop on Information Assurance and Security, United States Military Academy, West Point, NY, pp. 85–90 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Baldassarri, P., Montesanto, A., Puliti, P. (2007). Detecting Anomalous Traffic Using Statistical Discriminator and Neural Decisional Motor. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-73053-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73052-1
Online ISBN: 978-3-540-73053-8
eBook Packages: Computer ScienceComputer Science (R0)