Abstract
The capability to detect anomalous states in a network is important for both the smooth operation of the network and the security of the network. Modern networks are often heterogeneous. This raises a new challenge for anomaly detection, as there may be a wide variety of anomalous activities across the heterogeneous components of a network. We often seek a detection system that not only performs accurate anomaly detection but also provides mechanisms for human expert to understand the decision making process inside the system. In this paper, we investigate the application of sparse Bayesian methods for anomaly detection in such scenario. By taking advantage of the sparse Bayesian frameworkâs capability to conduct automatic relevance discovery, we construct a detection system whose decision making is mostly based on a few representative examples from the training set. This provides human interpretability as expert can analyze the representative examples to understand the detection mechanism. Our experiment results show the potential of this approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agyemang, M., Barker, K., Alhajj, R.: A comprehensive survey of numeric and symbolic outlier mining techniques. Intell. Data Anal. 10(6), 521â538 (2006)
Beale, J., Caswell, B., Poor, M.: Snort 2.1 intrusion detection. Syngress Publishing (2004)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3) (2009)
Chen, W.-H., Hsu, S.-H., Shen, H.-P.: Application of SVM and ANN for intrusion detection. Computers & Oper. Res. 32, 2617â2634 (2005)
Dickerson, J.E., Dickerson, J.A.: Fuzzy network profiling for intrusion detection. In: 19th International Conference of the North American Fuzzy Information Processing Society (2000)
Faul, A.C., Tipping, M.E.: Analysis of sparse bayesian learning. In: Advances in Neural Information Processing Systems, pp. 383â389 (2001)
Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artificial Intelligence Review 22(2), 85â126 (2004)
Kayacik, H.G., Zincir-Heywood, A.N., Heywood, M.I.: A hierarchical SOM-based intrusion detection system. Eng. Appl. of AIÂ 20(4), 439â451 (2007)
KDD. Kdd cup intrusion detection dataset (1999), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Levin, I.: KDD-99 classifier learning contest: LLSoftâs results overview. SIGKDD Explorations 1(2), 67â75 (2000)
Patcha, A., Park, J.-M.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks 51(12), 3448â3470 (2007)
Paxson, V.: Bro: a system for detecting network intruders in real-time. In: Proceedings of the 7th USENIX Security Symposium (1998)
Porras, P.A., Neumann, P.G.: Emerlad. In: Proceedings of 20th National Information Systems Security Conference, pp. 353â365 (1997)
Stolfo, S., Prodromidis, A., Tselepsis, S., Lee, W., Fan, D., Chan, P.: JAM: Java agents for meta-learning over distributed databases. In: Workshop on Fraud Detection and Risk Management AAAI 1997 (1997)
Tipping, M.E.: Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211â244 (2001)
Williams, O., Blake, A., Cipolla, R.: Sparse bayesian learning for efficient visual tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 27(8), 1292â1304 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Âİ 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhang, J., Kannan, R. (2012). A Sparse Bayesian Framework for Anomaly Detection in Heterogeneous Networks. In: Zhang, X., Qiao, D. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 74. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29222-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-29222-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29221-7
Online ISBN: 978-3-642-29222-4
eBook Packages: Computer ScienceComputer Science (R0)