Abstract
In this paper intrusion detection using Bayesian probability is discussed. The systems designed are trained a priori using a subset of the KDD dataset. The trained classifier is then tested using a larger subset of KDD dataset. Initially, a system was developed using a naive Bayesian classifier that is used to identify possible intrusions. This classifier was able to detect intrusion with an acceptable detection rate. The classier was then extended to a multi-layer Bayesian based intrusion detection. Finally, we introduce the concept that the best possible intrusion detection system is a layered approach using different techniques in each layer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Crothers T (2003) Implementing intrusion detection systems: a hands-on guide for securing the network. Wiley, Indianapolis
Bace R, Mell P (2001) NIST special publication on intrusion detection systems, National Institute of Standards and Technology
Agarwal R, Joshi M (2000) PNrule: a new framework for learning classifier models in data mining (a case-study in network intrusion detection)
Levin I (2000) KDD-99 classifier learning contest LLSoft’s results overview. ACM SIGKDD Explorations I(2):67–75
Grapham P (2004) Hackers and painters: big ideas from the computer age, O’Reilly
Issac B, Jap W, Sutanto J (2009) Improved bayesian anti-spam filter Iimplementation and analysis on independent spam corpuses. In: international conference on computer engineering and technology, ICCET, Singapore, 2009
Alkabani Y, El-Kharashi M, Bedor H (2006) Hardware/software partitioning of a bayesian spam filter via hardware profiling. In: IEEE international symposium on industrial electronics, Canada, 2006
Chien J-T, Huang C-H, Shinoda K, Furui S (2006) Towards optimal bayes decision for speech recognition. In: IEEE international conference on acoustics, Speech and Signal Processing, ICASSP, Toulouse, 2006
Shi X, Manduchi R (2003) A study on bayes feature fusion for image classification. In: conference on computer vision and pattern recognition workshop, CVPRW, Madison, 2003
Kruegel C, Mutz D, Robertson W, Valeur F (2003) Bayesian event classification for intrusion detection. In: 19th annual computer security applications conference (ACSAC), IEEE Computer Society, Las Vegas
Cemerlic A, Yang L, Kizza J (2008) Network intrusion detection based on bayesian networks. In: Proceedings of the twentieth international conference on software engineering and knowledge engineering, SEKE, CA, 2008
Mehdi M, Zair A, Anou A, Bensebti M (2007) A bayesian networks in intrusion detection systems. J Comput Sci 3(5):259–265
Darwiche A (2010) Bayesian networks. Commun ACM 53(12):80–90
KDD Cup (1999) Data, 1999. [Online]. Available. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Aickelin U, Twycross J, Hesketh-Roberts T (2007) Rule generalization in intrusion detection systems using SNORT. Int J Electron Secur Digit Forensics 1(1):101–116
Lee W, SSJ, Mok K (1999) A data mining framework for building intrusion detection models. In: Proceedings of the 1999 IEEE symposium on security and privacy, Oakland
Chou TS (2007) Ensemble fuzzy belief intrusion detection design, Florida International University, Paper AAI3299199
Altwaijry H, Algarni S (2012) Bayesian based intrusion detection system. CCIS J, 1:1–6
Altwaijry H, Algarny S (2011) Multi-layer bayesian based intrusion detection system. In: Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science, WCECS 2011, San Francisco, 19-21 October, pp 918–922
Snort—Homepage, [Online]. Available. http://www.snort.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Altwaijry, H. (2013). Bayesian Based Intrusion Detection System. In: Kim, H., Ao, SI., Rieger, B. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 170. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4786-9_3
Download citation
DOI: https://doi.org/10.1007/978-94-007-4786-9_3
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4785-2
Online ISBN: 978-94-007-4786-9
eBook Packages: EngineeringEngineering (R0)