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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
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)