Abstract
Network security has become an essential component of any computer network. Despite significant advances having been made on network-based intrusion prevention and detection, ongoing attacks penetrating network-based security mechanisms have been reported. It is being realized that network-based security mechanisms such as firewalls or intrusion detection systems (IDS) are not effective in detecting certain attacks such as insider attacks and attacks without generating significant network traffic. The trend of network security will be to merge host-based IDS (HIDS) and networkbased IDS (NIDS). This chapter will provide the fundamentals of host-based anomaly IDS as well as their developments. A new architectural framework is proposed for intelligent integration of multiple detection engines. The novelty of this framework is that it provides a feedback loop so that one output from a detection engine can be used as an input for another detection engine. It is also illustrated how several schemes can be derived from this framework. New research topics for future research are discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
A.S. Tanenbaum, A.S. Woodhull: Operating Systems: Design and Implementation, 3rd edn. (Pearson, NJ, USA 2006)
J.M. Garrido: Principles of modern operating systems (Jones and Barlett, MA, USA 2008)
A.S. Tanenbaum: Computer Networks, 3rd edn. (Prentice-Hall, NJ, USA 1996)
W.R. Stevens: TCP/IP Illustrated: the protocols (Addison Wesley Longman, MA, USA 1994)
J. Joshi, P. Krishnamurthy: Network Security. In: Information Assurance: Dependability and Security in Networked Systems, ed. by Y. Qian (Elsevier, Amsterdam, The Netherlands 2008), Chap. 2
B. Schneier: Applied Cryptography, Protocols, Algorithms, and Source Code in C (Wiley, NJ, USA 1996)
Y. Wang, J. Hu, D. Philips: A fingerprint orientation model based on 2D Fourier expansion (FOMFE) and its application to singular-point detection and fingerprint indexing, IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 13 (2007)
K. Xi, J. Hu: Introduction to bio-cryptography. In: Springer Handbook on Communication and Information Security, ed. by P. Stavroulakis (Springer, Berlin, Germany 2009), Chap. 6
J. Hu, P. Bertok, Z. Tari: Taxonomy and framework for integrating dependability and security. In: Information Assurance: Dependability and Security in Networked Systems, ed. by Y. Qian (Elsevier, Berlin, Germany 2008), Chap. 6
P.E. Proctor: The Practical Intrusion Detection Handbook (Prentice Hall PTR, NJ, USA 2001)
CNN.com: Worm strikes down Windows 200 systems (2005), available from: http://www.cnn.com/2005/TECH/internet/08/16/computer:worm/ (last accessed November 25, 2008)
Sophos: Breaking news: worm attacks CNN, ABC, The Financial Times, and The New York Times (2005), http://www.sophos.com/pressoffice/news/articles/2005/08/va_breakingnews.html (last accessed November 25, 2008)
D. Denning: An intrusion detection model, IEEE Symposium on Security and Privacy (IEEE, NJ, USA 1986) pp. 118–131
J. Hu, Q. Dong, X. Yu, H.H. Chen: A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection, IEEE Netw. 23(1), 42–47 (2009)
R.R. Kompella, S. Singh, G. Varghese: On scalable attack decision in the network, IEEE/ACM Trans. Netw. 15(1), 14–25 (2007)
S.A. Hofmeyr, S. Forrest, A. Somayaji: Intrusion detection using sequences of system calls, J. Comput. Secur. 6(3), 151–180 (1998)
D. Hoang, J. Hu, P. Bertok: Intrusion detection based on data mining, 5th Int. Conference on Enterprise Information Systems (Angers 1998) pp. 341–346
X.D. Hoang, J. Hu: An efficient hidden Markov model training scheme for anomaly intrusion detection of server applications based on system calls, IEEE Int. Conference on Networks (ICON 2004) (Singapore 2004) pp. 470–474
X.D. Hoang, J. Hu, P. Bertok: A multi-layer model for anomaly intrusion detection using program sequences of system calls, 11th IEEE Int. Conference on Network (ICON 2003) (Sydney 2003) pp. 531–536
W. Lee, S.I. Stolfo: A framework for constructing features and models for intrusion detection systems, ACM Trans. Inf. Syst. Secur. 3(4), 227–261 (2000)
W. Lee, S.J. Stolfo: Data mining approaches for intrusion detection, Proc. 7th USENIX Security Symposium (San Antonio 1998)
C. Warrender, S. Forrest, B. Perlmutter: Detecting intrusions using system calls: alternative data models, IEEE Computer Society Symposium on Research in Security and Privacy (1999) pp. 257–286
J.L. Gauvain, C.H. Lee: Bayesian learning of Gaussian mixture densities for hidden Markov models, Proc. DARPA Speech and Natural Language Workshop (1991)
S. Forrest: A sense of self for Unix processes, IEEE Symposium on Computer Security and Privacy (1996)
X.H. Dau: E-Commerce Security Enhancement and Anomaly Intrusion Detection Using Machine Learning Techniques. Ph.D. Thesis (RMIT University, Melbourne 2006)
L.R. Rabiner: A tutorial on hidden Markov model and selected applications in speech recognition, Proc. IEEE 77(2), 257–286 (1989)
X.H. Dau: Intrusion detection, School of Computer Science and IT (RMIT University, Melbourne 2007)
J. Langford: Optimizing hidden Markov model learning, Technical Report (Toyota Technological Institute at Chicago, Chicago 2007)
R. Dugad, U.B. Desai: A tutorial on hidden Markov models, Technical Report No: SPANN-96.1, Indian Institute of Technology, Bombay (1996)
J.L. Gauvain, C.H. Lee: MAP estimation of continuous density HMM: Theory and Applications, Proceedings of the DARPA Speech and Natural Language Workshop (1992)
J.L. Gauvain, C.H. Lee: A posteriori estimation for multivariate Gaussian mixture observations of Markov chains, IEEE Trans. Speech Audio Process. 1(2), 291–298 (1994)
Y. Gotoh, M.M. Hochberg, H.F. Silverman: Efficient training algorithm for HMM’s using incremental estimation, IEEE Trans. Speech Audio Process. 6(6), 539–548 (1998)
R.I.A. Davis, B.C. Lovell, T. Caelli: Improved estimation of hidden Markov model parameters from multiple observation sequences, 16th Int. Conference on Pattern Recognition (2002) pp. 168–171
X. Li, M. Parizean, R. Plamondon: Training hidden Markov models with multiple observations–A combinatorial method, IEEE Trans. Pattern Anal. Mach. Int. 22(4), 371–377 (2000)
R.J. Rummel: Understanding correlation (Department of Political Science University of Hawaii, Honolulu 1976)
H. Mannila, H. Toivonen, I. Verkamo: Discovery of frequent episodes in event sequences, Data Mining and Knowledge Discovery, Vol. 1 (Springer, MA, USA 1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hu, J. (2010). Host-Based Anomaly Intrusion Detection. In: Stavroulakis, P., Stamp, M. (eds) Handbook of Information and Communication Security. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04117-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-04117-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04116-7
Online ISBN: 978-3-642-04117-4
eBook Packages: EngineeringEngineering (R0)