Abstract
Computer security is an important issue for an organization due to increasing cyber-attacks. There exist some intelligent techniques for designing intrusion detection systems which can protect the computer and network systems. In this paper, we discuss multivariate linear regression model (MLRM) to develop an anomaly detection system for outlier detection in hardware profiles. We perform experiments on performance logfiles taken from a personal computer. Simulation results show that our model discovers intrusion effectively and efficiently.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tomášek, M., Čajkovský, M., Madoš, B.: Intrusion detection system based on system behaviour. In: IEEE Jubilee International Symposium Applied Machine Intelligence and Informatics, pp. 271−275 (2012)
Ali, F., Ali, B.H., Len, Y.Y.: Development of host based intrusion detection system for log files. In: IEEE Symposium on Business, Engineering and Industrial Applications-ISBEIA, pp. 281−285 (2011)
Om, H., Hazra, T.: Design of anomaly detection system for outlier detection in hardware profile using PCA. Int. J. Comput. Sci. Eng. 4(9), 1623–1632 (2012)
Yeung, D.Y., Ding, Y.: Host-based intrusion detection using dynamic and static behavioral models. Pattern Recogn. 36(1), 229–243 (2003)
Ou, C.M.: Host-based intrusion detection systems adapted from agent-based artificial immune systems. Neurocomputing 88, 78–86 (2012)
Laureano, M., Maziero, C., Jamhour, E.: Protecting host-based intrusion detectors through virtual machines. Comput. Netw. 51(5), 1275–1283 (2007)
Mechtri, L., Djemili, F., Ghoualmi, N.: Intrusion detection using principal component analysis. Eng. Syst. Manage. Appl.vol no 1−6 (2010)
Shyu, M., Chen, S., Kanoksri, S., Chang, L.: A novel anomaly detection scheme based on principal component classifier. In: IEEE Foundations and New Directions Data Mining Workshop. , pp. 172−179 (2003)
Badr, N., Noureldien, N.A.: Examining outlier detection performance for principal components analysis method and its robustification method. int. J. Adv. Eng. Technol. 6(2), 573–582 (2013)
Ye, N., Emran, S.M., Chen, Q., Vilbert, S.: Multivariate statistical analysis of audit trails for host based intrusion detection. IEEE Trans. Comput. 51(7), 810–820 (2002)
Om, H., Hazra, T.: Statistical techniques in anomaly intrusion detection system. int. J. Adv. Eng. Technol. 5(1), 378–398 (2012)
Filzmoser, P., Garrett Robert, G., Reimann, C.: Multivariate outlier detection in exploration geochemistry. Comput. Geosci. 31(5), 579–587 (2005)
Sayed, A., Aziz, A., Dayem, A., Darwish, G.: Network intrusion detection system applying multivariate control charts: In INFOS. (2008)
Rencher, A.C.: Methods of Multivariate Analysis. A Wiley Inc. Publication. (2002)
Gupta, B.B., et al.: Estimating Strength of a DDoS Attack Using Multiple Regression Analysis: Advanced Computing. Springer, Heidelberg (2011)
Jobson, J.D.: Applied Multivariate Data Analysis Categorical and Multivariate Methods. Springer, New York (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Gautam, S.K., Om, H. (2015). Multivariate Linear Regression Model for Host Based Intrusion Detection. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 3. Smart Innovation, Systems and Technologies, vol 33. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2202-6_33
Download citation
DOI: https://doi.org/10.1007/978-81-322-2202-6_33
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2201-9
Online ISBN: 978-81-322-2202-6
eBook Packages: EngineeringEngineering (R0)