User Behavioral Analysis Using Markov Chain and Steady-State in Tracer and Checker Model
DOI:
https://doi.org/10.13052/2245-1439.826Keywords:
Biometric authentication, mobile agent, intrusion detection, Markov chain process, TCM server, HIDSAbstract
Tracer and checker model is an intrusion detection technique that uses mobile agent to track the user behaviour in ad-hoc network. Mobile agent can migrate to host and execute tasks parallelly. We enhanced TCM model to identify the intrusion in a host by analysing user behaviour during authentication process. Markov chain is a random process that transit from one state to another which depends only on the current state but not the sequence of events. Mobile agent is used to analyse the user input behaviour during authentication process which helps to predict intrusion in the system. In this paper, a behavioural approach is handled to identify the intrusion process. Markovchain is used with the proposed behaviour approach and Mobile agents are used to distribute this functionality. Behavioural analysis is illustrated and simulation are experimented.
Downloads
References
V. Arun, K. L. Shunmuganathan, ‘Encrypted Tracer and Checker Model’, Journal of Emerging Technologies – Image Processing and Networking, Vol. 6, No. 2, pp. 23–27, 2011.
O. Al-Jarrah, ‘Network Intrusion Detection System using attack behaviour classification’ International Conference on Information and Communication Systems (ICICS), pp. 1–6, 2014.
Alon Schclar, LiorRokach, Adi Abramson, and Yuval Elovici, ‘User Authentication Based on Representative Users’, IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, Vol. 42, No. 6, 2012.
R. Kirushnaamoni, Mepco Schlenk, ‘Defenses to curb online password guessing attacks’, International Conferene on Information Communication and Embedded Systems, 2013.
Na Zeng, Wuhan China, Xiaolong Zhang, Hong Zhang, ‘Intramural Network Intrusion Detection by Monitoring User Behavior’, International Symposium on Knowledge Acquisition and Modeling, pp. 178–181, 2009.
M. B. Nirmala, A. S. Manjunath, ‘Mobile agent based secure code update in wireless sensor networks’, International Conference on Information Networking pp. 75–80, 2015.
G. Ioannou, P. Louvieris, N. Clewley, G. Powell, ‘A Markov multi-phase transferable belief model: An application for predicting data exfiltration APTs’, International Conference on Information Fusion (FUSION), pp. 842–849, 2013.
Zheng Zhang, Xiu Yang, Oseledets, G. E. Karniadakis, ‘Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition’, Computer-Aided Design of Integrated Circuits and Systems, Vol. 34, pp. 63–76, 2015.
S. Bleha, C. Slivinsky, and B. Hussein, ‘Computer-access security systems using keystroke dynamics’, IEEE Trans. Pattern Analysisand Machine Intelligence., Vol. 12, No. 12, pp. 1217–1222, 1990.
A. El-Saddik, M. Orozco, Y. Asfaw, S. Shirmohammadi, and A. Adler, ‘A novel biometric system for identification and verification of haptic users’, IEEE Transaction on Instrumentation and Measurement, Vol. 56, pp. 895–906, 2007.
E. Frank and I. H. Witten, ‘WEKA: A machine learning workbench for data mining’, in Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, O. Maimon and L. Rokach, Eds. New York: Springer, pp. 1305–1314, 2005.
N. J. Grabham and N. M. White, ‘Validation of keypad user identity using a novel biometric technique’, Journal of Physics, Vol. 76, pp. 012023-1–012023-6, 2007.
Yusuf Albayram, MhammadMaifi Hasan Khan and Ross Buck, ‘An investigation into users’ considerations towards using password managers’, Human-centric Computing and Information Sciences, 2017.
T. Subburaj and K. Suthendran, ‘Detection and Trace Back of DDoS Attack Based on Statistical Approach’, Journal of Advanced Research in Dynamical and Control Systems, Vol. 13, pp. 66–74, 2017.
