Identifying Intrusion Behaviour using Enhanced Hidden Markov Model
T Purnima1, Chandu Delhipolice2, K. Sarada3

1T Purnima*, Teaching Asst, Dep of CSE, SRM University, A.P., India.
2Chandu Delhipolice, Assistant Professor, Priyadarshini Institute of Technology & Science. Tenali, A.P.
3K. Sarada, Research Scholar, Dep of IT, VFSTR, Vadlamudi, A.P.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 29, 2020. | Manuscript published on April 10, 2020. | PP: 1618-1623 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4039049620/2020©BEIESP | DOI: 10.35940/ijitee.F4039.049620
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Abstract: Data Mining is a method for detecting network intrusion detection in networks. It brings ideas from variety of areas including statistics, machine learning and database processes. Decreasing price of digital networking is now economically viable for network intrusion detection. This analysis chiefly examines the system intrusion detection with machine learning and DM methods. To improve the accuracy and efficiency of SHMM, we are collecting multiple observation in SHMM that will be called as Multiple Hidden Markov Model (MHMM). It is used to improve better Detection accuracy compare with SHMM. In the standard Hidden Markov Model, we have observed three fundamental problems are Evaluation and decoding another one is learning problem. The Evaluation problem can be used for word recognition. And the Decoding problem is related to constant attention and also the segmentation. In this Proposed Research, the primary purpose is to model the sequence of observation in Network log and credit card log transactions process using Enhanced Hidden Markov Model (EHMM). And show how it can be used for intrusion detection in Network. In this procedure, an EHMM is primarily trained with the conventional manners of a intruders. If the trained EHMM does not recognize an incoming Intruder transaction with adequately high probability, it is thought to be fraudulent. 
Keywords:  IDS, KDD, HMM
Scope of the Article: Probabilistic Models and Methods