A Survey on Intrusion Detection Technique over the Web Data
Bhagwat P. Dwivedi1, Shiv Kumar2, Babita Pathik3
1Bhagwat P. Dwivedi, M.Tech. Scholar, Department of Computer Science and Engineering, Lakshmi Narain College of Technology Excellence, R.G.P.V. University, Bhopal (M.P)-462021, India.
2Dr. Shiv Kumar, Professor & Head, Department of Computer Science and Engineering, Lakshmi Narain College of Technology Excellence, R.G.P.V. University, Bhopal (M.P)-462021, India.
3Babita Pathik, Assistant Professor, Department of Computer Science and Engineering, Lakshmi Narain College of Technology Excellence, R.G.P.V. University, Bhopal (M.P)-462021, India.
Manuscript received on 23 January 2017 | Revised Manuscript received on 30 January 2017 | Manuscript published on 30 January 2017 | PP: 1-3 | Volume-5 Issue-6, January 2017 | Retrieval Number: F1640015617©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The intrusion detection systems (IDSs) generate large number of alarms most of which are false positives. Fortunately, there are reasons for triggering alarms where most of these reasons are not attacks. In this work, a new data mining technique has been developed to group alarms and to produce clusters. we have monitored a paper IDS over web mining – up approach which is efficient and determined to visualized the intrusion data and optimize according to the user requirement and monitored the data efficiently, here we would like to further enhance research work on analyzing and using the entropy data as input and to use them in technique to visualize and to optimize according to the user requirement in the web entropy visualization.
Keywords: Network intrusion, web mining scenario, web intrusion data, Data Mining Algorithms.
Scope of the Article: Data Mining