Reducing the False Alarm Rate in Intrusion Detection System by Providing Authentication and Improving the Efficiency of Intrusion Detection System by using Filtered Clusterer Algorithm using Weka Tool
Pratik Jain1, Ravikant Kholwal2, Tavneet Singh Khurana3
1Pratik Jain*, Computer Science, IPS Academy, Institute of Engineering and Science, Indore, India.
2Ravikant Kholwal Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.
3Tavneet Singh Khurana, Computer Science, IPS Academy, Institute of Engineering and Science, Indore, India.
Manuscript received on April 06, 2021. | Revised Manuscript received on April 15, 2021. | Manuscript published on April 30, 2021. | PP: 134-143 | Volume-10 Issue-4, April 2021. | Retrieval Number: 100.1/ijeat.D24130410421 | DOI: 10.35940/ijeat.D2413.0410421
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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: An IDS supervises network traffic by searching for skeptical activities and previously determined threats and sends alerts when detected. In the current times, the splendors of Intrusion detection still prevail censorial in cyber safety, but maybe not as a lasting resolution. To study a plant, one must start with roots, so Cambridge dictionary defines an intrusion as “an occasion when someone goes into an area or situation where they’re not wanted or expected to be”. For understanding the article, we will characterize interruption as any network movement or unapproved framework identified with one or more PCs or networks. This is an interpretation of permissible use of a system attempting to strengthen his advantages to acquire more noteworthy access to the framework that he is at present endowed, or a similar client attempting to associate with an unapproved far-off port of a server. These are the interruptions which will cause from the surface world, a bothered ex-representative who was terminated recently, or from your reliable staff. In this proviso, the fair information is found as an attack when the case is a false positive. Here they are zeroing in on this issue with a representation and offering one answer for a similar issue. The KDD CUP 1999 informational index is utilized. Here we dropped the number of counts and considered the OTP authentication system. In the result of this test, it may be very well seen that on the off chance that a class has a higher number of checks, at that point this class is believed to be an anomaly class. In any case, it will be considered an oddity if the genuine individual is passing the edge esteem is considered an intruder. One arrangement is proposed to distinguish the genuine individual and to eliminate false positives.
Keywords: Anomaly Detection System (ADS), Bogus positive, Clustering, Data mining, Detection rate, Ensemble, False alert rate, K-Means.
Scope of the Article: Data mining