A New Intrusion Prevention System Based on Ann using Genuine Traffic
R. Jayakarthik1, M.S. Nidhya2, M. Indirakumar3
1Dr. R. Jayakarthik, Associate Professor, Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India
2Dr. M.S. NIDHYA, Assistant Professor, Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India
3M. Indirakumar, PG Student, Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India
Manuscript received on 01 April 2019 | Revised Manuscript received on 07 May 2019 | Manuscript published on 30 May 2019 | PP: 2683-2687 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1323058119/19©BEIESP
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Abstract: With the advancement in the computer Technologies we investigation the neural system technique and stream-based system information as information source. A Two Stages Neural Network interruption location framework dependent on-stream information is proposed for identifying and arranging assaults in system traffic. The principal organize recognizes noteworthy changes in the rush hour gridlock that could be a potential assault, while the second stage characterizes if there is a known assault and all things considered groups the sort of assault. After distinguishing proof of assaults, set beginning square as a spring up message. After that gather the votes from client’s who are visit the influenced pages. We group into false positive and false negative dependent on the KNN arrangement. In this article, a created learning model for Fast Learning Network (FLN) in light of molecule swarm enhancement (PSO) and counterfeit neural system. In ANN works in three phases like initial segment is to prepare the two phases of neural systems and to discover the ideal number of hubs in shrouded layers. The second piece of trial was led to test recognition module (neural system organize one). The third piece of examinations was directed to test the recognition and order modulewebsite.
Index Terms: Artificial Neural Network, Intrusion Detection System, Network Security
Scope of the Article: Artificial Intelligence