Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection
K. Sai Manoj1, P. S. Aithal2

1Dr. K. Sai Manoj*, Member IEEE & Postdoctoral researcher, Department of CSE, Srinivas University, Karnataka, Mangalore, India.
2S. Rajesh, CSE, V R Siddhartha Engineering College, Vijayawada, India.
Manuscript received on January 24, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 29, 2020. | PP: 4084-4090 | Volume-9 Issue-3, February 2020. | Retrieval Number:  C5979029320/2020©BEIESP | DOI: 10.35940/ijeat.C5979.029320
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Abstract: An interference discovery framework is customizing that screens a singular or an arrangement of PCs for toxic activities that are away for taking or blue-penciling information or spoiling framework shows. The most methodology used as a piece of the present interference recognition framework is not prepared to deal with the dynamic and complex nature of computerized attacks on PC frameworks. In spite of the way that compelling adaptable methodologies like various frameworks of AI can realize higher discovery rates, cut down bogus alert rates and reasonable estimation and correspondence cost. The use of data mining can realize ceaseless model mining, request, gathering and littler than ordinary data stream. This examination paper portrays a connected with composing audit of AI and data delving procedures for advanced examination in the assistance of interference discovery. In perspective on the number of references or the congruity of a rising methodology, papers addressing each procedure were recognized, examined, and compacted. Since data is so fundamental in AI and data mining draws near, some striking advanced educational records used as a piece of AI and data burrowing are depicted for computerized security is shown, and a couple of recommendations on when to use a given system are given.
Keywords: Cloud Computing, Data mining, Block Chain, Machine Learning, Cyber Security, Attacks, ADS, SMV.