Protect Internet from Intrusion with Advanced Spark Framework
N. Deshai1, B.V.D.S. Sekhar2, S. Venkataramana3
1N. Deshai, Department of Information Technology, Sagi Rama Krishnam Raju Engineering College, Bhimavaram (Andhra Pradesh), India.
2B.V.D.S.Sekhar, Department of Information Technology, Sagi Rama Krishnam Raju Engineering College, Bhimavaram (Andhra Pradesh), India.
3S. Venkata Ramana, Department of Information Technology, Sagi Rama Krishnam Raju Engineering College, Bhimavaram (Andhra Pradesh), India.
Manuscript received on 12 May 2019 | Revised Manuscript received on 06 June 2019 | Manuscript Published on 15 June 2019 | PP: 186-190 | Volume-8 Issue-1S3 June 2019 | Retrieval Number: A10340681S319/2019©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: Today’s internet world, the huge volume of internet data traffic flow with high velocity every second, that become extremely comprehensive and complicated due to a massive amount of data generated as streaming mostly in all applications on every field. However, rapidly increasing the more cyber crimes over the cloud systems and various transactions. The latest and essential security technology in the computer network is Intrusion detection system, that needs effective and more enhanced detection technologies, which ensure to recognize the new intrusive activities and critical threats to network security. Therefore, to avoid extremely the intrusion issues becomes more tedious and unexciting action. Because processing with conventional data processing tools is a challenging task due to the poor enhancement in internet-based services. In this paper, we strongly recommended a latest apache spark paradigm, which developed with more fault tolerant, distributed, scalable, and reliable system. Regarding correlation and Chi-squared feature, the selection are being used to overcome the less advanced features and then evaluate intrusion prevention technique with Random forest, regression, Support vector machines, decision trees, Bayes classifier and k-means are being used for quick and effective countermeasures to prevent different intrusion occurrences.
Keywords: Security, Network, Intrusion Detection System, Apache Spark.
Scope of the Article: Patterns and Frameworks