A Threat-Hunting of UNSW-NB15 with Machine Learning Techniques to Achieve Resilience
Diana Arulkumar1, Kartheeban.K2
1Diana Arulkumar, Department of Computer Science, Karunya Institute of Technology and Science, Coimbatore (Tamil Nadu), India.
2Kartheeban K, Department of Computer Science, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 30 December 2019 | PP: 488-494 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A1110291S419/19©BEIESP | DOI: 10.35940/ijeat.A1110.1291S419
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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: In order to focus on the mission and functions of the business of the organizations, cyber resiliency have to play a critical role against the adversaries’ target. The strategy recommended by NIST to reduce the suspect ability of cyber-attacks of the system with the three dimensions such as harden the target, limit the damage to the target and make the target resilient. The threats could be based on cyber and noncyber. The objective is to provide cyber resiliency on the Advanced Persistent Threat (APT), has born with the nature of sophisticated, stealthy, persistent towards target and highly adoptable to the environment. The challenge is to provide cyber resilience to the system from compromising tactics of the adversaries, uncertain in eradication of threat due to its persistent nature, recognizing its adapting ability. The cyber resiliency also links with other disciplines like safety, fault tolerance, privacy, resilience and survivability, reliability and security.
Keywords: Advanced Persistent Threat (APT, Cyber-Attacks, Cyber Resiliency), UNSW-NB15.
Scope of the Article: Machine Learning