Relational Characteristics of Maliciousness and Hacker in a Cyberattack
Jin-keun Hong1, Jung-Soo Han2
1Jin-Keun Hong, Division of ICT, Baekseok University, Cheonan City, South Korea.
2Jung-Soo Han, Division of ICT, Baekseok University, Cheonan City, South Korea.
Manuscript received on 20 September 2019 | Revised Manuscript received on 06 October 2019 | Manuscript Published on 11 October 2019 | PP: 525-530 | Volume-8 Issue-2S10 September 2019 | Retrieval Number: B10920982S1019/2019©BEIESP | DOI: 10.35940/ijrte.B1092.0982S1019
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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: Cyber security threats are increasing day by day. However, this threat is sophisticated and intelligent. Therefore, artificial intelligence-based learning algorithms are emerging to effectively respond to cybersecurity threats. However, there has yet to be any interest or approach in studying the likelihood of an attack by efficiently analyzing the causes of the attack by individuals, religious groups, and hackers subordinate to state agencies. An idea in this study is to analyze hacker tendencies. And the link to how hacker tendencies affect attacks is being sought by the intelligence algorithm, which provides a sample of the predictive model as a preliminary study. Therefore, this study required a study on what an attacker’s individual is influenced by, how a hacker subordinate to a religious group is affected by an attack from a religious group, and how a hacker subordinate to a national institution is affected by an attack from a state institution. In this study, however, we briefly focused on the factors that affect these attacks. In this study, we proposed an intelligent simplified model that predicts threats with the goal of producing results on whether or not an attack by combining the pattern of attack with inputs and weighing factors. Therefore, three groups of attackers were analyzed. From this, a simple intelligent algorithm model was presented The results of this study are expected to help derive the correlation between future hacker attack propensity analysis and intelligent algorithm. Future research will implement a threat analysis system that can more specifically derive attack propensity factors and apply them to intelligent algorithms (weights, f functions) to determine whether an attack is possible or not.
Keywords: Cyberattack, Learning, Hacker, Analysis, Malicious.
Scope of the Article: Learning Software Design Engineering