A Predictive Classification Method for Email Phishing Attacks using Random Forest and A-R trees
Sanjitha M1, Sri Lakshmi J2, S Sameeksha3, Ravi V4

1Sanjitha M, Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, Karnataka, India.
2Sri Lakshmi J, Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, Karnataka, India.
3S Sameeksha, Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, Karnataka, India.
4Ravi V, Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, Karnataka, India.
Manuscript received on July 23, 2020. | Revised Manuscript received on August 03, 2020. | Manuscript published on August 10, 2020. | PP: 421-424 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J75710891020 | DOI: 10.35940/ijitee.J7571.0891020
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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-attacks are the attempts made by an individual or an organization deliberately, to breach the information system mainly computers of another individual or organization. These attacks have risen in recent years due to various reasons posing the need for systems that can use adaptive learning techniques to detect and mitigate these attacks at an early stage. Phishing is one of the significant cyber-attacks. According to global security report 2019, phishing was the major cause of attacks in corporate networks. Phishing attack uses disguised email to achieve its goal. In this attack, attacker masquerade himself as a trusted individual or a company and trick the email recipient into clicking malicious links or attachments. The proposed method provides a testbed for detecting and mitigating various types of phishing attacks. Machine learning techniques are used to build an intelligent system which can detect phishing attacks. This application uses random forest algorithm with AR-Trees (acceptance-rejection tree algorithm) to determine the attacks by considering various datasets available online and new datasets dynamically constructed for making the system ready to mitigate future phishing attacks. 
Keywords:  AR-Trees, Random Forest.
Scope of the Article: Classification