Phishing Website Detection using Machine Learning

Abstract: Phishing attack is one of the commonly known attack where the information from the internet users is stolen by the intruder. The internet users are losses their sensitive information such as Protected passwords, personal information and their transactions to the intruders. The Phishing attack is normally carried by the attackers where the legitimate frequently used websites are manipulated and masked to gather the personal information of the users. The Intruders use the personal information and can manipulate the transactions and get definite from them. From the literature there are various anti-Phishing websites by the various authors. Some of the techniques are Blacklist or Whitelist and heuristic and visual similarity-based methods. In spite of the users using these techniques most of the users are getting attacked by the intruders by means of Phishing to gather their sensitive information. A novel Machine Learning based classification algorithm has been proposed in this paper which uses heuristic features where feature selection can be extracted from the attributes such as Uniform Resource Locator, Source Code, Session, Type of security involve, Protocol used, type of website. The proposed model has been evaluated using five machine learning algorithms such as random forest, Decision Tree, Logistic regression. Out of these models, the random forest algorithm performs better with attack detection accuracy of 92%. More over the Random Forest Model uses orthogonal and oblique classifiers to select the best classifiers for accurate detection of Phishing attacks in the websites.

Keywords: Phishing attack; Personal Machine Learning; Classification Algorithms; Cyber Security.