A visualization cybersecurity method based on features' dissimilarity

Phishing attacks on websites are a serious problem that has seen a recent dramatic increase due to the higher volume of online financial transactions and advancements in computer network technology. One of the main challenges with existing intelligent phishing detection approaches is that despite their promising detection rates they do not provide novice users with alerting mechanisms in order to enrich users' experience and knowledge of deceptive techniques. This paper proposes a new anti-phishing technique that not only detects phishing websites accurately, but also offers to novice users an alerting mechanism with rich rules. The key to success in the proposed anti-phishing technique are the features that have been developed by using a hybrid feature analysis. These provide visual cues in the web browser when phishing attacks occur. The rich rules are derived using a fuzzy rule induction approach and they can be utilized by the novice users to understand the security issues of the phishing problem. To evaluate the proposed technique, several experiments have been conducted using feature selection methods and classification algorithms (Furia, SMO, AdaBoost, Naïve Bayes, C4.5) against distinctive feature sets derived from a real phishing dataset. The results show that there are six features, which are not redundant, and when processed using Furia generate effective phishing detection models. More importantly, detection of these features is the basis of an alerting tool for pinpointing possible phishing attacks.