Abstract
Phishing is an online unlawful act which takes place when a malicious webpage impersonates as genuine webpage for acquiring confidential details about the user. The phishing attack maintains to acquire a crucial risk factor for web user and annoying threat in the domain of electronic commerce. This study proposes a brain storm optimization (BSO) based association rule mining (ARM) model called BSOARM model to detect of genuine and phishing URLs. Here, BSO algorithm is applied to optimize the rules generated by ARM. The rule attained is deduced to highlight the features which are further common in phishing URLs.To performance of the BSO-ARM model has been tested using a Phishing Dataset. The projected BSO-ARM model has optimized the number of generated rules as 45 and attained maximum accuracy of 86.35%, precision of 81.60%, recall of 86.81% and F-score of 84.13% respectively. These values ensured that the BSO-ARM model has offered better outcomes over the compared methods.
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