Experimental Study on Meta Heuristic Optimization Algorithms for Fake Website Detection

*Asst. Professor, Department of BCA, SS & IT, CMS College of Science & Commerce, Coimbatore, Tamil Nadu, INDIA E-mail: [email protected] **Associate Professor, Department of Computer Science & Engineering., Government College of Technology, Coimbatore, Tamil Nadu, INDIA Abstract: The convenience of online commerce has been embraced by consumers and criminals alike. Phishing, the act of stealing personal information via the internet for the purpose of committing financial fraud, has become a significant criminal activity on the internet. There has been good progress in identifying the threat, educating businesses and customers, and identifying countermeasures. However, there has also been an increase in attack diversity and technical sophistication by the people conducting phishing and online financial fraud. Phishing has a negative impact on the economy through financial losses experienced by businesses and consumers, along with the adverse effect of decreasing consumer confidence in online commerce. This work helps in the detection of phishing websites which is based on Multi-Class Associative Classification optimized with ACO, PSO, BFOA and MBAT algorithms. Rule generation using Associative Classification algorithm over the extracted features of the websites help in creating training data sets to classify their legitimacy. After classification, those results have been optimized ACO,PSO, BFOA & MBAT Algorithms. The experimental results demonstrated and compared the feasibility of using MBAT in real world applications and its better performance.