Improving the CMLM Algorithm by Reducing the High Rate of Indiscernibility Relations Among Website Phishing Objects

Phishing is a common malicious cybercrime in which attackers trick people into revealing their sensitive information. Phishing attacks are serious, as they permit the attacker to perceive and steal the victim’s personal and sensitive information, such as credit card numbers and passwords, in a scam way while the victim is browsing the phishing website. Security experts are responsible for developing and continually improving their algorithms to save the community and guard its information. Traditional security techniques fail to do this effectively; experts are looking for new and robust methods. In this research, we use machine learning (ML) to improve phishing detection tasks. This technology efficiently influences the recognition of hidden patterns in large data inputs. The CMLM algorithm, used for detecting phishing website attacks, suffers from a high rate of indiscernibility relations, which reflect the lack of understanding of the outputs. Additionally, it does not account for imbalanced data. This paper proposes two new versions of the CMLM algorithm that effectively address these issues by integrating methods such as K-means clustering, Stability-correlation and correlation (ScC), rough set (RS) theory, principal component analysis (PCA), decision tree (DT), and deep learning (DL) in a controlled manner. The results show that the proposed methods demonstrate higher accuracy in detecting phishing websites than CMLM, achieving accuracies of 100%, 99.96%, and 99.81% across three key datasets. Compared to CMLM, the improvement margins are 0.47%, 3.08%, and 3.45% for DS1, DS2, and DS3, respectively.