(untitled) (Informatica, 2024)
As cyber attacks become more sophisticated, identifying and mitigating bad domain names has become critical to assuring the security of online environments. This paper presents a framework for detecting malicious domain names using a feature selection strategy and a majority vote method. The suggested methodology begins with the extraction of important features from domain names and their related characteristics, followed by a rigorous feature selection procedure to determine the most discriminating attributes. To accomplish feature selection, a variety of feature selection techniques are used, including chi-square statistics, information gain, gain ratio, and correlation-based feature selection, to analyse the value of each characteristic in distinguishing benign and malicious domain names. In addition, a majority voting strategy is utilised to improve the detection system’s overall accuracy and reliability by combining the predictions of different classifiers such as AdaBoost, logistic regression, k-nearest neighbours, naive bayes, and multilayer perceptron. The ensemble of classifiers is trained on the ideal features, yielding a complete and robust model capable of accurately recognising mali- cious domain names while minimising false positives. The proposed approach is evalu- ated against real-world examples of harmful domain names. The suggested framework employing Chi-square feature selection and majority voting detects malicious domain names with an accuracy of 99.44%, precision of 99.44%, recall of 99.44%, and f-measure of 99.44%. The use of feature selection and a majority voting technique improves the system’s adaptability and resilience in the face emerging cyber threats.