Cardinal Correlated Oversampling for Detection of Malicious Web Links Using Machine Learning

The problem with malicious websites is growing day by day as it leads to the black listing of websites. The unauthorized websites are gathering the user’s database information and their assets. Few of the URLs are completely used as a host webpage to publish unrelated web content that signifies cyber-attacks. Cracking the presence of malicious website still pertains as open task due to the lack of web characteristics for malicious and benign websites. To overcome this problem, we are using machine learning techniques for detecting the malicious content and web links. Backgrounding the above, this paper used malicious webpage dataset extracted from UCI dataset repository for predicting the level of mushroom edibility. The categorization of malicious webpage classes is achieved in five ways. Firstly, the dataset consisting of 21 features with 1781 records and is preprocessed with encoding, feature scaling and missing values. Secondly, raw dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed. Thirdly, the cardinality free malicious dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed. Fourth, the correlated free malicious dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed. Fifth, the oversampled malicious dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed with precision, recall, accuracy, running time and F-score. Implementation analysis portrays that the decision tree classifier for raw, Cardinality reduced dataset tends to retain the accuracy with 97.1% before and after feature scaling. The random forest classifier with correlated free dataset tends to retain the 96.6% accuracy before and after feature scaling. Decision tree classifier for oversampled dataset tends to retain the accuracy with 98.4% before and after feature scaling. From the above analysis decision tree classifier is found to be more efficient in its accuracy with all raw, cardinality free and oversampled dataset.