A Study on Machine Learning and Deep Learning Techniques for Identifying Malicious Web Content

The rapid proliferation of internet usage has led to an exponential increase in cyber threats, particularly malicious websites that can compromise user data and system integrity. Traditional methods of web security are increasingly becoming obsolete, necessitating more dynamic and adaptive approaches. This research paper presents a comprehensive comparative study of Machine Learning (ML) and Deep Learning (DL) techniques for the detection of malicious websites. Utilizing a dataset of over 420,000 web URLs, categorized into various features such as domain, subdomain, and domain suffix, the study aims to evaluate the effectiveness, precision, and computational efficiency of multiple algorithms. Two Convolutional Neural Network (CNN) models were developed and compared against traditional ML algorithms including Decision Trees, Random Forests, AdaBoost, K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Extra Trees, and Gaussian Naive Bayes. The models were rigorously evaluated based on metrics such as accuracy, precision, recall, and F1-score. Preliminary results indicate that CNN models outperform traditional ML algorithms, achieving an accuracy rate of up to 98%, thereby highlighting the potential of DL in cybersecurity applications. Moreover, the study addresses the challenges posed by high cardinality and class imbalance in the dataset. Various data preprocessing techniques were employed to mitigate these issues, including feature engineering and oversampling of minority classes. The research contributes to the field by providing a detailed analysis of each algorithm’s strengths and weaknesses, thereby offering valuable insights into the adaptability and scalability of ML and DL techniques in malicious web detection.