Malware Detection in Network Traffic using Machine Learning

The ever-present threat of malware proliferation to network security calls for strong and flexible detection mechanisms. The topic of this research paper is the proactive detection of malware in network traffic by means of machine learning techniques. Through the use of a variety of datasets and advanced feature extraction techniques, this research investigates the effectiveness of supervised and unsupervised machine learning models. The intricate details of feature engineering, model selection, and data preprocessing are covered in the paper in order to separate malicious patterns from benign network activity. By conducting extensive validation and testing, the models’ performance is assessed, revealing their potential for real-time malware detection. In the context of network security, the research negotiates the difficulties posed by unbalanced data, adversarial attacks, and the requirement for model interpretability. By means of this thorough analysis, the research makes a valuable contribution to the advancement of malware detection and the development of robust defence mechanisms against dynamic threats in network environments.