Hybrid Machine Learning Approach for Malware Analysis

Beyond traditional signature-based approaches, additional analytic techniques are required due to the increasing complexity and diversity of malware threats.This research presents a machine learning (ML)-based technique designed to improve the precision and effectiveness of malware analysis. The suggested methodology uses feature extraction, dataset curation, and model deployment to improve malware detection capabilities by utilizing machine learning methods. The solution that is being suggested has the ability to extract all features from malware samples, including both dynamic and static properties like API calls and file structures. These characteristics provide a solid framework for further investigation. It also highlights how crucial dataset preparation and selection are. The correct dataset, which is pre-processed to assure model performance, strikes a balance between representation and diversity by utilizing a variety of sources, including synthetic samples and real-world data. Malware detection uses a set of algorithms namely including K-Nearest Neighbor, Support Vector Machines, and Naive Bayes. Here, the two powerful tools are used from Kali Linux distribution Nikto and Metasploit, to enhance the state-of-the-art malware analysis process. By incorporating these methodologies into the proposed framework, the effectiveness of malware analysis.