Authors: Ashu Gulia, Sangeeta Rani, Monika Saini
Abstract: In the realm of cybersecurity, the continuous evolution and sophistication of malware pose significant challenges to the detection and mitigation of cyber threats. This research paper delves into the analysis of anomaly detection of malware using Support Vector Machines (SVM), a powerful machine learning algorithm known for its effectiveness in classification tasks. By leveraging SVM for anomaly detection, this study aims to explore the potential of SVM in identifying malicious behavior patterns that deviate from normal system activities. The paper provides insights into implementing SVM-based anomaly detection for malware, including data preprocessing, feature extraction, model training, and evaluation. Furthermore, the research investigates the performance of SVM in detecting various types of malware and assesses its effectiveness in real-world scenarios. Through a comprehensive analysis, this paper contributes to the understanding of SVM-based anomaly detection techniques for malware. It provides valuable insights into the efficacy and limitations of SVM in combating cyber threats.
