Analysis of Traffic Using the Snort Tool for the Detection of Malware Traffic

Abstract

The increasing prevalence of malware threats necessitates the development of robust methods for detecting and mitigating malicious network traffic. This paper presents an analysis of traffic using the Snort tool for the detection of malware traffic. The study focuses on understanding traffic patterns, evaluating Snort's performance, and comparing it with other tools or methods for malware detection. The methodology involves data collection, preprocessing, Snort configuration, and traffic analysis. The results reveal valuable insights into traffic patterns associated with malware activities, demonstrate Snort's effectiveness in detecting known malware signatures, and assess its efficiency and scalability. The comparison with other tools provides a comprehensive understanding of Snort's strengths and limitations. This research contributes to the field of network security by providing practical insights for network administrators and suggesting future research directions.