">Detection and classification of malicious software using machine learning and deep learning
Author(s):
Bushra Fatima, K.Balakrishna Maruthiram
Keywords:
API calls, registry modifications, and file operations are extracted and utilized within the malware detection system.
Abstract
Malicious software is designed to intentionally disrupt computer systems. It can be analyzed using either static or dynamic techniques, which help to identify unique patterns essential for accurate malware detection. Over the past decade, numerous methods have been proposed to detect malware, often emphasizing threats that target networks. For example, DDoS attacks represent a prevalent risk in network security, overwhelming devices with excessive requests and thereby blocking legitimate access. Software vulnerabilities present another significant security concern, capable of compromising entire systems, stealing information, altering data, denying service, and damaging devices. This paper focuses on dynamic analysis to develop a malware detection system using machine learning techniques. It introduces a behavior based approach to malware detection. The methodology involves setting up a dynamic analysis environment and running malware samples through classification algorithms. Various behavioral indicators such as PSI.
Article Details
Unique Paper ID: 166800 Publication Volume & Issue: Volume 11, Issue 2 Page(s): 1812 - 1816
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