Machine Learning for Threat Detection in Softwares

Abstract

The paper examines the application of machine learning (ML) techniques in the field of cybersecurity with the aim of enhancing threat detection and response capabilities. The initial section of the article provides a comprehensive examination of cybersecurity, highlighting the increasing significance of proactive defensive strategies in response to evolving cyber threats. Subsequently, a comprehensive overview of prevalentonline hazards is presented, emphasizing the imperative for the development of more sophisticated methodologies to detect and mitigate such risks. The primary emphasis of this work is to the practical use of machine learning in the identification and detection of potential dangers inside real-world contexts. This study examines three distinct cases: the detection of malware, attempts to breach security, and anomalous behavior shown by software. Each case study provides a detailed breakdown of the machine learning algorithms and approaches employed, demonstrating their effectiveness in identifying and mitigating risks. The paper further discusses the advantages and disadvantages associated with employing machine learning techniques for threat detection. One advantage of this approach is its ability to facilitatethe examination of extensive datasets, identification of intricate patterns, and prompt decision-making. However, discussions also revolve around difficulties like as erroneous discoveries, adversarial attacks, and concerns over privacy.