Citation :

Nishchai jayanna Manjula, Srikanth Daggumalli, 2025. "Machine Learning-Based Detection of Malware Threats: A Proactive Approach to Cybersecurity" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 3, Issue 1: 140-148.

Abstract :

With the increasing speed and complexity of cyber attacks malware remains one of the most significant cybersecurity threats faced by organizations, individuals and governments. Traditional signature detection systems struggle to keep pace with evolving zero-day threats, making Machine Learning (ML) a crucial component of modern cybersecurity. With applications in intrusion detection malware analysis fraud prevention and real-time security response systems ML plays a key role in the detection of threats, prevention and incident response. However integrating ML into cybersecurity presents several challenges. The dynamic nature of cyber threats demands regular model updates. At the same time high-quality data, frequent false alarms, vulnerability to attacks and limited resources make its use more difficult. Additionally privacy and ethical concerns related to data collection and monitoring pose significant hurdles. Despite these challenges, ML techniques continue to evolve with advancements in data sharing and privacy regulations driving its responsible use. If these obstacles are effectively addressed ML can provide more adaptive, scalable and efficient cyber security solutions strengthening defense mechanisms against advanced cyber threats.

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Keywords :

Machine Learning, Cybersecurity, Malware, Response, Detection.