Ransomware Detection Techniques Using Machine Learning Methods
Shuaib Ahmed Wadho, Yichiet Aun, Ming Lee Gan, Chen Kang Lee, Sijjad Ali, Rehan Akbar · 2024
Considering the rising frequency and refinement of ransomware attacks, there is a rising significance for dynamic and successful methods of detection and mitigation. Conventional mark-based approaches frequently demonstrate lacking in recognizing new and developing variations of ransomware. This paper investigates the use of machine learning methods for ransomware detection, expecting to improve the precision and flexibility of detection instruments. It presents a far-reaching examination of different machine learning methods and algorithms, assessing their viability in perceiving ransomware patterns. The discoveries offer important bits of knowledge into the development of cybersecurity arrangements that are stronger and proactive in tending to the dynamic landscape of ransomware threat.