Malware Detection Using Machine Learning Algorithms

With the exponential growth of internet-connected devices, malware has become a pressing cybersecurity threat. Traditional signature-based methods struggle to detect new or evolving malware, motivating the integration of machine learning (ML) into detection systems. This paper explores the application of various ML algorithms in malware detection, comparing their performance, accuracy, and implementation challenges. A structured approach combining data preprocessing, feature extraction, model training, and evaluation is discussed. Results show that ML-based approaches significantly improve detection accuracy and adaptability against novel threats.