Year: 2025
Type: article
Abstract: 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.
Authors: Yash Tiwari, Soheb Ansari, Arman Raja, Ayush Upadhay, Ms. Namita Srivastava
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410889113