APMWMM: Approach to Probe Malware on Windows Machine using Machine Learning
Praveen Tumuluru, Lakshmi Ramani Burra, Muthumala Vishnu Vardhan Reddy, Sampoornamma Sudarsa, Y. Sreeraman, Avuthu Lalith Adithya Reddy · 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · 2022
The popularity of Windows gadgets is growing as well and are more defenseless to malware attacks. This venture proposes a modern imaging strategy to identify malware viably by converting malware parallels/binaries into Byte code and applying machine learning to those bytes code files. With the increased use of the internet, Malware attacks on the system are becoming much more prevalent. Several strategies are attempted; however none of them have been successful in detecting malware which is unknown. To combat various threats, the suggested research used static malware research systems based on machine learning approaches to recognize Windows-based malwares.