Malware Analysis Using Machine Learning and Deep Learning

Malware Analysis Using Machine Learning and Deep Learning

연구 분야: Safety

학회: 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS)

Malware is a severe danger to everyone from home users to huge corporations. As a result, it's a popular research topic. Malware fingerprints and activity patterns are analyzed both statically and dynamically to detect it. Analysis of malware on a daily basis by humans is a tedious task and still we are not ready for zero-day attack malware. Many machine learning methods are currently being utilized to find and analyze malware. The detection and classification accuracy varies based on algorithm used and the dataset taken to perform the testing. New and improved dataset will provide us with more accurate data and then new algorithm can be constructed based on it. The result will be more malware analysis data which can be used for malware detection by antivirus software. Given that Android smartphones are currently the target of the majority of attacks, the field of android malware analysis is expanding. Finding the best method to employ for a generic dataset is the research's ultimate objective, and the tested algorithm should surpass all prior tests on that dataset.