Analysis of Behavioral Artifacts of Malware for its Detection using Machine Learning

Malware is created with the express purpose of causing damage to a computer system. Malware may be examined using either static or dynamic analysis techniques. These techniques allow for the precise extraction of unique patterns, which may then be used to identify malware in files. In this research, we propose a behaviorally based approach to identifying malware. Since new malware families and variations are constantly being found on the internet and dark web, they constitute a particularly severe danger. To undo the damage done by malware might be difficult because of the encryption methods it employs. Malware proliferation tracks with AI's rapid development. Because machine learning and deep learning may identify previously unknown risks, its potential use in identifying malware is receiving a lot of attention. This approach yields prediction algorithms that can analyze the actions of malware to unearth new variations and families. There are a total of 3540 rows describing features of corrupted files and 6999 describing properties of uncorrupted files in the dataset. To achieve this, I used the 98.19% accurate Random Forest machine learning method and the 96.77% accurate KKN machine learning algorithm.