A Robust Machine Learning-Based Mechanism for Detection and Analysis of Malware Attacks

Cybercriminals (hackers) may exploit potential vulnerabilities in a code or system for malicious reasons. As global innovation grows, so does cybersecurity, leading attackers to become more sophisticated. The attackers (hackers) are waiting for a chance to exploit our fault in order to create a backdoor for their malicious code. In light of these obstacles, we must be more attentive and sophisticated than the adversaries. The use of smart gadgets will gradually increase. As a result, we must develop an effective plan for protecting these smart devices. Given these considerations, we present a viable technique for machine learning-based malware attack detection and analysis (abbreviated MLBM-DAM). The proposed MLBM-DAM can detect malware inside any application, file, or system. In MLBM-DAM, we used a variety of machine learning algorithms to identify and analyze malware assaults, including random forest, decision tree, and k-nearest neighbors. The suggested MLBM-DAM’s practical implementation is described, as well as critical performance measures like as precision, recall, and accuracy. In addition, the suggested MLBM-DAM’s performance is compared to that of several competing approaches. The exhibited MLBM-DAM outperformed its current rivals.