Usage Patterns and Implementation of Machine Learning for Malware Detection and Predictive Evaluation

Researchers have been studying malware detection and predictive analysis for more than ten years due to vast growing of media and network vulnerabilities. Intrusion detection systems and packet capture tools have been used for a long time to monitor servers and conduct forensic investigation of network infrastructure (IDS). With the concluding comments, this paper highlights the many elements of malware detection and avoidance methods. Deep learning based on malware datasets collected from actual honeypots and honeynets is used in the proposed study to design and execute an innovative and successful deep learning-based method. In order to train and forecast malware on various criteria, such as error factor, accuracy rate, and overall performance, the suggested artificial neural net-based multilayered method is employed. Density-based clustering has been utilised in the past to identify malware and make predictions, and the resulting accuracy is up to 97 percent. Even when utilising the previous method, the highest accuracy of 97 percent was less than our method’s maximum accuracy of 100 percent for several growing datasets.