Malware Analysis Using RNN and Naive Bayes Algorithm

The main thing about the internet age is the protection and coverage from the intruders. The instinct of the security experts will find the malware which is notorious in nature but the case is not similar at all times. Many malwares can intrude a system and even after scanning phase many viruses may exist. So the main part in the detection and analysis of the malware is the scanning phase and identification phase. Moreover in the scanning phase there should be a dynamic approach to predict and detect the malware. This can be achieved by the new emerging technology “Machine learning”. Machine learning provides a good prediction mechanism which will in return give us great results. The Algorithms in machine learning are used to predict the output of an event with various number of ways and accuracies. The Main algorithms and networks are the classification and the sequential data analyzing networks. RNN algorithm and Naive bayes algorithm provide this purpose. As these algorithms are tested separately in the field of analysis the combination of these algorithms will be a boost for the malware analysis. RNN provides a standard model for sequential data analysis and data prediction in sequential order. The Naive Bayes algorithm provides a model for classification of the malware families so that we can easily do things to prevent those families of malwares. As we are using the common name malware we are proposing this solution to the wide range if the malicious softwares