Malware Detection using Deep Learning Methods

Rapid development of the internet leads the malware to become one of the most significant threads nowadays. Malware, is any kind of program or file which would adversely affect the computer users in a harmful way. Malware exist in different forms which includes worms, viruses in computer, Trojan horses, etc. These malicious contents can degrade the overall performance of the system. It includes activities like stealing, encrypting or deleting sensitive data, etc. without the consent of the user. Malware detection is a milestone in the field of computer security. For detecting malware many methods have been evolved. Researchers are mainly concentrated in malware identification methods based on machine learning. Malware can be detected in two ways. They are static approach and dynamic approach. This paper mainly deals with the current challenges faced by malware detection methods and also explores a categorized new method in machine learning. The methods discussed here are combined static and dynamic approach, random forest, Bayes classification. This work will help in cyber security area and also which will help the researchers to do efficient researches.