An intelligent robust malware detection by implementing deep learning

Malicious software (ransom ware) cyber attacks in frequency and severity, posing an increasingly serious threat to computer systems everywhere. Malware detection is a hot study area as several multiple computers, organisations, and governments have been affected by an exponential rise in malware attacks. Dynamic and static assessment of malicious characteristics and behaviour patterns is time expensive and useless in real-time malware detection, according to current technologies. It is becoming increasingly common for malicious apps to use polymorphic and adaptive techniques to rapidly modify their behaviour and develop a number of new malicious apps. In order to undertake an effective malware analysis, machine learning techniques (MLAs) are increasingly being used to create new malware varieties. This approach is time-consuming since it requires considerable feature engineering, learning and representation of features. Moreover the feature extraction process could be effectively eliminated by using advanced MLAs like deep learning. These methods have been shown to perform better with a biased training dataset, which restricts their practical application in real-time scenarios. A new improved approach for successful zero-day malware detection must be developed in order to eliminate biases and analyze these approaches autonomously.&nbsp