Abstract
Today, many of devices are connected to internet through networks. Malware (such as computer viruses, trojans, ransomware, and bots) has becoming a critical concern and evolving security threats to the internet users nowadays. To make legitimate users safe from these attacks, many anti-malware software products has been developed. Which provide the major defensive methods against those malwares. Due to rapid spread and easiness of generating malicious code, the number of new malware samples has dramatically increased. There need to take an immediate action against these increase in malware samples which would result in an intelligent method for malware detection. Machine learning approaches are one of the efficient choices to deal with the problem which helps to distinguish malware from benign ones. In this paper we are considering xception model for malware detection. This experiment results shows the efficiency of our proposed method, which gives 98% accuracy with malimg dataset. This paper helps network security area for their efficient works.
Highlights
In the internet age, there are more possibility to take place malicious actions
The rapidity in increase of malware samples or malicious attacks cause the exponential growth in defensive methods
Malware detection methods are Revised Manuscript Received on April 15, 2020. * Correspondence Author
Summary
There are more possibility to take place malicious actions (such as encrypting data, hijacking etc.). The security of computer system become more concern To avoid those attacks many anti-malware defensive methods came up. The rapidity in increase of malware samples or malicious attacks cause the exponential growth in defensive methods. Users needs their security from those attacks which are taking place due those malicious actions. Static analysis and dynamic analysis methods covers all the limitations in the signature methods. Static technique is fast and safest method which are good at analyzing multipath malwares. It has a low level of false positive that shows the analysis having a high accuracy rate and efficient. There comes the Naive Bayesian, Decision Tree, Random Forest, Support Vector Machine methods. by using these methods an accurate malware classification can be achieved
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