An Effective Method for Predicting Malware Family
Nourin N.S1, Sulphikar A2
1Nourin N.S*, Department, Name of the affiliated College or University/Industry, City, Country.
2Sulphikar A, department, Name of the affiliated College or University/Industry, City, Country.
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1230-1233 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8510049420/2020©BEIESP | DOI: 10.35940/ijeat.D8510.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Convolution Neural Network, Machine Learning, Malware Detection.