Malware Detection using ANN(Malviz.AI)
Vasu Sethia1, Shivam Kataria2, Jeyasekar A3
1Vasu Sethia*, B.Tech,SRM Institute of Science and Technology, Chennai, India.
2Shivam Kataria, B.Tech,SRM Institute of Science and Technology, Chennai, India.
3Jeyasekar A, Associate Professor, CSE,SRM Institute of Science and Technology, Chennai, India.
Manuscript received on April 09, 2020. | Revised Manuscript received on April 12, 2020. | Manuscript published on April 30, 2020. | PP: 2418-2423 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8025049420/2020©BEIESP | DOI: 10.35940/ijeat.D8025.049420
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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: With an increase in the number of internet users, the number of cyber-attacks happening in organizations is increasing day by day. Most of the cyber-attacks involve the use of malicious software known as malware to steal personal information, gain unauthorized access to the computer systems and carry out malicious activities which can cause huge financial losses to the organizations. Viruses, worms, rootkits, adware or anything that performs malicious activities is classified as malware. Detecting malware is a major challenge faced by the anti-malware industry as the signature-based malware detection methods may not provide accurate detection of malware. In this paper, an artificial neural network approach for malware detection is presented to overcome the shortcomings of signature-based malware detection methods. The proposed method can be used as a base model for the malware detection process and can be further developed to enhance the functionality.
Keywords: Malware, Artificial Neural Network, Machine learning, Network security, Malware Detection, Windows PE