Polymorphic Malware Detection by Image Conversion Technique
Arpan Chakraborty1, Krishna Kriti2, Yateendra3, M.S. Bennet Praba4
1Arpan Chakraborty, Pursuing B.Tech, Computer Science and Engineering, SRM Institute of Science andTechnology, Chennai, Tamil Nadu, India.
2Krishna Kriti, Pursuing B.Tech, Computer Science and Engineering, SRM Institute of Science andTechnology, Chennai, Tamil Nadu, India.
3Yateendra, Pursuing B.Tech, Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
4M.S. Bennet Praba, Assistant Professor in SRM Institute of Science and Technology in Ramapuram, Chennai, Tamil Nadu, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2898-2903 | Volume-9 Issue-3, February 2020. | Retrieval Number: B4999129219/2020©BEIESP | DOI: 10.35940/ijeat.B4999.029320
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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: This model implements ways to detect polymorphic malware. This model uses a dynamic approach to detect the polymorphic malware. The objective is to increase the accuracy and efficiency of the detection as this malware can morph themselves, making it difficult to trace through anti-malware systems. As the tracing is going to be difficult the detection and classification system needs to be flexible that can able to detect the malware in every possible environment. This objective can be achieved by giving the system a superintelligence, this can be done by using the Convolutional Neural Networks (CNNs) in our system. This method records the pattern or the traces made by the polymorphic malware. The pattern is in the form of the image which is formed by converting the binary format of the hash codes. The generated images are then sent to the training module, based on this training module the Convolutional Neural Networks gives the result for any testing data.
Keywords: Convolutional Neural Network, Image Processing, Metamorphic viruses