Detection of Malware in Large Networks using Deep Auto Encoders

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Published: Jun 10, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6s.6894
Keywords:
feature learning, Deep Auto Encoder, large scale networks, malwares

Main Article Content

K. Janani
Research Scholar,Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India
R. Gunasundari
Professor & Head,Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, India

Abstract

Data mining and machine learning have been heavily studied in recent years with the purpose of detecting sophisticated malware. The majority of these approaches rely on architectures that do not involve deeply enough into the learning process, despite the fact that they have yielded excellent results. This is because deep learning is finding increasing application in both business and academia thanks due to its skills in feature learning. In this paper, we develop a Deep Auto Encoder (DAE) based detection mechanism to detect the malwares crawling in the large scale networks. The DAE act as an unsupervised deep learning model that helps in detecting the malwares. The simulation is conducted on two different datasets to test the robustness of the model. The results show that the proposed method has higher rate of accuracy in detecting the attacks than other methods.

Article Details

How to Cite
Janani, K. ., & Gunasundari, R. . (2023). Detection of Malware in Large Networks using Deep Auto Encoders. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6s), 255–261. https://doi.org/10.17762/ijritcc.v11i6s.6894
Issue
Vol. 11 No. 6s (2023): Advances in Computational Modeling and Simulation of Computing Systems
Section
Articles

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