Mitigation of Malware Effect using Cyber Threat Analysis using Ensemble Deep Belief Networks
K. Janani, Research Scholar, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore (Tamil Nadu), India.
Manuscript received on September 02, 2021. | Revised Manuscript received on September 08, 2021. | Manuscript published on September 30, 2021. | PP: 40-46 | Volume-10 Issue-11, September 2021. | Retrieval Number: 100.1/ijitee.K947709101121 | DOI: 10.35940/ijitee.K9477.09101121
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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: Cybersecurity is a technique that entails security models development techniques to the illegal access, modification, or destruction of computing resources, networks, program, and data. Due to tremendous developments in information and communication technologies, new dangers to cyber security have arisen and are rapidly changing. The creation of a Deep Learning system requires a substantial number of input samples and it can take a great deal of time and resources to gather and process the samples. Building and maintaining the basic system requires a huge number of resources, including memory, data and computational power. In this paper, we develop an Ensemble Deep Belief Networks to classify the cybersecurity threats in large scale network. An extensive simulation is conducted to test the efficacy of model under different security attacks. The results show that the proposed method achieves higher level of security than the other methods.
Keywords: Cybersecurity, Deep Learning, Ensemble Deep Belief Network, Attacks.