Erdos – Renyi Random Graph and Machine Learning Based Botnet Detection
K. Akilandeswari1, L. Baranivel2, G. Anwar Basha3
1Dr. K. Akilandeswari*, Associate Professor, Department of Computer Science, Government Arts College (Autonomous), Salem.
2Mr. L. Baranivel, MCA, Department of M. Tech, VIT, Vellore, India.
3Mr. G. Anwar Basha. Associate Professor, Department of Computer Science, Government Arts College (Autonomous), Salem.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 1-3 | Volume-9 Issue-5, March 2020. | Retrieval Number: E1996039520/2020©BEIESP | DOI: 10.35940/ijitee.E1996.039520
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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: Basically large networks are prone to attacks by bots and lead to complexity. When the complexity occurs then it is difficult to overcome the vulnerability in the network connections. In such a case, the complex network could be dealt with the help of probability theory and graph theory concepts like Erdos – Renyi random graphs, Scale free graph, highly connected graph sequences and so on. In this paper, Botnet detection using Erdos – Renyi random graphs whose patterns are recognized as the number of connections that the vertices and edges made in the network is proposed. This paper also presents the botnet detection concepts based on machine learning.
Keywords: Botnet Detection, Machine Learning, Erdos – Renyi Random Graph.
Scope of the Article: Machine Learning