A Three-Stage Botnet Detection Technique using Random and Obrazom Graphs
K. Akilandeswari1, G.Anwar Basha2, L.Baranivel3
1Dr. K. Akilandeswari*, Associate Professor, Dept of Computer Science, Govt Arts College (Autonomous), Salem.
2Mr.G. Anwar Basha, an Independent Researcher with 10 years of Teaching Experience.
3Mr.L.Baranivel, Pursuing MCA, M. Tech, Research at VIT, Vellore
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 571-576 | Volume-9 Issue-5, March 2020. | Retrieval Number: E1997039520/2020©BEIESP | DOI: 10.35940/ijitee.E1997.039520
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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: This paper proposes three-stage botnet detection technique based on the anomaly and community detection. The first stage is a pragmatic node based distributed approach of sparse graph sequences. The second stage detects the bot from sparse matrix and correlations of interactions among the node. In the third stage, random graph is evaluating the performance of the bots and verified with both odd and even types of nodes. The same is extended and verified through Obrazom triple connected graphs. This verification is helpful to identify the aggressive bots through the optimized pivotal nodes. Machine Learning based Botnet Detection techniques are implemented in various levels like centralized and distributed level of networks. We can apply this three-stage bot detection in large-scale data.
Keywords: Botnets, Random Graphs, Sparse Graph, Social Network and Optimization.
Scope of the Article: Waveform Optimization for Wireless Power Transfer