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Statistics report
Feb. 2023
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 119
Paper Published : 1427
No. of Authors : 3631
  Journal Paper


Paper Title :
Enhancing HTTP Botnet Identification and Analysis using Data Mining Algorithm

Author :Tirth Mehta, Anil Suthar

Article Citation :Tirth Mehta ,Anil Suthar , (2021 ) " Enhancing HTTP Botnet Identification and Analysis using Data Mining Algorithm " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 27-31, Volume-9,Issue-7

Abstract : Abstract - In present scenario the different malware attack botnet is an extensive threat attack which is famous cyber-attack over all systems. Many more group of connected computers has been promisingly controlled by the BOT. Master to perform various type of attack like DDOS, Fraud Like click Fraud etc. the main reason of the becoming the famous tool for perform different attack is his Command-and-Control channel connected to the computer systems and handle them. In the recent trend botnet has expanded in to supplementary like HTTP Botnet. In this research paper we are proposed the different association rule mining technique, it allow us robotize detecting similarity from the huge amount of the network flow and determining the bot traffic and non bot traffic. Our proposed approaches achieved highest accuracy 85.45% of detecting the bot from the network flow traffic with the timestamp dataset. Keywords - Botnet Identification, Association Rule Mining Technique, Command-and-Control channel, UDP DDOS, TCP DDOS.

Type : Research paper

Published : Volume-9,Issue-7


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-18094   View Here

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