Proceedings of the International Conference on
Software, Knowledge, Information Management and Applications (SKIMA 2013)
Advanced Technology Solutions and Applications in Higher Education and Enterprises
 
(from Chapter 9: Emerging Technologies in Creative Applications)

 Full Citation and Abstract

0 Title: Investigation of peer-to-peer botnet using TCP control packets and data mining techniques
  Author(s): Mohammad Aluthaman, Nauman Aslam, M.A. Hossain, Rafe Alasem
  Address: Department of Computer Science and Digital Technologies, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, NE1-8ST, UK
Department of Computer Science and Digital Technologies, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, NE1-8ST, UK
Department of Computer Science and Digital Technologies, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, NE1-8ST, UK
Department of Electrical Engineering, Faculty of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
mohammad.alauthaman @ northumbria.ac.uk, nauman.aslam @ northumbria.ac.uk, alamgir.hossain @ northumbria.ac.uk, rkasem @ imamu.edu.sa
  Reference: SKIMA 2013 Proceedings  pp. 418 - 426
  Abstract/
Summary
Nowadays botnets are commonly used in cyber-attacks and malicious activities. A botnet is the main way to carry and spread many malicious codes in internet that are responsible for many malicious activities including spam mail, distributed denial of service attack and click fraud. In this paper, we propose an approach to detect botnetís malicious behavior by using data mining classification techniques based on the features of TCP control packet. We study the performance and accuracy of popular classification techniques on existing datasets. Experiment shows that the proposed approach is able to identify botnets with high accuracy rate and high performance in a short time. The evaluation results show that the proposed solution can detect bot hosts with more than 99% accuracy, whereas the average of false positive rate is lower than 2%.
 
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