Identification of Peer-to-Peer Botnets in DDOS Attacks
Keywords:
DDOS attack, Botnets,Bot Scanner,IP address.Abstract
A Distributed denial of service (DDOS) is an coordinated attack generated by multiple computer systems attack a target (i.e) a server, or other network resource and cause a Denial of service (DOS)attack for users. A Botnet is a network of computers under the control of a Bot master. Each individual device in a botnet is referred as a bot. The most common application of botnet includes DDOS attack, Data theft and email spam. In existing approaches there is a difficult in testing the algorithm over more datasets, in order to examine the impact on performance of the nature of the system under attack, and the different behaviors of users surfing on the network. In this paper, we propose a Botnet detection technique by using this technique we can able to handle more datasets as well as we can also identify the bots in the network. To do this a bot scanner is used to scan the incoming file of the normal user if the incoming file is a malicious file then the bot scanner blocks that file and trace the IP address in order to identify botnet in the network.
References
- N. Hoque, D. Bhattacharyya, and J. Kalita, Botnet in DDoS attacks: trends and challenges, IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2242-2270, fourth quarter 2015.
- L. Feinstein, D. Schnackenberg, R. Balupari, and D. Kindred,Statistical approaches to DDoS attack detection and response,in Proc. DARPA Information Survivability Conference and Exposition, Washington, DC, USA, Apr. 2003, pp. 303-314.
- Y. Xiang,K. Li,and W. Zhou,Low-rate DDoS attacks detection and traceback by using new information metrics, IEEE Trans. Inf. Forensics and Security, vol. 6, no. 2, pp. 426- 437, Jun. 2011
- Vincenzo Matta,Mario Di Mauro,and Maurizio Longo, DDoS Attacks with Randomized Traffic Innovation: Botnet Identification Challenges and Strategies, IEEE Transaction On Networking, August 2017.
- Abinaya. E,Balamurugan. K,Detecting BOT Victim in Client Networks, IEEE Transaction On Networking ,Oct. 2016.
- Sherish Johri , Novel Method for Intrusion Detection using Data Mining, IEEE Transaction OnNetworking nov.2015.
- Z. Berkay Celik1, Patrick McDaniel1, Rauf Izmailov2, Nicolas and Ananthram Swami3 , Building Better Detection with Privileged Information, IEEE Transaction On Networking april 2011.
- W. Stallings, Cryptography and Network Security: Principles and Prac- tice, 6th ed., Pearson, 2013.
- J. Yuan and K. Mills, Monitoring the macroscopic effect of DDoS flooding attacks, IEEE Trans. Depend. Secure Comput., vol. 2, no. 4, pp. 324-335, Oct. 2005.
- L. Li, J. Zhou, and N. Xiao, DDoS attack detection algorithms based on entropy computing, in Proc. ICICS 2007, Zhengzhou, China, Dec. 2007, pp. 452-466.
- J. Luo, X. Yang, J. Wang, J. Xu, J. Sun, and K. Long, On a mathematical model for low-rate shrew DDoS,IEEE Trans. Inf. Forensics and Security, vol. 9, no. 7, pp. 1069-1083, Jul. 2014.
- V. Matta, and P. Willett,Distributed detection with censoring sensors under physical layer secrecy,vol. 57, no. 5, pp. 1976-1986, May 2009.
- M. Barni and B. Tondi,The source identification game: an information theoretic perspective, vol. 8,no. 3, pp. 450-463, Mar. 2013.
- B. Kailkhura, S. Brahma, B. Dulek, Y. S Han, and P. Varshney,Distributed detection in tree networks: Byzantines and mitigation techniques, vol. 10, no. 7,pp. 1499-1512, Jul. 2015.
- M. Mardani,G. Mateos,and G. B. Giannakis,Dynamic anomalography:tracking network anomalies via sparsity and low rank, vol. 7, no. 1, pp. 50-66, Feb. 2013.
- M. Mardani,G. Mateos,and G. B. Giannakis,Recovery of low-rank plus compressed sparse matrices with application to unveiling traffic anomalies, vol. 59, no. 8, pp. 5186- 5205,Aug. 2013.
- Y. Chen, K. Hwang, and W.-S. Ku, "Collaborative detection of DDoS attacks over multiple network domains," IEEE Trans. Parallel Distrib.Syst., vol. 18, no. 12, pp. 1649-1662, Dec. 2007.
- G. Oke and G. Loukas, "A denial of service detector based on maximum likelihood detection and the random neural network," The Comput. J vol. 50, no. 6, pp. 717-727, 2007.
- M. S. Fallah and N. Kahani, "TDPF: A traceback-based distributed packet filter to mitigate spoofed DDoS attacks," Security Commun.Netw., vol. 7, no. 2, pp. 245-264, 2013.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.