Contents & References of Presenting a peer-to-peer (P2P) botnet detection method based on cluster similarity
List:
The first chapter. 1
Definitions and generalities. 1
1-1-Introduction. 2
1-2-The importance and necessity of conducting research. 5
1-3-Aspect of novelty and innovation in research. 5
The second chapter. 6
Review of previous studies. 6
2-1-Introduction. 7
2-2-diagnosis criteria. 7
2-3 levels of detection. 7
2-4-group level. 8
The third chapter. 14
Suggested method. 14
3-1- Proposed method. 15
3-2- Architecture of the proposed method. 16
3-2-1- Data collection: 18
3-2-2- Interpreting packages 20
3-2-3- Structured data. 21
3-2-4- Selection of features 23
3-2-5- Clustering. 23
3-2-6- Detection of new hosts. 24
3-3- Implementation and pseudo code of the proposed method. 24
Chapter Four. 27
Implementation. 27
Evaluation of the proposed method. 28
4-1- The architecture of the evaluation framework. 28
4-2- The results of the proposed method. 30
The fifth chapter. 51
Conclusion. 51
5-1- Conclusion. 52
Source:
[1] Ang-Ning Tan, Michael Steinbach, and Vipin Kumar. 2005. Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Boston, MA, USA.
[2] Choi H. and H. Lee, “Identifying Botnets by Capturing Group Activities in DNS Traffic”, Computer Networks, Vol. 56, pp. 20–33, 2012.
[3] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA.
[4] Gu G., R.Perdisci, J.Zhang, and W.Lee, “BotMiner: Cluster Analysis of NetworkTraffic for Protocol- and Structure-Independent Botnet Detection”, in Proceedings of the 17th USENIX Security Symposium, San Jose, CA, USA, 2008.
[5] Ha Duc T., Yan Guanhua, Eidenbenz, Stephan, Ngo, H.Q. "On the Effectiveness of Structural Detection and Defense Against P2P-based", IEEE dependable systems and networks conference, pp. 297-306, 2009.
[6] Kira, Kenji and Rendell, Larry (1992). The Feature Selection Problem: Traditional Methods and a New Algorithm. AAAI-92 Proceedings.
[7] Livadas, C., Walsh, R., Lapsley, D., Strayer, W.T., "Using Machine Learning Techniques to Identify Botnet Traffic", IEEE Internetwork Research Department BBN Technologies, proceeding 31th IEEE conference, pp. 967–974, 2006. [8] Renato Cordeiro de Amorim and Boris Mirkin. 2012. Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering. Pattern Recognition. 45, 3 (March 2012), 1061-1075. DOI=10.1016/j.patcog.2011.08.012 http://dx.doi.org/10.1016/j.patcog.2011.08.012. [9] Shahrestani, Alireza, Feily, Maryam, Ahmad, Rodina, Ramadass, Sureswaran, "Discovery of Invariant Bot Behavior through Visual Network Monitoring System", IEEE Fourth International Conference on Emerging Security Information, Systems and Technologies, pp. 182-188, 2010.
[10] Sherif Saad, Issa Traore, Ali A. Ghorbani, Bassam Sayed, David Zhao, Wei Lu, John Felix, Payman Hakimian, "Detecting P2P botnets through network behavior analysis and machine learning", Proceedings of 9th Annual Conference on Privacy, Security and Trust (PST2011), July 19-21, 2011. Montreal, Quebec, Canada".
[11] Stinsonand, Elizabeth, C. Mitchell, John, "Characterizing Bots' Remote Control Behavior". Botnet detection countering the largest security threat, edited by Lee, W., Dagon, D., Springer publishing, 2008. [12] Wang K., C. Huang, S. Lin, and Y. Lin, "A fuzzy pattern-based filtering algorithm for botnet detection", Computer Networks, Vol. 55, No. 15. 3275–3286, 2011.
[13] Xiaocong Y., D. Xiaomei, Y. Ge, Q. Yuhai, and Y. Dejun. "Data-Adaptive Clustering Analysis for Online Botnet Detection", in Proceedingd of the 3rd IEEE International Joint Conference on Computational Science and Optimization, Anhui, China, 2010.
[14] Yahyazadeh, M. and M. Abadi,Abadi, “BotOnus: An Online Unsupervised Method for Botnet Detection”, ISeCure, Vol. 4, No. 1, pp. 51–62, 2012.
[15] Yu, X., Dong, X., Yu, Ge, Qin, Yuhai, Yue, D., "Data-adaptive Clustering Analysis for Online Botnet Detection", IEEE Third International Joint Conference on Computational Science and Optimization, Vol. 1, pp. 456-460, 2010.
[16] Zeng, Y., Hu, Xin, G. Shin, K., "Detection of Botnets Using Combined Host- and Network-Level Information". IEEE/IFIP International Conference on Dependable Systems & Networks (DSN), pp. 291-300, 2010. [17] R.C. Amorim, An adaptive spell checker based on PS3M "Improving the clusters of replacement words" in: M. Kurzynski, M. Wozniak (Eds.), Computer Recognition Systems, vol. 3, Springer, Berlin/Heidelberg, 2009, pp. 519–526.
[18] R.C. Amorim, B. Mirkin, J. Gan "A Method for Classifying Mental Tasks in the Space of EEG Transforms". Technical Report BBKS-10-01, Birkbeck University of London, London, 2010.
[19] Y. Chen, M. Rege, M. Dong, J. Hua," Non-negative matrix factorization for semi-supervised data clustering", Knowledge Information Systems 17 (3) (2008) 355-379. [20] C.Y. Tsai, C.C. Chiu,"Developing a feature weight adjustment mechanism for a K-Means clustering algorithm", Computational Statistics and Data Analysis 52 (2008) 4658–4672. [21] J. Fan, M. Han, J. Wang, "Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation", Pattern Recognition 42 (11) (2009) 2527–2540. [22] L. Zhong, Y. Jinsha, Z. Weihua, "Fuzzy C-Mean Algorithm with Morphology Similarity Distance", in: Proceedings of the Sixth International Conference on Fuzzy Systems and Knowledge Discovery 3 (2009) 90–94.