Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

Because of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms.

[1]  Andreas Terzis,et al.  A multifaceted approach to understanding the botnet phenomenon , 2006, IMC '06.

[2]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[3]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[4]  Sureswaran Ramadass,et al.  A Survey of Botnet and Botnet Detection , 2009, 2009 Third International Conference on Emerging Security Information, Systems and Technologies.

[5]  Mehdi R. Zargham,et al.  A self-organizing map and its modeling for discovering malignant network traffic , 2009, 2009 IEEE Symposium on Computational Intelligence in Cyber Security.

[6]  Kuan-Cheng Lin,et al.  CSO-based feature selection and parameter optimization for support vector machine , 2009, 2009 Joint Conferences on Pervasive Computing (JCPC).

[7]  Wanmin Wu,et al.  Quality of experience evaluation of voice communication: an affect-based approach , 2011, Human-centric Computing and Information Sciences.

[8]  Guandong Xu,et al.  Improving Recommendations by the Clustering of Tag Neighbours , 2012 .

[9]  D. K. Lobiyal,et al.  A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks , 2012, Human-centric Computing and Information Sciences.

[10]  Shuzong Wang,et al.  A Hybrid of Artificial Fish Swarm Algorithm and Particle Swarm Optimization for Feedforward Neural Network Training , 2007 .

[11]  Heejo Lee,et al.  Identifying botnets by capturing group activities in DNS traffic , 2012, Comput. Networks.

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Tao Liu,et al.  Feature Optimization Based on Artificial Fish-Swarm Algorithm in Intrusion Detections , 2009, 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing.

[14]  Mohamed Abdel Fattah,et al.  The Use of MSVM and HMM for Sentence Alignment , 2012, J. Inf. Process. Syst..

[15]  Ruchika Malhotra,et al.  Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality , 2012, J. Inf. Process. Syst..

[16]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[17]  Li Xiao,et al.  An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm , 2002 .

[18]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[19]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[20]  Mita Nasipuri,et al.  Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Naïve Bayes, and Artificial Neural Networks , 2012, J. Inf. Process. Syst..

[21]  W. Timothy Strayer,et al.  Botnet Detection Based on Network Behavior , 2008, Botnet Detection.

[22]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[23]  Chun-Ying Huang,et al.  A fuzzy pattern-based filtering algorithm for botnet detection , 2011, Comput. Networks.

[24]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[25]  Saeed Farzi.  Efficient Job Scheduling in Grid Computing with Modified Artificial Fish Swarm Algorithm , 2009 .

[26]  Alex Pappachen James,et al.  Ranked selection of nearest discriminating features , 2012, Human-centric Computing and Information Sciences.

[27]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[28]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.