Revealing the Feature Influence in HTTP Botnet Detection

Botnet are identified as one of most emerging threats due to Cybercriminals work diligently to make most of the part of the users’ network of computers as their target. In conjunction to that, many researchers has conduct a lot of study regarding on the botnets and ways to detect botnet in network traffic. Most of them only used the feature inside the system without mentioning the feature influence in botnet detection. Selecting a significant feature are important in botnet detection as it can increase the accuracy of detection. Besides, existing research focusses more on the technique of recognition rather than uncovering the purpose behind the selection. Therefore, this paper will reveal the influence feature in botnet detection using statistical method. The result obtained showed the accuracy is about 91% which is approximately acceptable to use the influence feature in detecting botnet activity.

[1]  Haidar Osman,et al.  Automatic feature selection by regularization to improve bug prediction accuracy , 2017, 2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE).

[2]  Chia-Mei Chen,et al.  Web botnet detection based on flow information , 2010, 2010 International Computer Symposium (ICS2010).

[3]  Ram Sarkar,et al.  Breast cancer detection using feature selection and active learning , 2017 .

[4]  Verónica Bolón-Canedo,et al.  Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset , 2011, Expert Syst. Appl..

[5]  Jens Myrup Pedersen,et al.  An efficient flow-based botnet detection using supervised machine learning , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[6]  Nikhil R. Pal,et al.  Feature Selection With Controlled Redundancy in a Fuzzy Rule Based Framework , 2018, IEEE Transactions on Fuzzy Systems.

[7]  Chun-Ying Huang,et al.  Effective bot host detection based on network failure models , 2013, Comput. Networks.

[8]  M. Wilscy,et al.  Using entropy of traffic features to identify bot infected hosts , 2013, 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS).

[9]  R. Divya,et al.  Multiple time series clinical data with frequency measurement and feature selection , 2016, 2016 IEEE International Conference on Advances in Computer Applications (ICACA).

[10]  Kristin L. Sainani,et al.  Logistic Regression , 2014, PM & R : the journal of injury, function, and rehabilitation.

[11]  Hossein Rouhani Zeidanloo,et al.  Botnet detection based on traffic monitoring , 2010, 2010 International Conference on Networking and Information Technology.

[12]  Ali A. Ghorbani,et al.  Detecting P2P botnets through network behavior analysis and machine learning , 2011, 2011 Ninth Annual International Conference on Privacy, Security and Trust.

[13]  Philip Hingston,et al.  A Statistical Rule Learning Approach to Network Intrusion Detection , 2015, 2015 5th International Conference on IT Convergence and Security (ICITCS).

[14]  Ali A. Ghorbani,et al.  Towards effective feature selection in machine learning-based botnet detection approaches , 2014, 2014 IEEE Conference on Communications and Network Security.

[15]  Futai Zou,et al.  Detecting HTTP Botnet with Clustering Network Traffic , 2012, 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing.

[16]  Rui Sousa,et al.  Analyzing the behavior of top spam botnets , 2012, 2012 IEEE International Conference on Communications (ICC).

[17]  Mingteh Chen,et al.  The Analysis and Identification of P2P Botnet's Traffic Flows , 2011, Int. J. Commun. Networks Inf. Secur..

[18]  Ali Feizollah,et al.  A Study Of Machine Learning Classifiers for Anomaly-Based Mobile Botnet Detection , 2013 .

[19]  Daria S. Lavrova,et al.  Applying Correlation and Regression Analysis to Detect Security Incidents in the Internet of Things , 2015 .

[20]  Gavin Brown,et al.  Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection , 2012, J. Mach. Learn. Res..

[21]  Syed Ali Khayam,et al.  A Taxonomy of Botnet Behavior, Detection, and Defense , 2014, IEEE Communications Surveys & Tutorials.

[22]  Benoit Claise,et al.  Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information , 2008, RFC.

[23]  Xiapu Luo,et al.  Detecting stealthy P2P botnets using statistical traffic fingerprints , 2011, 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN).

[24]  G. Kirubavathi Venkatesh,et al.  HTTP Botnet Detection Using Adaptive Learning Rate Multilayer Feed-Forward Neural Network , 2012, WISTP.

[25]  Nitin K. Singh,et al.  Sparse feature selection for classification and prediction of metastasis in endometrial cancer , 2017, BMC Genomics.

[26]  Aderemi Oluyinka Adewumi,et al.  Efficient Feature Selection Technique for Network Intrusion Detection System Using Discrete Differential Evolution and Decision , 2017, Int. J. Netw. Secur..

[27]  Hamidreza Zareipour,et al.  A New Feature Selection Technique for Load and Price Forecast of Electrical Power Systems , 2017, IEEE Transactions on Power Systems.

[28]  Wanlei Zhou,et al.  Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics , 2011, IEEE Transactions on Information Forensics and Security.

[29]  Richard E. Overill,et al.  A Statistical Approach for Discovering Critical Malicious Patterns in Malware Families , 2015 .

[30]  Ali A. Ghorbani,et al.  A statistical approach to botnet virulence estimation , 2011, ASIACCS '11.

[31]  Tao Lu,et al.  Adaptive feature selection based on local descriptor distinctive degree for vehicle retrieval application , 2017, 2017 IEEE International Conference on Consumer Electronics (ICCE).

[32]  Ali A. Ghorbani,et al.  Botnet detection based on traffic behavior analysis and flow intervals , 2013, Comput. Secur..

[33]  Maryam Var Naseri,et al.  Periodicity classification of HTTP traffic to detect HTTP Botnets , 2015, 2015 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).