Intrusion detection system using hybrid binary PSO and K-nearest neighborhood algorithm

At this time computer networks have been widely used to exchange confidential data information between server to mobile devices and desktop. Although there are several security methods such as access control, data encryption and the use of hardware or software as a firewall, unauthorized access through computer networks to obtain confidential data information is increasing. In this paper, we propose an intrusion detection mechanism based on binary particle swarm optimization (PSO) and k-nearest-neighbor (k-NN) algorithms. Preliminary experiments show that our approach successfully increased up to 2% of accuracy generated by k-nearest-neighbor (k-NN) algorithms.

[1]  Yun Ling,et al.  Application of the PSO-SVM Model for Credit Scoring , 2011, 2011 Seventh International Conference on Computational Intelligence and Security.

[2]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[4]  Gisung Kim,et al.  A novel hybrid intrusion detection method integrating anomaly detection with misuse detection , 2014, Expert Syst. Appl..

[5]  Jaideep Srivastava,et al.  A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection , 2003, SDM.

[6]  Salvatore J. Stolfo,et al.  Data Mining Approaches for Intrusion Detection , 1998, USENIX Security Symposium.

[7]  A. Kohn.  [Computer viruses]. , 1989, Harefuah.

[8]  Farrukh Aslam Khan,et al.  Network intrusion detection using hybrid binary PSO and random forests algorithm , 2015, Secur. Commun. Networks.

[9]  Bhushan H. Trivedi,et al.  Reducing Features of KDD CUP 1999 Dataset for Anomaly Detection Using Back Propagation Neural Network , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

[10]  H. Javitz,et al.  Detecting Unusual Program Behavior Using the Statistical Component of the Next-generation Intrusion Detection Expert System ( NIDES ) 1 , 1997 .

[11]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[12]  Peter Mell,et al.  NIST Special Publication on Intrusion Detection Systems , 2001 .

[13]  B. Walczak,et al.  Particle swarm optimization (PSO). A tutorial , 2015 .

[14]  Mohammad Zulkernine,et al.  A hybrid network intrusion detection technique using random forests , 2006, First International Conference on Availability, Reliability and Security (ARES'06).

[15]  Sushil Jajodia,et al.  ADAM: Detecting Intrusions by Data Mining , 2001 .

[16]  Chih-Fong Tsai,et al.  CANN: An intrusion detection system based on combining cluster centers and nearest neighbors , 2015, Knowl. Based Syst..

[17]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[18]  Lam For Kwok,et al.  IDS False Alarm Filtering Using KNN Classifier , 2004, WISA.

[19]  Sung-Bae Cho,et al.  Efficient anomaly detection by modeling privilege flows using hidden Markov model , 2003, Comput. Secur..

[20]  Adel Nadjaran Toosi,et al.  A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers , 2007, Comput. Commun..

[21]  Chun-Hung Richard Lin,et al.  Intrusion detection system: A comprehensive review , 2013, J. Netw. Comput. Appl..

[22]  V. Rao Vemuri,et al.  Use of K-Nearest Neighbor classifier for intrusion detection , 2002, Comput. Secur..

[23]  Ludmil Mikhailov,et al.  Towards the Development of OMNIVORE: An Evolving Intelligent Intrusion Detection System , 2007, SGAI Conf..