Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection

In Internet applications, due to the growth of big data with more features, intrusion detection has become a difficult process in terms of computational complexity, storage efficiency and getting optimized solutions of classification through existing sequential computing environment. Using a parallel computing model and a nature inspired feature selection technique, a Hadoop Based Parallel Binary Bat Algorithm method is proposed for efficient feature selection and classification in order to obtain optimized detection rate. The MapReduce programming model of Hadoop improves computational complexity, the Parallel Binary Bat algorithm optimizes the prominent features selection and parallel Naïve Bayes provide cost-effective classification. The experimental results show that the proposed methodologies perform competently better than sequential computing approaches on massive data and the computational complexity is significantly reduced for feature selection as well as classification in big data applications.

[1]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[2]  Mohammad Saniee Abadeh,et al.  A hybridization of evolutionary fuzzy systems and ant Colony optimization for intrusion detection , 2010, ISC Int. J. Inf. Secur..

[3]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[4]  Dayong Deng,et al.  Parallel Reducts Based on Attribute Significance , 2010, RSKT.

[5]  V. Venkatachalam,et al.  PERFORMANCE COMPARISON OF INTRUSION DETECTION SYSTEM CLASSIFIERS USING VARIOUS FEATURE REDUCTION TECHNIQUES , 2008 .

[6]  Shi-Jinn Horng,et al.  A novel intrusion detection system based on hierarchical clustering and support vector machines , 2011, Expert Syst. Appl..

[7]  P. Balasubramanie,et al.  Improving the Attack Detection Rate in Network Intrusion Detection using Adaboost Algorithm , 2012 .

[8]  L. Lundberg,et al.  Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making , 2005 .

[9]  Siti Zaiton Mohd Hashim,et al.  BMOA: Binary Magnetic Optimization Algorithm , 2012 .

[10]  Wei Hu,et al.  Network-based intrusion detection using Adaboost algorithm , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

[11]  Xavier Llorà,et al.  Scaling Genetic Algorithms Using MapReduce , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[12]  Kevin D. Seppi,et al.  Parallel PSO using MapReduce , 2007, 2007 IEEE Congress on Evolutionary Computation.

[13]  Andrew H. Sung,et al.  The Feature Selection and Intrusion Detection Problems , 2004, ASIAN.

[14]  Ashwin Srinivasan,et al.  Data and task parallelism in ILP using MapReduce , 2011, Machine Learning.

[15]  Yixian Yang,et al.  A distance sum-based hybrid method for intrusion detection , 2013, Applied Intelligence.

[16]  Dominik Slezak,et al.  Parallel Island Model for Attribute Reduction , 2005, PReMI.

[17]  Emin Anarim,et al.  An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks , 2005, Expert Syst. Appl..

[18]  Natalio Krasnogor,et al.  Nature‐inspired cooperative strategies for optimization , 2009, Int. J. Intell. Syst..

[19]  Wei Hu,et al.  AdaBoost-Based Algorithm for Network Intrusion Detection , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Bernhard Pfahringer,et al.  Winning the KDD99 classification cup: bagged boosting , 2000, SKDD.

[21]  C. Xiang,et al.  Design of mnitiple-level tree classifiers for intrusion detection system , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[22]  Jian Ma,et al.  A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering , 2010, Expert Syst. Appl..

[23]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[24]  Kunle Olukotun,et al.  Map-Reduce for Machine Learning on Multicore , 2006, NIPS.

[25]  Cheng Xiang,et al.  Design of Multiple-Level Hybrid Classifier for Intrusion Detection System , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.

[26]  Jesse Weaver.  A Scalability Metric for Parallel Computations on Large, Growing Datasets (like the Web) , 2012, SSWS+HPCSW@ISWC.

[27]  Chi-Ho Tsang,et al.  Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction , 2005, 2005 IEEE International Conference on Industrial Technology.

[28]  Hany M. Harb,et al.  Adaboost Ensemble with Genetic Algorithm Post Optimization for Intrusion Detection , 2011 .

[29]  T. Revathi,et al.  Minimal complexity attack classification intrusion detection system , 2013, Appl. Soft Comput..

[30]  Li Cheng,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010 .

[31]  Qing He,et al.  Parallel K-Means Clustering Based on MapReduce , 2009, CloudCom.

[32]  Elsayed Radwan,et al.  Intrusion Detection Using Rough Sets based Parallel Genetic Algorithm Hybrid Model , 2009 .

[33]  Witold Pedrycz,et al.  Rough Sets and Knowledge Technology , 2014, Lecture Notes in Computer Science.

[34]  Itzhak Levin,et al.  KDD-99 classifier learning contest LLSoft's results overview , 2000, SKDD.

[35]  Xiaodong Yue,et al.  Parallel attribute reduction algorithms using MapReduce , 2014, Inf. Sci..

[36]  Hossein Nezamabadi-pour,et al.  BGSA: binary gravitational search algorithm , 2010, Natural Computing.

[37]  Jano I. van Hemert,et al.  A generic parallel processing model for facilitating data mining and integration , 2011, Parallel Comput..

[38]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[39]  Ajith Abraham,et al.  Modeling intrusion detection system using hybrid intelligent systems , 2007, J. Netw. Comput. Appl..

[40]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.