Comparison of intrusion detection system based on feature extraction

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    In network traffic classification redundant feature and irrelevant features in data create problems. All such types of features time-consuming make slow the process of classification and also affect a classifier to calculate accurate decisions such type of problem caused especially when we deal with big data. In this paper, we compare our proposed algorithm with the other IDS algorithm.

     

     


  • Keywords


    IDS; Big Data; Feature Selection; Spark

  • References


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      [2] Mohay, George M. Computer and intrusion forensics. Artech House, 2003.

      [3] Nguyen, Hai Thanh, Katrin Franke and Slobodan Petrovic. "Feature Extraction Methods for Intrusion Detection Systems." Threats, Countermeasures, and Advances in Applied Information Security. IGI Global, 2012. 23-52. Web. 13 Feb. 2018. doi:10.4018/978-1-4666-0978-5.ch002

      [4] Jupriyadi and A. I. Kistijantoro, "Vitality based feature selection for intrusion detection," 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), Bandung, 2014, pp. 93-96.

      [5] M. Panda, A. Abraham and M. R. Patra, "Discriminative multinomial Naïve Bayes for network intrusion detection," 2010 Sixth International Conference on Information Assurance and Security, Atlanta, GA, 2010, pp. 5-10.

      [6] M. Z. Alom, V. Bontupalli and T. M. Taha, "Intrusion detection using deep belief networks," 2015 National Aerospace and Electronics Conference (NAECON), Dayton, OH, 2015, pp. 339-344.

      [7] Heba F. Eid , Mostafa A. Salama , boul Ella Hassanien , Tai-hoon Kim Bi-Layer Behavioral-Based Feature Selection Approach for Network Intrusion Classification” , International Conference on Security Technology SecTech 2011: Security Technology pp 195-203

      [8] Eid H.F., Hassanien A.E., Kim T., Banerjee S. (2013) Linear Correlation-Based Feature Selection for Network Intrusion Detection Model. In: Awad A.I., Hassanien A.E., Baba K. (eds) Advances in Security of Information and Communication Networks. Communications in Computer and Information Science, vol 381. Springer, Berlin, Heidelberg.

      [9] Laxkar P., Chakrabarti P.,Ghosh A. and Panwar P., “An effective Intrusion Detection System Using Machine Learning Library of Spark”, International Journal of Emerging Technology and Advanced Engineering, 8(2),pp.48-52,2018


 

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Article ID: 14829
 
DOI: 10.14419/ijet.v7i2.33.14829




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