Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

Intelligent Feature Extraction through Bigram& Trigram Schemes for Detecting Network Intrusions

Author : T. Augustine 1 P. Vasudeva Reddy 2 P.V.G.D. Prasad Reddy 3

PDF

Date of Publication :24th January 2018

Abstract: In the present scenario of network architecture, handling long payload features is a challenge, specifically because many machine learning algorithms are not able to process these long payload features. Some of the Network Intrusion Detection Systems (NIDS) are completely avoiding these long payload features. To address this challenge, a new methodology called feature extraction through Bigram and Trigram techniques has been proposed. The long payload features are encoded though these proposed techniques and are prepared to be used in machine learning algorithms. Experiments were carried out on ISCX 2012 data set. The designed feature selection based system has shown a noticeable improvement on the performance using different metrics.

Reference :

    1. Shiravi A, Shiravi H, Tavallaee M, Ghorbani AA. Toward developing a systematic approach to generate benchmark datasets for intrusion detection. ComputSecur 2012;31(3):357–74 doi. Available from: http://dx.doi.org/10.1016/j.cose.2011.12.012, http://http: //www. science direct.com/science/article/ pii/ S01674 048110 01672.
    2. Garcia LP, de Carvalho AC, Lorena AC. Effect of label noise in the complexity of classification problems. Neurocomputing 2015;160:108–19. Available from: http://dx.doi.org/10.1016/j.neucom.2014.10.085, http://www.sciencedirect.com/science/article/pii/S09252312150 01241.
    3. Beigi EB, Jazi HH, Stakhanova N, Ghorbani AA. Towards effective feature selection in machine learningbased botnet detection approaches, in: 2014 IEEE Conference on Communications and Network Security; 2014, pp. 247–255. doi:10.1109/ CNS.2014.6997492.
    4. Bolon-Canedo V, Snchez-Maroo N, Alonso-Betanzos A, Bentez J, Herrera F. A review of microarray datasets and applied feature selection methods. Inf Sci (Ny) 2014;282:111–35. Available from: http://dx. doi.org /10.1016 /j.ins.2014.05.042, http://www. sciencedirect .com/science /article/pii/ S0020025514006021.
    5. Beniwal S, Arora J. Classification and feature selection techniques in data mining. Int J Eng Res Technol 2012;1(6):1– 6.
    6. Fahad A, Tari Z, Khalil I, Habib I, Alnuweiri H. Toward an efficient and scalable feature selection approach for internet traffic classification. ComputNetw 2013;57(9):2040–57. Available from: http:// dx.doi. org/10. 1016/j.comnet. 2013.04.005, http://http:/ /www. science direct.com/science/article/pii/ S1389128613001163.
    7. Aghdam MH, Kabiri P. Feature selection for intrusion detection system using ant colony optimization. IJ NetwSecur 2016;18(3):420–32.
    8. Bolón-Canedo V, Sánchez-Maroño N, AlonsoBetanzos A. A review of feature selection methods on synthetic data. KnowlInfSyst 2013;34(3):483–519. doi:10.1007/s10115-012-0487-8.
    9. Sahu SK, Sarangi S, Jena SK. A detail analysis on intrusion detection datasets, in: 2014 IEEE International Advance Computing Conference (IACC), 2014, pp. 1348–1353. doi:10.1109/IAdCC.2014.6779523.
    10. Mancini LV, Di Pietro R. Intrusion Detection Systems. Springer; 2008
    11. Ambusaidi MA, He X, Nanda P, Tan Z. Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans Comput 2016;65(10):2986–98. doi:10.1109/TC.2016.2519914.
    12. Abou El Kalam A., Gad El Rab M., and Deswarte Y. (2014), A model-driven approach for experimental evaluation of intrusion detection systems, Security Comm. Networks, 7, pages 1955–1973, doi: 10.1002/sec.911
    13. Mell P, Hu V, Lipmann R, Haines J, Zissman M. An overview of issues in testing intrusion detection systems. Technical Report, NIST IR 7007, National Institute of Standard and Technology, USA, 2003.
    14. NiccolòCascarano, Luigi Ciminiera, and FulvioRisso. 2010. Improving cost and accuracy of DPI traffic classifiers. In Proceedings of the 2010 ACM Symposium on Applied Computing (SAC '10). ACM, New York, NY, USA,641-646. DOI= http://dx.doi. org/10. 1145 /1774088.1774223
    15. Laurent Bernaille , Renata Teixeira , Ismael Akodkenou , Augustin Soule , KaveSalamatian, Traffic classification on the fly, ACM SIGCOMM Computer Communication Review, v.36 n.2, April 2006 [doi>10.1145/1129582.1129589]
    16. TarfaHamed , Rozita Dara, Stefan C. Kremer, Network intrusion detection system based on recursive feature addition and bigram technique, computers & s e c u r i t y 73 ( 2 0 1 8 ) 137–155
    17. Zhang M, Wang L, Jajodia S, Singhal A, Albanese M. Networkdiversity: a security metric for evaluating the resilience ofnetworks against zero-day attacks. IEEE Trans Inf ForensicsSec 2016;11(5):1071–86. doi:10.1109/TIFS.2016.2516916.
    18. Intrusion detection evaluation dataset (ISCXIDS2012), http://unb.c a/cic/res earch /data sets /index.html
    19. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intelligent Syst Technol 2011;2(3):27.Debar H. An introduction to intrusiondetection systems, in: Proceedings of Connect2000; 2002, pp. 1–18.
    20. Open Source, freely available and downloadable from: https: //www .cs. Waikato .ac. nz/ ml/ weka /down loading.html

Recent Article