New Intrusion Detection Framework Using Cost Sensitive Classifier and Features

New Intrusion Detection Framework Using Cost Sensitive Classifier and Features

Автор: Phyo Thu Thu Khine, Htwe Pa Pa Win, Khin Nwe Ni Tun

Журнал: International Journal of Wireless and Microwave Technologies @ijwmt

Статья в выпуске: 1 Vol.12, 2022 года.

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The huge increase amount of Cyber Attacks in computer networks emerge essential requirements of intrusion detection system, IDS to monitors the cybercriminals. The inefficient or unreliable IDS can decrease the performance of security services and today world applications and make the ongoing challenges on the Cyber Security and Data mining fields. This paper proposed a new detection system for the cyber-attacks with the ensemble classification of efficient cost sensitive decision trees, CSForest classifier and the least numbers of most relevant features are selected as the additional mechanism to reduce the cost. The standard dataset, NSL-KDD, IDS is used to appraise the results and compare the previous existing systems and state-of-the-art methods. The proposed system outperforms the other existing systems and can be public a new benchmark record for the NSL-KDD datasets of intrusion detection system. The proposed combination of choosing the appropriate classifier and the selection of perfect features mechanism can produce the cost-efficient IDS system for the security world.

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CSForest, Cyber Attacks, Cyber Security, Data mining, Feature Selection, Ensemble Classification, Intrusion Detection System, NSL-KDD

Короткий адрес: https://sciup.org/15018383

IDR: 15018383   |   DOI: 10.5815/ijwmt.2022.01.03

Список литературы New Intrusion Detection Framework Using Cost Sensitive Classifier and Features

  • Khraisat, A., Gondal, I., Vamplew, P. et al. Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecur 2, 20 (2019). https://doi.org/10.1186/s42400-019-0038-7
  • Faria M.M., Monteiro A.M. (2019) Intrusion Detection in Computer Networks Based on KNN, K-Means++ and J48. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham.https://doi.org/10.1007/978-3-030-01054-6_19
  • Sandeep Gurung, Mirnal Kanti Ghose, Aroj Subedi,"Deep Learning Approach on Network Intrusion Detection System using NSL-KDD Dataset", International Journal of Computer Network and Information Security(IJCNIS), Vol.11, No.3, pp.8-14, 2019.DOI: 10.5815/ijcnis.2019.03.02
  • Yaser Ghaderipour, Hamed Dinari. " A Flow-Based Technique to Detect Network Intrusions Using Support Vector Regression (SVR) over Some Distinguished Graph Features ", International Journal of Mathematical Sciences and Computing (IJMSC), Vol.6, No.4, pp.1-11, 2020. DOI: 10.5815/ijMSC.2020.04.01
  • Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J.; Alazab, A. Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine. Electronics 2020, 9(1), 173; https://doi.org/10.3390/electronics9010173
  • D P Gaikwad, "Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors", International Journal of Computer Network and Information Security(IJCNIS), Vol.13, No.4, pp.26-34, 2021. DOI: 10.5815/ijcnis.2021.04.03
  • B.A. Manjunatha, Prasanta Gogoi, M. T. Akkalappa, "Data Mining based Framework for Effective Intrusion Detection using Hybrid Feature Selection Approach", International Journal of Computer Network and Information Security(IJCNIS), Vol.11, No.8, pp.1-12, 2019.DOI: 10.5815/ijcnis.2019.08.01
  • Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A., 2009. A detailed analysis of the kdd cup 99 data set, in: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, IEEE. pp. 1–6. doi:10.1109/CISDA.2009.5356528.
  • Krömer, P., Platoš, J., Snášel, V., Abraham, A., 2011. Fuzzy classification by evolutionary algorithms, in: 2011 IEEE International Conference on Systems, Man, and Cybernetics, IEEE. pp. 313–318. doi:10.1109/ICSMC.2011.6083684.
  • M. Mohammadi, B. Raahemi, A. Akbari, and B. Nassersharif, “New classdependent feature transformation for intrusion detection systems”, Secur. Commun. Netw., vol. 5, no. 12, pp. 1296_1311, 2012. https://doi.org/10.1002/sec.403
  • Pervez, M.S., Farid, D.M., 2014. Feature selection and intrusion classification in nsl-kdd cup 99 dataset employing svms, in: The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), IEEE. pp. 1–6. doi:10.1109/SKIMA.2014.7083539.
  • Kanakarajan N.K., Muniasamy K. (2016) Improving the Accuracy of Intrusion Detection Using GAR-Forest with Feature Selection. In: Das S., Pal T., Kar S., Satapathy S., Mandal J. (eds) Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015. Advances in Intelligent Systems and Computing, vol 404. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2695-6_45
  • H. H. Pajouh, R. Javidan, R. Khayami, D. Ali, and K.-K. R. Choo, “A two layer dimension reduction and two-tier classification model for anomaly based intrusion detection in IoT backbone networks”, 2016 IEEE Transactions on Emerging Topics in Computing ( Volume: 7 , Issue: 2 , April-June 1 2019 ). DOI: 10.1109/TETC.2016.2633228
  • Ashfaq, R.A.R.,Wang, X.Z., Huang, J.Z., Abbas, H., He, Y.L., 2017. Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences 378, 484–497. doi:10.1016/j.ins.2016.04.019.
  • Pajouh, H.H., Dastghaibyfard, G. & Hashemi, S. Two-tier network anomaly detection model: a machine learning approach. J Intell Inf Syst 48, 61–74 (2017). https://doi.org/10.1007/s10844-015-0388-x
  • Pham, N.T., Foo, E., Suriadi, S., Jeffrey, H., Lahza, H.F.M., 2018. Improving performance of intrusion detection system using ensemble methods and feature selection, in: Proceedings of the Australasian Computer Science Week Multiconference, ACM. p. 2. doi:10.1145/3167918.3167951.
  • Khraisat A., Gondal I., Vamplew P. (2018) An Anomaly Intrusion Detection System Using C5 Decision Tree Classifier. In: Ganji M., Rashidi L., Fung B., Wang C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science, vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_14
  • Alzubi, Q.M., Anbar, M., Alqattan, Z.N.M. et al. Intrusion detection system based on a modified binary grey wolf optimisation. Neural Comput & Applic (2019). https://doi.org/10.1007/s00521-019-04103-1
  • “TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System”, IEEE Access, Volume 7, 94497 – 94507. DOI: 10.1109/ACCESS.2019.2928048
  • D. Selvamani and V. Selvi, “A Comparative Study on the Feature Selection Techniques for Intrusion Detection System”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.8 No.1, January-March 2019, pp. 42-47.
  • Balasaraswathi, V.R., Sugumaran, M. & Hamid, Y., “Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms”, J. Commun. Inf. Netw. 2, 107–119 (2017). https://doi.org/10.1007/s41650-017-0033-7
  • Zhou, Y., Cheng, G., Jiang, S., Dai, M., “Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier”, Cryptography and Security (cs.CR); Machine Learning (cs.LG). 2 April 2020, 107247, doi:10.1016/j.comnet.2020.107247, doi: arXiv:1904.01352v4
  • Siers M.J., Islam M.Z. (2014) Cost Sensitive Decision Forest and Voting for Software Defect Prediction. In: Pham DN., Park SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science, vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_80
  • M.J.Siers,M.Z.Islam,Software defect prediction using a cost sensitive decision forest and voting and a potential solution to the class imbalance problem, nformation Systems(2015), HYPERLINK "http://dx.doi.org/10.1016/j.is.2015.02.006i" http://dx.doi.org/10.1016/j.is.2015.02.006i
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