ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Performance Analysis Of An Efficient Framework For Intrusion Detection System Using Data Mining Techniques


Affiliations
1 Department of Computer Science, Bharathidasan University, India
     

   Subscribe/Renew Journal


In the midst of the COVID-19 epidemic crisis, due to the tremendous development of mobile and internet technologies, the excessive growth in cyber-crime makes networksurity a major concern. As a result, individuals and companies are gradually moving towards the use of Intrusion Detection System (IDS), as it plays a persuasive role in monitoring and detecting the traffic of a network. However, high dimensional data affect the performance of IDS by reducing prediction accuracy, increasing false positive rate and classification time. Hence the focus of this research work is to develop a novel framework by integrating Auto – Bi Level (ABL) Classification with Double Filtering Fine Tuning – Ensemble Hybrid (DFFT-EH) feature selection. The experiments are conducted using NSL- KDD a benchmark intrusion detection dataset and it is proved that the proposed framework performs well with good accuracy, less false positive rate and less classification time when compared with voting ensemble classifier and other existing standard algorithms.

Keywords

Auto – Bi Level (ABL) classification, Intrusion Detection System (IDS), Data Mining (DM), Feature Selection, Ensemble
Subscription Login to verify subscription
User
Notifications
Font Size

  • Saurabh Mukherjee and Neelam Sharma, “Intrusion Detection using Naive Bayes Classifier with Feature Reduction”, Procedia Technology, Vol. 4, pp. 119-128, 2012.

  • Shina Sheen and R Rajesh, “Network Intrusion Detection using Feature Selection and Decision Tree Classifier”, Proceedings of IEEE Region Conference on TENCON, pp. 1-4, 2008.

  • Y. Saeys, T. Abeel and Y. Van De Peer, “Robust Feature Selection using Ensemble Feature Selection Techniques”, Proceedings of IEEE Region Conference on Machine Learning and Knowledge Discovery in Databases, pp. 1-13, 2008.

  • T. Hamed, R. Dara and S.C. Kremer, “An Accurate, Fast Embedded Feature Selection for SVMs”, Proceedings of IEEE International Conference on Machine Learning and Applications, pp. 135-140, 2014.

  • Nazrul Hoque, Mihir Singh and Dhruba K. Bhattacharyya, “EFS-MI: An Ensemble Feature Selection Method for Classification”, Complex and Intelligent Systems, Vol. 4, pp. 105-118, 2017.

  • F. Salo, A. Nassif and A. Essex, “Dimensionality Reduction with IG-PCA and Ensemble Classifier for Network Intrusion Detection”, Computer Networks, Vol. 148, pp. 164-175, 2019.

  • S.S. Ahmadi, S. Rashad and H. Elgazzar, “Efficient Feature Selection for Intrusion Detection Systems”, Proceedings of IEEE International Conference on Ubiquitous Computing, Electronics and Mobile Communication, pp. 1029-1034, 2019.

  • E. Karabulut, S. Ozel and T. İbrikci, “A Comparative Study on the Effect of Feature Selection on Classification Accuracy”, Procedia Technology, Vol. 1, pp. 323-327, 2012.

  • M.C. Rekha Preethi and R. Chetan, “Least Square Support Vector Machine based IDS, using Feature Selection Algorithm”, International Journal of Emerging Trends and Technology in Computer Science, Vol. 6, No. 3, pp. 64-68, 2017.

  • S.L. Shiva Darshan and C.D. Jaidhar, “Performance Evaluation of Filter-based Feature Selection Techniques in Classifying Portable Executable Files”, Procedia Computer Science, Vol. 125, pp. 346-356, 2018.

  • M. Cherrington, F. Thabtah, J. Lu and Q. Xu, “Feature Selection: Filter Methods Performance Challenges”, Proceedings of International Conference on Computer and Information Sciences, pp. 1-4, 2019.

  • S.Vanaja and K. Ramesh kumar, “Analysis of Feature Select ion Algorithms on Classification: A Survey”, International Journal of Computer Applications, Vol. 96, No. 17, pp. 28-35, 2014.

  • C. Iwendi, S. Khan, J. Anajemba, M. Mittal, M. Alenezi and M. Alazab, “The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems”, Sensors, Vol. 20, No. 9, pp. 2559-2567, 2020.

  • N. Pham, E. Foo, S. Suriadi, H. Jeffrey and H. Lahza, “Improving Performance of Intrusion Detection System using Ensemble Methods and Feature Selection”, Proceedings of the Australasian Week Multi-Conference on Computer Science, pp. 1-6, 2018.

  • L. Li, Y. Yu, S. Bai, Y. Hou and X. Chen, “An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and $k$ -NN”, IEEE Access, Vol. 6, pp. 12060-12073, 2018.

  • Y. Zhou, G. Cheng, S. Jiang and M. Dai, “Building an Efficient Intrusion Detection System based on Feature Selection and Ensemble Classifier”, Computer Networks, Vol. 174, pp. 1-24, 2020.

  • C. Lin, A. Li and R. Jiang, “Automatic Feature Selection and Ensemble Classifier for Intrusion Detection”, Journal of Physics: Conference Series, Vol. 1856, No. 1, pp. 12067-12078, 2021.


Abstract Views: 199

PDF Views: 1




  • Performance Analysis Of An Efficient Framework For Intrusion Detection System Using Data Mining Techniques

Abstract Views: 199  |  PDF Views: 1

Authors

C. Amali Pushpam
Department of Computer Science, Bharathidasan University, India
J. Gnana Jayanthi
Department of Computer Science, Bharathidasan University, India

Abstract


In the midst of the COVID-19 epidemic crisis, due to the tremendous development of mobile and internet technologies, the excessive growth in cyber-crime makes networksurity a major concern. As a result, individuals and companies are gradually moving towards the use of Intrusion Detection System (IDS), as it plays a persuasive role in monitoring and detecting the traffic of a network. However, high dimensional data affect the performance of IDS by reducing prediction accuracy, increasing false positive rate and classification time. Hence the focus of this research work is to develop a novel framework by integrating Auto – Bi Level (ABL) Classification with Double Filtering Fine Tuning – Ensemble Hybrid (DFFT-EH) feature selection. The experiments are conducted using NSL- KDD a benchmark intrusion detection dataset and it is proved that the proposed framework performs well with good accuracy, less false positive rate and less classification time when compared with voting ensemble classifier and other existing standard algorithms.

Keywords


Auto – Bi Level (ABL) classification, Intrusion Detection System (IDS), Data Mining (DM), Feature Selection, Ensemble

References