Models Comparison Based On Intrusion Detection Using Machine Learning


  • Maimuna Yusuf Ma’aji Department of Computer Science, Umaru Musa Yar’adua University Katsina, Nigeria
  • Muhammad Sirajo Aliyu Department of Computer Science, Faculty of Computing, Federal University Dutse, Jigawa State


Data Mining, Intrusion Detection, KDD CUP 99, Network security


Network security is the greatest challenges in our current generation. Network intrusion detection (NID) is one of the fundamental techniques used to protect computer networks from threads. However, there is contemplation on the possibility and sustainability of the traditional approaches employed especially with the current modern networks. Recently, majority of the researchers employ the application of machine learning models such as logistic regression (LR), random forest (RF), K-nearest neighbor (KNN) and Support Vector Machines (SVM) among others to address the NID problem. In this research, the Network intrusion classification system has been analyzed based on KDD Cup ’99 data set. In the classifier implementation section, the three most widely used models were employed; K-nearest neighbor, random forest and logistic regression as our classification algorithms. The attack detection accuracy and training time was used to evaluate the models. The random forest provides the best detection accuracy of 99.5% and 74 second training time provide by logistic regression.


Abraham T. (2001). IDDM: Intrusion Detection Using Data Mining Techniques, Technical report DSTO electronics and surveillance research laboratory, Salisbury, Australia.

Agarwal R., Joshi M. and V. (2000). A New Framework for Learning Classifier Models in Data Mining”, Tech. Report, Dept. of Computer Science, University of Minnesota.

Aggarwal P. and Sharma S.K. (2015). An Empirical Comparison of Classifiers to Analyze Intrusion Detection, Proc. of Fifth International Conference an Advanced Computing and Communication Technologies.

Akashdeep Sharma ,Ishfaq Manzoor, Neeraj Kumar, (2017). A Feature Reduced Intrusion Detection System Using ANN Classifier, Expert Systems With Applications

Arman Tajbakhsh, Mohammad Rahmati, and Abdolreza Mirzaei (2008) Intrusion detection using fuzzy association rules, Applied Soft Computing ASOC509, Elsevier B.V.

Chen M.S., Han J and Yu Philip S. (1996). Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, vol.8, No.6, pp.866-883.

Chihab Y., Ouhman A., Erritali m. and Ouahidi B. (2013)," Detection & Classification of Internet Intrusion Based on the Combination of Random Forest and Naïve Bayes, International Journal of Engineering and Technology (IJET).

Christine Dartigue, Hyun IK Jang and Wenjun Zeng (2009). A New data-mining based approach for network Intrusion detection, Proc. of Seventh Annual Communication Networks and Services Research Conference, pp.372-377.

Daniel B., Couto J., Jajodia S. and Wu N. (2001). ADAM: A Test Bed for Exploring the Use of Data Mining in Intrusion Detection”, SIGMOD, vol30, no.4, pp: 15-24.

Duan L. and Xiao Y. (2018). An Intrusion Detection Model Based on Fuzzy C-means Algorithm, 8thInternational Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, pp. 120-123.

Foster Provost and Tom Fawcett (2000). Robust Classification for Imprecise Environment, pp.1-38, Kluwer Academic Publishers.

Gupta KK, Nath B. and Kotagiri R. (2010). Layered Approach Using Conditional Random Fields for Intrusion Detection,” IEEE Transactions on Dependable and Secure Computing, vol. 7, no. 1, pp. 35–49.

Gupta D., Singhal S, Malik S. and Singha. (2016). Network intrusion detection system using various datamining techniques, IEEE publication.

Kabir, E., Hu, J. Wang H. and Zhuo G. (2017). A novel statistical technique for intrusion detection systems, Future Generation Computer Systems

Keerthika G. and Priya D. S. (2015). Feature Subset Evaluation and Classification using Naive Bayes Classifier, Journal of Network Communications and Emerging Technologies (JNCET) Volume 1, Issue 1.

Li Y. and Guo L. (2007). An Active Learning Based TCM-KNN Algorithm for Supervised Network Intrusion Detection”, In: 26th Computers and Security, pp: 459–467.

Moradi M. and Zulkernine M. (2003), A Neural Network Based System for Intrusion Detection and Classification of Attack, Natural Science and Engineering Research Council Canada (NSERC).

Mohammed M., Mazid M., Shawkat A. and Kevin S. (2009). Tickle, A Comparison Between Rule Based and Association Rule Mining Algorithms, Third International Conference on Network and System Security.

Mukund Y. and Nayak S. (2016). Improving false alarm rate in intrusion detection systems using Hadoop’, 21-24 Sept, International Conference. Vol.3

Mrutyunjaya P. and Ranjan Patra M. (2009). Evaluating Machine Learning Algorithms for Detecting Network Intrusions, International Journal of Recent Trends in Engineering, vol. 1, no.1.

Raheem Esraa and Saleh Alomari (2018). An Adaptive Intrusion Detection System by using Decision Tree OsamahAdil, Journal of AL-Qadisiyah for computer science and mathematics Vol.10 No.2

Sathyabama S., Irfan Ahmed M., Saravanan A. (2011). Network Intrusion Detection Using Clustering: A Data Mining Approach, International Journal of Computer Application (0975-8887), vol. 30, no. 4.

Tesfahun A. and Bhaskari L. (2015). Effective Hybrid Intrusion Detection System: A Layered Approach, IJCNIS, vol.7, no.3, pp.35-41.

Wang, Zheng. (2018). Deep learning-based intrusion detection with adversaries. IEEE Access 6, 38367- 38384.

Wang H., Cao J. and Zhang Y. (2005). A flexible payment scheme and its role-based access control, IEEE Transactions on knowledge and Data Engineering, vo. 17, no. 3, 425–436.

Wang D., Zhang Z., Wang P., Yan J and Huang X. (2016). Targeted Online Password Guessing: An Underestimated Threat, ACM Conference on Computer and Communications Security, pp. 1242-1254.

Warrender C., Forrest S. and Pearl M. (1999). Detecting Intrusions Using System Calls: Alternative Data Models”, in IEEE symposium on security and privacy, pp:133-145.

Wenke L., Stolfo S. and J. (2000). A Framework for Constructing Features and Models for Intrusion Detection Systems, ACM transactions on Information and system security (TISSEC), vol.3, Issue 4.

Xu X., (2006). Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier Construction and sequential Pattern Prediction. International Journal of Web Services Practices 2(1-2), pp:49–58.

Zheng Z., Li J., Manikapoulos C.N., Jorgenson J. and ucles J. (2001). HIDE: A Hierarchical Network Intrusion Detection System Using Statistical Pre-Processing and Neural Network Classification, IEEE workshop proceedings on Information assurance and security, pp: 85-90.