Intrusion Detection in Computer Networks Through Combining Particle Swarm Optimization and Decision Tree Algorithms

Document Type : Original Article


1 Department of Computer Engineering, University of Rahjuyan Danesh Borazjan, Bushehr, Iran

2 Department of Computer Engineering, Firoozabad Branch, Islamic Azad University, Firoozabad, Iran

3 Department of Managment, Najafabad Branch, Islamic Azad University, Najafabad, Iran.


Nowadays, network-based computer systems have an essential role in modern society and therefore can be targeted by enemies or intruders. To provide complete security in a computer system that is connected to the network, the use of firewalls and other intrusion prevention mechanisms is not always enough, and it is necessary to use other systems called intrusion detection systems. This type of system detects and notifies the user if an intruder passes through the firewall and antivirus and enters the system. Data mining techniques and methods are used to improve the function of these types of systems and to correctly detect intrusions. Due to a large number of features in the intrusion detection data, in this study, a subset of desired features was first selected by using a combination of graph-based clustering algorithm and Particle Swarm Optimization (PSO). Then, to classify the data and to detect intrusion, a model using the standard decision tree data mining technique is shown. The implementation of the proposed method is evaluated by using the NSL-KDD database, which has more realistic records than other intrusion detection data. The results of the experiments show a high functionality of the proposed method.


Aghdam, M. H., & Kabiri, P. (2016). Feature selection for intrusion detection system using ant colony optimization. IJ Network Security, 18(3), 420-432.
Benaicha, S. E., Saoudi, L., Guermeche, S. E. B., & Lounis, O. (2014). Intrusion detection system using genetic algorithm. 2014 Science and Information Conference,
Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing (pp. 1-4). Springer.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008.
Chae, H.-s., Jo, B.-o., Choi, S.-H., & Park, T.-k. (2013). Feature selection for intrusion detection using nsl-kdd. Recent advances in computer science, 20132, 184-187.
Chen, M.-H., Chang, P.-C., & Wu, J.-L. (2016). A population-based incremental learning approach with artificial immune system for network intrusion detection. Engineering Applications of Artificial Intelligence, 51, 171-181.
Goyal, A., & Kumar, C. (2008). GA-NIDS: a genetic algorithm based network intrusion detection system. Northwestern university.
Kenkre, P. S., Pai, A., & Colaco, L. (2015). Real time intrusion detection and prevention system. Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014,
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks,
Kevric, J., Jukic, S., & Subasi, A. (2017). An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Computing and Applications, 28(1), 1051-1058.
Mubarak, S. L. (2016). Intrusion Detection System using SVM SOM & NN. Journal of network and computer applications, 30(1), 114-132.
Muda, Z., Yassin, W., Sulaiman, M., & Udzir, N. (2011). Intrusion detection based on K-Means clustering and Naïve Bayes classification. 2011 7th international conference on information technology in Asia,
Pan, S., Morris, T., & Adhikari, U. (2015). Developing a hybrid intrusion detection system using data mining for power systems. IEEE Transactions on Smart Grid, 6(6), 3104-3113.
Revathi, S., & Malathi, A. (2013). A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. International Journal of Engineering Research & Technology (IJERT), 2(12), 1848-1853.
Rezaeipanah, A., & Ahmadi, G. (2020). Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network. The Computer Journal.
Sabharwal, C. L., Hacke, K. R., & St. Clair, D. C. (1992). Formation of clusters and resolution of ordinal attributes in ID3 classification trees. Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990's,
Saha, S., Sairam, A. S., Yadav, A., & Ekbal, A. (2012). Genetic algorithm combined with support vector machine for building an intrusion detection system. Proceedings of the International Conference on Advances in Computing, Communications and Informatics,
Theodoridis, S., & Koutroumbas, K. (2009). Feature generation I: data transformation and dimensionality reduction. Pattern Recognition, 323-409.
Warsi, S., Rai, Y., & Kushwaha, S. (2015). Selective Iteration based Particle Swarm Optimization (SIPSO) for Intrusion Detection System. International Journal of Computer Applications, 124(17).
Zuech, R., Khoshgoftaar, T. M., & Wald, R. (2015). Intrusion detection and big heterogeneous data: a survey. Journal of Big Data, 2(1), 1-41.