Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Alhenawi, Esra’a; Alazzam, Hadeel; Al-Sayyed, Rizik; AbuAlghanam, Orieb; Adwan, Omar

doi:10.2478/cait-2022-0040

Abstract

A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.

References

1. Alazzam, H., O. AbuAlghanam, Q. M. Al-zoubi, A. Alsmady, E. Alhenawi. A New Network Digital Forensics Approach for Internet of Things Environment Based on Binary Owl Optimizer. – Cybernetics and Information Technologies, Vol. 22, 2022, No 3, pp. 146-160.10.2478/cait-2022-0033
Search in Google Scholar Back to article
2. AbuAlghanam, O., M. Qatawneh, W. Almobaideen, M. Saadeh. A New Hierarchical Architecture and Protocol for Key Distribution in the Context of Iot-Based Smart Cities. – Journal of Information Security and Applications, Vol. 67, pp. 103-173.10.1016/j.jisa.2022.103173
Search in Google Scholar Back to article
3. Abualghanam, O., M. Qatawneh, W. Almobaideen. A Survey of Key Distribution in the Context of Internet of Things. – Journal of Theoretical and Applied Information Technology, Vol. 97, No 22, pp. 3217-3241.
Search in Google Scholar Back to article
4. AbuAlghanam, O., L. Albdour, L., O. Adwan. Multimodal Biometric Fusion Online Handwritten Signature Verification Using Neural Network and Support Vector Machine. – Transactions, Vol. 7, No 8.
Search in Google Scholar Back to article
5. Alazzam, H., A. Sharieh, K. E. Sabri. A Lightweight Intelligent Network Intrusion Detection System Using Ocsvm and Pigeon Inspired Optimizer. – Applied Intelligence, pp. 1-18.
Search in Google Scholar Back to article
6. Scarfone, K., P. Mell. Guide to Intrusion Detection and Prevention Systems (IDPS). NIST. Special Publication. Vol. 800. 2007. 94 p.10.6028/NIST.SP.800-94
Search in Google Scholar Back to article
7. Torabi, M., N. I. Udzir, M. T. Abdullah, R. A. Yaakob. Review on Feature Selection and Ensemble Techniques for Intrusion Detection System. – Network, Vol. 1, No 2.
Search in Google Scholar Back to article
8. Raghunandan, K. R., A. Ganesh, S. Surendra, K. Bhavya. Key Generation Using Generalized Pell’s Equation in Public Key Cryptography Based on the Prime Fake Modulus Principle to Image Encryption and Its Security Analysis. – Cybernetics and Information Technologies, Vol. 20, 2020, No 3, pp. 86-101.10.2478/cait-2020-0030
Search in Google Scholar Back to article
9. Ejaz, S., U. Noor, Z. Rashid. Visualizing Interesting Patterns in Cyber Threat Intelligence Using Machine Learning Techniques. – Cybernetics and Information Technologies, Vol. 22, 2022, No 2, pp. 96-113.10.2478/cait-2022-0019
Search in Google Scholar Back to article
10. Alazzam, H., A. Sharieh, K. E. Sabri. A Feature Selection Algorithm for Intrusion Detection System Based on Pigeon Inspired Optimizer. – Expert Systems with Applications, Vol. 148, pp. 113249.10.1016/j.eswa.2020.113249
Search in Google Scholar Back to article
11. AbuAlghanam, O., O. Adwan, M. A. Al Shariah, M. Qatawneh. Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation. – Cybernetics and Information Technologies, Vol. 22, 2022, No 2, pp. 36-49.10.2478/cait-2022-0015
Search in Google Scholar Back to article
12. Dietterich, T. G. Ensemble Methods in Machine Learning. – In: Proc. of International Workshop on Multiple Classifier Systems, Springer, pp. 1-15.10.1007/3-540-45014-9_1
Search in Google Scholar Back to article
13. Alhenawi, E., R. Al-Sayyed, A. Hudaib, S. Mirjalili. Feature Selection Methods on Gene Expression Microarray Data for Cancer Classification: A Systematic Review. – Computers in Biology and Medicine, Vol. 140, pp. 105051.10.1016/j.compbiomed.2021.10505134839186
Search in Google Scholar Back to article
14. Kant, S., D. Agarwal, P. K. Shukla. A Survey on Fuzzy Systems Optimization Using Evolutionary Algorithms and Swarm Intelligence. – Computer Vision and Robotics, pp. 421-444.10.1007/978-981-16-8225-4_33
Search in Google Scholar Back to article
15. Dharini, S., S. Jain. A Novel Metaheuristic Optimal Feature Selection Framework for Object Detection with Improved Detection Accuracy Based on Pulse-Coupled Neural Network. – Soft Computing, pp. 1-13.
Search in Google Scholar Back to article
16. Huda, R. K., H. Banka. Efficient Feature Selection Methods Using PSO with Fuzzy Rough Set as Fitness Function. – Soft Computing, Vol. 26, No 5, pp. 2501-2521.10.1007/s00500-021-06393-x
Search in Google Scholar Back to article
17. Wang, H., C. He, Z. Li. A New Ensemble Feature Selection Approach Based on Genetic Algorithm. – Soft Computing, Vol. 24, No 20, pp. 15811-15820.10.1007/s00500-020-04911-x
Search in Google Scholar Back to article
18. Tan, F., X. Fu, Y. Zhang, A. G. Bourgeois. A Genetic Algorithm-Based Method for Feature Subset Selection. – Soft Computing, Vol. 12, No 2, pp. 111-120.10.1007/s00500-007-0193-8
Search in Google Scholar Back to article
19. Gharaee, H., H. Hosseinvand. A New Feature Selection IDS Based on Genetic Algorithm and SVM. – In: Proc. of 8th International Symposium on Telecommunications (IST’16), IEEE, 2016, pp. 139-44.10.1109/ISTEL.2016.7881798
Search in Google Scholar Back to article
20. Kumar, G. R., G. Ramachandra, K. Nagamani. An Efficient Feature Selection System to Integrating SVM with Genetic Algorithm for Large Medical Datasets. – International Journal, Vol. 4, No 2, pp. 272-277.
Search in Google Scholar Back to article
21. Ghatasheh, N., I. Altaharwa, K. Aldebei. Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction – IEEE Access.
Search in Google Scholar Back to article
22. Deng, X., M. Li, S. Deng, L. Wang. Hybrid Gene Selection Approach Using Xgboost and Multi-Objective Genetic Algorithm for Cancer Classification. – Medical & Biological Engineering & Computing, Vol. 60, pp. 663-681.10.1007/s11517-021-02476-x35028863
Search in Google Scholar Back to article
23. Sahu, B., D. Mishra. A Novel Feature Selection Algorithm Using Particle Swarm Optimization for Cancer Microarray Data. – Procedia Engineering, Vol. 38, 2012, pp. 27-31.10.1016/j.proeng.2012.06.005
Search in Google Scholar Back to article
24. Vijayashree, J., H. P. Sultana. A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier. – Programming and Computer Software, Vol. 44, 2018, No 6, pp. 388-397.10.1134/S0361768818060129
Search in Google Scholar Back to article
25. Paniri, M., M. B. Dowlatshahi, H. Nezamabadi-pour. MLACO: A Multi-Label Feature Selection Algorithm Based on Ant Colony Optimization. – Knowledge-Based Systems, Vol. 192, 2020, pp. 105-285. https://www.sciencedirect.com/science/article/pii/S095070511930580510.1016/j.knosys.2019.105285
Search in Google Scholar Back to article
26. Jayaprakash, A., C. KeziSelvaVijila. Feature Selection Using Ant Colony Optimization (ACO) and Road Sign Detection and Recognition (RSDR) System. – Cognitive Systems Research, Vol. 58, 2019, pp. 123-133.10.1016/j.cogsys.2019.04.002
Search in Google Scholar Back to article
27. Alijla, B. O., C. P. Lim, L. P. Wong, A. T. Khader, M. A. Al-Betar. An Ensemble of Intelligent Water Drop Algorithm for Feature Selection Optimization Problem. – Applied Soft Computing, Vol. 65, pp. 531-541.10.1016/j.asoc.2018.02.003
Search in Google Scholar Back to article
28. Acharya, N., S. Singh. An IWD-Based Feature Selection Method for Intrusion Detection System. – Soft Computing, Vol. 22, pp. 4407-4416.10.1007/s00500-017-2635-2
Search in Google Scholar Back to article
29. Aslahi-Shahri, B., R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M. J. Golkar, A. Ebrahimi. A Hybrid Method Consisting of GA and SVM for Intrusion Detection System. – Neural Computing and Applications, Vol. 27, pp. 1669-1676.10.1007/s00521-015-1964-2
Search in Google Scholar Back to article
30. Kunhare, N., R. Tiwari, J. Dhar. Particle Swarm Optimization and Feature Selection for Intrusion Detection System. – Sadhan, Vol. 45, No 1, pp. 1-14.10.1007/s12046-020-1308-5
Search in Google Scholar Back to article
31. Al Tawil, A., K. E. Sabri. A Feature Selection Algorithm for Intrusion Detection System Based on Moth Flame Optimization. – In: Proc. of International IEEE Conference on Information Technology (ICIT’21), 2021, pp. 377-381.10.1109/ICIT52682.2021.9491690
Search in Google Scholar Back to article
32. Kira, K., L. A. Rendell. The Feature Selection Problem: Traditional Methods and a New Algorithm. – AAAI, Vol. 2, pp. 129-134.
Search in Google Scholar Back to article
33. Zhang, Y., X. Ren, J. Zhang. Intrusion Detection Method Based on Information Gain and ReliefF Feature Selection. – In: Proc. of International Joint Conference on Neural Networks (IJCNN’19), 2019, pp. 1-5.10.1109/IJCNN.2019.8851756
Search in Google Scholar Back to article
34. Shreem, S. S., S. Abdullah, M. Z. A. Nazri, M. Alzaqebah. Hybridizing ReliefF, MRMR Filters and GA Wrapper Approaches for Gene Selection. – J Theor Appl Inf Technol, Vol. 46, No 2, pp. 1034-1039.
Search in Google Scholar Back to article
35. Gu, Q., Z. Li, J. Han. Generalized Fisher Score for Feature Selection. – arXiv preprint arXiv:12023725.
Search in Google Scholar Back to article
36. He, X., D. Cai, P. Niyogi. Laplacian Score for Feature Selection. – Advances in Neural Information Processing Systems, Vol. 18.
Search in Google Scholar Back to article
37. Glover, F., M. Laguna. Tabu Search – Handbook of Combinatorial Optimization. – Springer, 1998. pp. 2093-2229.10.1007/978-1-4613-0303-9_33
Search in Google Scholar Back to article
38. Ahmadian, A., A. Elkamel, A. Mazouz. An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network. – Energies, Vol. 12, No 16, pp. 30-52.10.3390/en12163052
Search in Google Scholar Back to article
39. Alazzam, H., E. Alhenawi, R. Al-Sayyed. A Hybrid Job Scheduling Algorithm Based on Tabu and Harmony Search Algorithms. – The Journal of Supercomputing, Vol. 75, pp. 7994-8011.10.1007/s11227-019-02936-0
Search in Google Scholar Back to article
40. Tubishat, M., N. Idris, L. Shuib, M. A. Abushariah, S. Mirjalili. Improved Salp Swarm Algorithm Based on Opposition Based Learning and Novel Local Search Algorithm for Feature Selection. – Expert Systems with Applications, Vol. 145, pp. 113-122.10.1016/j.eswa.2019.113122
Search in Google Scholar Back to article
41. Shehab, M., A. T. Khader, M. A. Al-Betar, L. M. Abualigah. Hybridizing Cuckoo Search Algorithm with Hill Climbing for Numerical Optimization Problems. – In: Proc. of 8th International Conference on Information Technology (ICIT’17), 2017, pp. 36-43.10.1109/ICITECH.2017.8079912
Search in Google Scholar Back to article
42. Shah-Hosseini, H. The Intelligent Water Drops Algorithm: A Nature Inspired Swarm-Based Optimization Algorithm. – International Journal of Bio-Inspired Computation, Vol. 1, No 1-2, pp. 71-79.10.1504/IJBIC.2009.022775
Search in Google Scholar Back to article
43. Lippmann, R. P., I. Graf, D. Wyschogrod, S. E. Webster, D. J. Weber, S. Gorton. The 1998 DARPA/AFRL Off-Line Intrusion Detection Evaluation. – In: Proc. of First International Workshop on Recent Advances in Intrusion Detection (RAID).
Search in Google Scholar Back to article
44. Ahmed, M., A. Mahmood, N. Abdun, J. Hu. A Survey of Network Anomaly Detection Techniques – Journal of Network and Computer Applications, Vol. 60, pp. 19-31.10.1016/j.jnca.2015.11.016
Search in Google Scholar Back to article
45. Moustafa, N., J. Slay. UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set). – In: Proc. of Military Communications and Information Systems Conference (MilCIS’15), 2015, pp. 1-6.10.1109/MilCIS.2015.7348942
Search in Google Scholar Back to article
46. Tavallaee, M., E. Bagheri, W. Lu, A. A. Ghorbani. A Detailed Analysis of the KDD CUP 99 Data Set. – In: Proc. of IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1-6.10.1109/CISDA.2009.5356528
Search in Google Scholar Back to article
47. Ingre, B., A. Yadav. Performance Analysis of NSL-KDD Dataset Using ANN. – In: Proc. of International Conference on Signal Processing and Communication Engineering Systems, 2015, pp. 92-96.10.1109/SPACES.2015.7058223
Search in Google Scholar Back to article
48. Ribaric, S., I. Fratric. Experimental Evaluation of Matching-Score Normalization Techniques on Different Multimodal Biometric Systems. – In: Proc. of MELECON 2006-2006 IEEE Mediterranean Electrotechnical Conference, pp. 498-501.
Search in Google Scholar Back to article
49. Sahu, S. K., S. Sarangi, S. K. Jena. A Detail Analysis on Intrusion Detection Datasets. – In: Proc. of IEEE International Advance Computing Conference (IACC’14), 2014, pp. 1348-1353.10.1109/IAdCC.2014.6779523
Search in Google Scholar Back to article
50. Song, Y. Y., L. Ying. Decision Tree Methods: Applications for Classification and Prediction. – Shanghai Archives of Psychiatry, Vol. 27, No 2, pp. 130.
Search in Google Scholar Back to article
51. Rawashdeh, H., S. Awawdeh, F. Shannag, E. Henawi, H. Faris, N. Obeid. Intelligent System Based on Data Mining Techniques for Prediction of Preterm Birth for Women with Cervical Cerclage. – Computational Biology and Chemistry, Vol. 85, pp. 107233.10.1016/j.compbiolchem.2020.10723332106071
Search in Google Scholar Back to article
52. Shukla, A. K, P. Singh, M. Vardhan. A Hybrid Gene Selection Method for Microarray Recognition. – Biocybernetics and Biomedical Engineering, Vol. 38, No 4, pp. 975-91.10.1016/j.bbe.2018.08.004
Search in Google Scholar Back to article
53. Powers, D. M. Evaluation: from Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. – arXiv preprint arXiv:201016061.
Search in Google Scholar Back to article
54. Liu, Z., Y. Shi. A Hybrid IDS Using GA-Based Feature Selection Method and Random Forest. – Int. J. Mach. Learn. Comput., Vol. 12, No 2, pp. 43-50.
Search in Google Scholar Back to article
55. Yin, Y., J. Jang-Jaccard, W. Xu, A. Singh, J. Zhu, F. Sabrina. IGRF-RFE: A Hybrid Feature Selection Method for MLP-Based Network Intrusion Detection on UNSW-NB15 Dataset. – arXiv preprint arXiv:220316365.
Search in Google Scholar Back to article
56. Kumar, V., D. Sinha, A. K. Das, S. C. Pandey, R. T. Goswami. An Integrated Rule Based Intrusion Detection System: Analysis on UNSW-NB15 Data Set and the Real Time Online Dataset. – Cluster Computing, Vol. 23, No 2, pp. 1397-418.10.1007/s10586-019-03008-x
Search in Google Scholar Back to article
57. Umar, M. A., C. Zhanfang, Y. Liu. Network Intrusion Detection Using Wrapper-Based Decision Tree for Feature Selection. – In: Proc. of International Conference on Internet Computing for Science and Engineering, 2020, pp. 5-13.10.1145/3424311.3424330
Search in Google Scholar Back to article
58. Wang, W., S. Jian, Y. Tan, Q. Wu, C. Huang. Representation Learning-Based Network Intrusion Detection System by Capturing Explicit and Implicit Feature Interactions. – Computers & Security, Vol. 112, pp. 102537.10.1016/j.cose.2021.102537
Search in Google Scholar Back to article
59. Zhang, H., J. L. Li, X. M. Liu, C. Dong. Multi-Dimensional Feature Fusion and Stacking Ensemble Mechanism for Network Intrusion Detection. – Future Generation Computer Systems, Vol. 122, pp. 130-143.10.1016/j.future.2021.03.024
Search in Google Scholar Back to article
60. Tama, B. A., M. Comuzzi, K. H. Rhee. TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System. – IEEE Access, Vol. 7, pp. 94497-94507.10.1109/ACCESS.2019.2928048
Search in Google Scholar Back to article
61. Aljawarneh, S., M. Aldwairi, M. B. Yassein. Anomaly-Based Intrusion Detection System through Feature Selection Analysis and Building Hybrid Efficient Model. – Journal of Computational Science, Vol. 25, pp. 152-160.10.1016/j.jocs.2017.03.006
Search in Google Scholar Back to article
62. Ghanem, W. A., A. Jantan. Training a Neural Network for Cyberattack Classification Applications Using Hybridization of an Artificial Bee Colony and Monarch Butterfly Optimization. – Neural Processing Letters, Vol. 51, No 1, pp. 905-946.10.1007/s11063-019-10120-x
Search in Google Scholar Back to article
63. Mohammadi, S., H. Mirvaziri, M. Ghazizadeh-Ahsaee, H. Karimipour. Cyber Intrusion Detection by Combined Feature Selection Algorithm. – Journal of Information Security and Applications, Vol. 44, pp. 80-88.10.1016/j.jisa.2018.11.007
Search in Google Scholar Back to article

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

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