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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

35. Gu, Q., Z. Li, J. Han. Generalized Fisher Score for Feature Selection. – arXiv preprint arXiv:12023725. Search in Google Scholar

36. He, X., D. Cai, P. Niyogi. Laplacian Score for Feature Selection. – Advances in Neural Information Processing Systems, Vol. 18. Search in Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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