References
- Udas, M., E. Karim, K. S. Ro. SPIDER: A Shallow PCA-Based Network Intrusion Detection System with Enhanced Recurrent Neural Networks. – Journal of King Saud University – Computer and Information Sciences, Vol. 34, 2022, No 10, pp. 10246-10272.
- Wang, K. Zheng, Y. Yang, X. Wang. An Explainable Machine Learning Framework for Intrusion Detection Systems. – IEEE Access, Vol. 8, 2020, pp. 73127-73141.
- Premkumar, T., V. P. Sundararajan. DLDM: Deep Learning-Based Defense Mechanism for Denial of Service Attacks in Wireless Sensor Networks. – Microprocess. Microsystems, Vol. 79, 2020, No August, 103278.
- IBM Security Cost of a Data Breach Report 2022. 2022.
- Fleck, A. Inflation Becomes the Leading Global Concern in 2022. – Statista, 2022 (Accessed 22 Junе 2023). https://www.statista.com/chart/28878/expected-cost-of-cybercrime-until-2027/
- Alhenawi, H., R. Alazzam, O. Al-Sayyed, Abualghanam, O. Adwan. Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm. – Cybernetics and Information Technologies, Vol. 22, 2022, No 4, pp. 73-90.
- Batchu, H. Seetha. An Integrated Approach Explaining the Detection of Distributed Denial of Service Attacks. – Computer Networks, 2022, 109269.
- Mallampati, H. Seetha. A Review on Recent Approaches of Machine Learning, Deep Learning, and Explainable Artificial Intelligence in Intrusion Detection Systems. – Majelisi Journal of Electrical Engineering, Vol. 17, 2023, No 1, pp. 29-54.
- Kim, L. Y., H. Kim. Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks. – Applied Sciences, Vol. 9, 2019, No 7.
- Roy, J., B. Li, Choi, Y. Bai. A Lightweight Supervised Intrusion Detection Mechanism for IoT Networks. – Futurre Genereration Computer Systems, Vol. 127, 2022, pp. 276-285.
- Saha, A., T. Priyoti, A. Sharma. Towards an Optimised Ensemble Feature Selection for DDoS Detection Using Both Supervised and Unsupervised Method. – In: Proc. of 19th Annual Consumer Communications and Networking Conference (CCNC’22), Las Vegas, N. V., USA, 2022.
- De Souza, C., B. Westphall, R. B. Machado, J. B. M. Sobral, G. dos S. Vieira. Hybrid Approach to Intrusion Detection in Fog-Based IoT Environments. – Computer Networks. Vol. 180, 2020.
- Yin, Y., et al. IGRF-RFE: A Hybrid Feature Selection Method for MLP-Based Network Intrusion Detection on UNSW-NB15 Dataset. – Journal of Big Data, Vol. 10, 2023, No 1.
- Patil, S., et al. Explainable Artificial Intelligence for Intrusion Detection System. – Electronics, Vol. 11, 2022, No 19.
- Kannari, N., S. Chowdary, R. Laxmikanth Biradar. An Anomaly-Based Intrusion Detection System Using Recursive Feature Elimination Technique for Improved Attack Detection. – In: Theory of Compututer Science. Vol. 931. 2022, pp. 56-64.
- Thakkar, A., R. Lohiya. Fusion of Statistical Importance for Feature Selection in Deep Neural Network-Based Intrusion Detection System. – Information Fusion, Vol. 90, 2023, No February, pp. 353-363.
- Hariharan, R., R. Rejimol Robinson, R. R. Prasad, C. Thomas, N. Balakrishnan. XAI for Intrusion Detection System: Comparing Explanations Based on Global and Local Scope. – Journal of Computer Viroogy. Hacking Technologies, Vol. 19, 2023, No 2, pp. 217-239.
- Alani, M. M. An Explainable Efficient Flow-Based Industrial IoT Intrusion Detection System. – Computers Electrctriacal Engginering, Vol. 108, 2023, No April, 108732.
- Sharafaldin, A., H. Lashkari, A. A. Ghorbani. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. – In: Proc. of Int. Conf. on Systems Security and Privacy, 2018, No Cic, pp. 108-116.
- Moustafa, N., J. Slay. UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set). – In: Proc of Mil. Commun. Inf. Syst. Conf. (MilCIS’15), 2015, No November.
- Veera Brahmam, M., S. Gopikrishnan, K. Raja Sravan Kumar, M. Seshu Bhavani. Pearson Correlation Based Outlier Detection in Spatial-Temporal Data of IoT Networks. – In: Proc. of Innov. Data Commun. Technol. Appl. Proc. ICIDCA 2021, Singapore, Springer, Nature, Singapore, Vol. 96, 2022, pp. 1019-1028).
- Silva, N., R. de Oliveira, D. S. V. Medeiros, M. A. Lopez, D. M. F. Mattos. A Statistical Analysis of Intrinsic Bias of Network Security Datasets for Training Machine Learning Mechanisms. – Annals of Telecommunications,Vol. 77, 2022, pp. 555-571
- Vergara, P., A. Estévez. A Review of Feature Selection Methods Based on Mutual Information. – Neural Computer Applications, Vol. 24, 2014, No 1, pp. 175-186.
- Pudil, J., Novovičová, J. Kittler. Floating Search Methods in Feature Selection. – Pattern Recognit. Lett., Vol. 15, 1994, No 11, pp. 1119-1125.
- Shirbani, F., H. Soltanian-Zadeh. Fast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets. – Amirkabir Journal of Science and Technology, Vol. 45, 2013, No 2, pp. 43-56.
- Ke, G., et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. – In: Proc of Conference on Neural Information Processing Systems, Vol. 2017, 2017, No December, pp. 3147-3155.
- Lundberg, P., G. Allen, S.-I. Lee. A Unified Approach to Interpreting Model Predictions (online). https://github.com/slundberg/shap
- Gu, K., Li, Z. Guo, Y. Wang. Semi-Supervised k-Means DDOS Detection Method Using Hybrid Feature Selection Algorithm. – IEEE Access, Vol. 7, 2019, pp. 64351-64365.
- Mhawi, A., Aldallal, S. Hassan. Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems. – Symmetry (Basel), Vol. 14, 2022, No 7.
- Fu, J., X. Lan Zhang. Gradient Importance Enhancement Based Feature f Fusion Intrusion Detection Technique. – Computer Networks, Vol. 214, 2022 No May, 109180.
- Kasongo, Sydney Mambwe. A Deep Learning Technique for Intrusion Detection System Using a Recurrent Neural Networks Based Framework. – Computer Communications, Vol. 199, 2023, pp. 113-125.
- Kasongo, Y. Sun. A Deep Learning Method with Wrapper Based Feature Extraction for Wireless Intrusion Detection System. – Computers Security, Vol. 92, 2020.
- Eunice, Q., M. Gao, Y. Zhu, Z. Chen, N. Lv. Network Anomaly Detection Technology Based on Deep Learning. – In: Proc. of 3rd Int. IEEE Conf. Front. Technol. Inf. Comput. ICFTIC 2021, pp. 6-9.
- Kasongo. An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms. – IEEE Access, Vol. 9, 2021, pp. 113199-113212.