References
- Sharafaldin, I., A. H. Lashkari, S. Hakak, A. A. Ghorbani. Developing a Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy. – In: Proc. of 53rd International Carnahan Conference on Security Technology, Chennai, India, 2019, IEEE, 2019.
- AlSaleh, I., A. Al-Samawi, L. Nissirat. Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements. – Sensors, Vol. 24, 2024, No 5, 1418.
- Ec-Sabery, M., A. B. Abbou, A. Boushaba, F. Mrabti, R. B. Abbou. The Optimized Extreme Learning Machine (GA-OELM) for DDoS Attack Detection in a Cloud Environment. – Journal of Computer Science, Vol. 21, 2025, No 1, pp. 146-157.
- Songa, A. V., G. R. Karri. An Integrated SDN Framework for Early Detection of DDoS Attacks in Cloud Computing. – Journal of Cloud Computing, Vol. 13, 2024, 64.
- Amitha, M., M. Srivenkatesh. DDoS Attack Detection in Cloud Computing Using Deep Learning Algorithms. – International Journal of Intelligent Systems and Applications in Engineering, Vol. 11, 2023, No 4, pp. 82-90.
- Gudla, S. P. K., S. K. Bhoi, S. R. Nayak, A. Verma. DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications. – Computational Intelligence and Neuroscience, Vol. 2022, 2022, pp. 1-17.
- Bensaid, R., N. Labraoui, A. A. A. Ari, L. Maglaras, H. Saidi, A. M. A. Lwahhab, S. Benfriha. Toward a Real-Time TCP SYN Flood DDoS Mitigation Using an Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing. – Security and Communication Networks, Vol. 2023, 2023, 6651584.
- Pokale, N. B., P. Sharma, D. T. Mane. Deep Hybrid Model for Attack Detection in IoT-Fog Architecture with Improved Feature Set and Optimal Training. – In: Web Intelligence, 2024.
- Selim, I. M., R. A. Sadek. An Effective Fog-Aware NIDS for DDoS Attack Detection. – IAENG International Journal of Computer Science, Vol. 52, 2025, No 6, pp. 1806-1814.
- Kurdi, H., V. Thayananthan. A Multi-Tier MQTT Architecture with Multiple Brokers Based on Fog Computing for Securing Industrial IoT. – Applied Sciences, Vol. 12, 2022, No 7173, pp. 1-15.
- Naik, N. Choice of Effective Messaging Protocols for IoT Systems: MQTT, CoAP, AMQP, and HTTP. – In: IEEE International Systems Engineering Symposium (ISSE’17), Vol. 1, 2017, No 7, pp. 1-7.
- Tavallaee, M., E. Bagheri, W. Lu, A. Ghorbani. A Detailed Analysis of the KDD Cup 99 Data Set. – In: Proc. of 2nd IEEE Symposium on Computational Intelligence for Security and Defense Applications. IEEE, 2009.
- Divekar, A., M. Parekh, V. Savla, M. Shirole. Benchmarking Datasets for Anomaly-Based Network Intrusion Detection: KDD Cup 99 Alternatives. – In: Proc. of IEEE International Conference on Computing, Communication and Security, Kathmandu, Nepal, 2018, IEEE, 2018.
- Sharafaldin, I., A. H. Lashkari, S. Hakak, A. A. Ghorbani. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. – In: Proc. of 4th International Conference on Information Systems Security and Privacy (ICISSP’18), Portugal, 2018, Scitepress, 2018.
- Ferri, C., J. Hernandez-Orallo, R. Modroiu. An Experimental Comparison of Performance Measures for Classification. – Pattern Recognition Letters, Vol. 30, 2009, No 1, pp. 27-38.
- Gamage, S., J. Samarabandu. Deep Learning Methods in Network Intrusion Detection: A Survey and an Objective Comparison. – Pattern Recognition Letters, Vol. 169, 2020, No 2.
- Gubta, B. B., M. Misra, R. C. Joshi. An ISP-Level Solution to Combat DDoS Attacks Using a Combined Statistical-Based Approach. – International Journal of Information Assurance and Security, Vol. 3, 2008, No 2, pp. 102-110.
- Jisa, D., T. Siza. Efficient DDoS Flood Attack Detection Using Dynamic Thresholding on Flow-Based Network Traffic. – Computers and Security, Vol. 82, 2019, pp. 284-295.
- Azahrani, R. J., A. Azahrani. Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Network Traffic. – Electronics, Vol. 10, 2021, No 23.
- Gaur, V., R. Kumar. Analysis of Machine Learning Classifiers for Early Detection of DDoS Attacks on IoT Devices. – Arabian Journal for Science and Engineering, Vol. 47, 2022, pp. 1353-1374.
- Wijnhoven, R. G. J., P. H. N. de With. Fast Training of Object Detection Using Stochastic Gradient Descent. – In: Proc. of 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010, IEEE, 2010.
- Probst, P., M. N. Wright, A.-L. Boulesteix. Hyperparameters and Tuning Strategies for Random Forest. – WIREs Data Mining and Knowledge Discovery, Vol. 9, 2019, No 3.
- Zou, Q., S. Xie, Z. Lin, M. Wu, Y. Ju. Finding the Best Classification Threshold in an Imbalanced Classification. – Big Data Research, Vol. 5, 2016, pp. 2-8.
- Jimin, L., Z. Jianye, Z. Huiqi, D. Xueyu, G. Xin. An Improved Random Forest Intrusion Detection Model Based on Tent Mapping. – In: Proc. of 4th World Symposium on Artificial Intelligence, Jilin, China, 2022. IEEE, 2022.
- Barona, R., E. Baburaj. An Efficient DDoS Attack Detection and Categorization Using an Adolescent Identity Search-Based Weighted SVM Model. – Peer-to-Peer Networking and Applications, Vol. 16, 2023, No 2, pp. 1227-1241.
- Faker, O., E. Dogdu. Intrusion Detection Using Big Data and Deep Learning Techniques. – In: Proc. of 2019 ACM SouthEast Conference (ACM SE’19), ACM, April 2019.
- Banerjee, C., T. Mukherjee, E. Pasiliao. An Empirical Study on Generalizations of the ReLU Activation Function. – In: Proc. of 2019 ACM SouthEast Conference (ACM SE’19), ACM, April 2019.
- Akhilesh, A. W., K. S. Brijesh. Performance Analysis of Sigmoid and ReLU Activation Functions in a Deep Neural Network. – In: A. Sheth, A. Sinhal, A. Shrivastava, A. K. Pandey, Eds. Intelligent Systems, Algorithms for Intelligent Systems, Springer, 2021, pp. 39-52.
- Jadon, S. A Survey of Loss Functions for Semantic Segmentation. – In: Proc. of IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’20), Via del Mar, Chile, 2020, IEEE, 2020.
- Zaheer, R., S. Humera. A Study of the Optimization Algorithms in Deep Learning. – In: Proc. of 3rd International Conference on Inventive Systems and Control (ICISC’19), Coimbatore, India, 2019, IEEE, 2019.
- Qian, N. On the Momentum Term in Gradient Descent Learning Algorithms. – Neural Networks, Vol. 12, 1999, No 1, pp. 145-151.
- Sutskever, I., J. Martens, G. Dahl, G. Hinton. On the Importance of Initialization and Momentum in Deep Learning. – In: Proc. of 30th International Conference on International Conference on Machine Learning (ICML’13), ACM, 2013.
- Shaikh, J., T. A. Syed, S. A. Shah, S. Jan, Q. Ul Ain, P. K. Singh. Advancing DDoS Attack Detection with Hybrid Deep Learning: Integrating Convolutional Neural Networks, PCA, and Vision Transformers. – International Journal on Smart Sensing and Intelligent Systems, Vol. 17, 2024, No 1, pp. 1-16.
- Mehmood, S., R. Amin, J. Mustafa, M. Hussain, F. S. Alsubaei, M. D. Zakaria. Distributed Denial of Services (DDoS) Attack Detection in SDN Using Optimizer-Equipped CNN-MLP. – PLOS ONE, Vol. 20, 2025, No 1.
- AlSaleh, I., A. Al-Samawi, L. Nissirat. Novel Machine-Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements. – Sensors, Vol. 24, 2024, No 5, 1418.
- Abdullah, E., K. Yildiz. Detection of DDoS Attacks with Feed-Forward Based Deep Neural Network Model. – Expert Systems with Applications, Vol. 486, 2021, No 3, pp. 75-174.