MUET logo

Machine Learning-Driven Approach for DDOS Attacks Detection using Neural-Based Networks: A Proficiency Study

Authors

  • Abdul Ghani Ansari

    Department of Information Technology, QUEST Nawabshah
    Author

  • Fareed Ahmed Jokhio

    Department of Computer Systems Engineering, QUEST Nawabshah
    Author

  • Muhammad Shehram Shah Syed

    Department of Software Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan
    Author

  • Hina Dharejo

    Department of Information Technology, QUEST Nawabshah
    Author

  • Fayaz Ahmed Memon

    Department of Software Engineering, QUEST Nawabshah.
    Author

DOI:

https://doi.org/10.22581/muet1982.3330

Keywords:

Knowledge Discovery Dataset (KDD) , Artificial Neural Network (ANN) , Distributed Denial of Service (DDoS) Attacks , Resilient Backpropagation , Fletcher-Powell Conjugate Gradient , Scaled Conjugate Gradient

Abstract

Cyber-attacks pose significant threats to the Internet and its connected resources, causing harm to institutions and governments. Advanced technologies like cloud computing, the Internet of Things (IoT), and Artificial Intelligence (AI) have made these attacks harder to detect. Botnets, controlled by malicious actors, are at the core of many Internet attacks, including Distributed Denial of Service (DDoS) attacks. Despite years of DDoS incidents, effective defense mechanisms are lacking. In our research, we use Resilient Backpropagation, Fletcher-Powell Conjugate Gradient (FPCG), and Scaled Conjugate Gradient with three Artificial Neural Network (ANN) models: Feedforward Neural Network (FFNN), Cascade-Forward Neural Network (CFFNN), and Fitnet Neural Network (FNN) to detect DDoS attacks. The results showed that the FNN achieved higher accuracy in less time.

Downloads

References

G. Dayanandam, T. V. Rao, D. Bujji Babu, and S. Nalini Durga, “DDoS Attacks—Analysis and Prevention,” Lecture Notes in Networks and Systems, vol. 32, pp. 1–10, 2019, doi: 10.1007/978-981-10-8201-6_1.

R. F. Fouladi, C. E. Kayatas, and E. Anarim, “Frequency-based DDoS attack detection approach using naive bayes classification,” 2016 39th International Conference on Telecommunications and Signal Processing, TSP 2016, pp. 104–107, Nov. 2016, doi: 10.1109/TSP.2016.7760838.

C. J. Hsieh and T. Y. Chan, “Detection of DDoS attacks based on neural network using Apache Spark,” 2016 International Conference on Applied System Innovation, IEEE ICASI 2016, Aug. 2016, doi: 10.1109/ICASI.2016.7539833.

Z. Ren, X. Liu, R. Ye, and T. Zhang, “Security and privacy on internet of things,” Proceedings of 2017 IEEE 7th International Conference on Electronics Information and Emergency Communication, ICEIEC 2017, pp. 140–144, Oct. 2017, doi: 10.1109/ICEIEC.2017.8076530.

G. Ramadhan, Y. Kurniawan, and Chang-Soo Kim, “Design of TCP SYN Flood DDoS attack detection using artificial immune systems,” pp. 72–76, Feb. 2017, doi: 10.1109/ICSENGT.2016.7849626.

M. Leon, T. Markovic, and S. Punnekkat, “Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection and Attack Classification,” Proceedings of the International Joint Conference on Neural Networks, vol. 2022-July, 2022, doi: 10.1109/IJCNN55064.2022.9892293.

R. Vishwakarma and A. K. Jain, “A survey of DDoS attacking techniques and defence mechanisms in the IoT network,” Telecommun Syst, vol. 73, no. 1, pp. 3–25, Jan. 2020, doi: 10.1007/S11235-019-00599-Z/METRICS.

F. Kargl, J. Maier, and M. Weber, “Protecting web servers from distributed denial of service attacks,” Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 514–524, Apr. 2001, doi: 10.1145/371920.372148.

M. Sachdeva, G. Singh, K. Kumar, and K. Singh, “DDoS Incidents and their Impact: A Review.,” Int. Arab J. Inf. Technol., vol. 7, no. 1, pp. 14–20, 2010.

J. E. Dayhoff and J. M. DeLeo, “Artificial neural networks: opening the black box,” Cancer: Interdisciplinary International Journal of the American Cancer Society, vol. 91, no. S8, pp. 1615–1635, 2001.

A. D. Dongare, R. R. Kharde, and A. D. Kachare, “Introduction to artificial neural network,” International Journal of Engineering and Innovative Technology (IJEIT), vol. 2, no. 1, pp. 189–194, 2012.

O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, 2018.

D. Peraković, M. Periša, … I. C.-2016 24th, and undefined 2016, “Artificial neuron network implementation in detection and classification of DDoS traffic,” ieeexplore.ieee.orgD Peraković, M Periša, I Cvitić, S Husnjak2016 24th telecommunications forum (TELFOR), 2016•ieeexplore.ieee.org, doi: 10.1109/TELFOR.2016.7818791.

T. Radivilova, L. Kirichenko, D. V. Ageyev, D. Ageiev, and V. Bulakh, “Classification methods of machine learning to detect DDoS attacks,” ieeexplore.ieee.orgT Radivilova, L Kirichenko, D Ageiev, V Bulakh2019 10th IEEE International Conference on Intelligent Data, 2019•ieeexplore.ieee.org, doi: 10.1109/IDAACS.2019.8924406.

O. Ali, P. C.-2018 9th I. A. U. Computing, and undefined 2018, “Towards DoS/DDoS attack detection using artificial neural networks,” ieeexplore.ieee.orgO Ali, P Cotae2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile, 2018•ieeexplore.ieee.org, Accessed: May 05, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8796637/

H. Bharadwaj, V. K.-P. computer science, and undefined 2018, “Comparative study of neural networks in path planning for catering robots,” ElsevierH Bharadwaj, V KumarProcedia computer science, 2018•Elsevier, Accessed: May 05, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050918309967

R. Fletcher, C. R.-T. computer journal, and undefined 1964, “Function minimization by conjugate gradients,” academic.oup.comR Fletcher, CM ReevesThe computer journal, 1964•academic.oup.com, 2024, Accessed: May 05, 2024. [Online]. Available: https://academic.oup.com/comjnl/article-abstract/7/2/149/335311

C. Arthur, V. Temeng, Y. Z.-G. M. Journal, and undefined 2020, “Performance evaluation of training algorithms in backpropagation neural network approach to blast-induced ground vibration prediction,” ajol.infoCK Arthur, VA Temeng, YY ZiggahGhana Mining Journal, 2020•ajol.info, vol. 20, no. 1, pp. 20–33, 2020, doi: 10.4314/gm.v20i1.3.

M. M.-N. networks and undefined 1993, “A scaled conjugate gradient algorithm for fast supervised learning,” ElsevierMF MøllerNeural networks, 1993•Elsevier, Accessed: May 05, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0893608005800565

B. Gupta, P. Chaudhary, X. Chang, N. N.-C. & Electrical, and undefined 2022, “Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers,” ElsevierBB Gupta, P Chaudhary, X Chang, N NedjahComputers & Electrical Engineering, 2022•Elsevier, Accessed: May 05, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0045790622000404

I. Cvitić, D. Peraković, M. Periša, and B. Gupta, “Ensemble machine learning approach for classification of IoT devices in smart home,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 11, pp. 3179–3202, Nov. 2021, doi: 10.1007/S13042-020-01241-0.

M. Ge, X. Fu, N. Syed, Z. Baig, G. Teo, and A. Robles-Kelly, “Deep learning-based intrusion detection for IoT networks,” ieeexplore.ieee.orgM Ge, X Fu, N Syed, Z Baig, G Teo, A Robles-Kelly2019 IEEE 24th pacific rim international symposium on dependable, 2019ieeexplore.ieee.org, doi: 10.1109/PRDC47002.2019.00056.

G. Ajeetha and G. Madhu Priya, “Machine Learning Based DDoS Attack Detection,” 2019 Innovations in Power and Advanced Computing Technologies, i-PACT 2019, Mar. 2019, doi: 10.1109/I-PACT44901.2019.8959961.

B. NG, S. S.- Neurocomputing, and undefined 2019, “Deep radial intelligence with cumulative incarnation approach for detecting denial of service attacks,” ElsevierBA NG, S SelvakumarNeurocomputing, 2019•Elsevier, Accessed: May 05, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231219302711

O. Elejla, B. Belaton, M. Anbar, … B. A.-… S. and T., and undefined 2019, “Comparison of classification algorithms on ICMPv6-based DDoS attacks detection,” SpringerOE Elejla, B Belaton, M Anbar, B Alabsi, AK Al-AniComputational Science and Technology: 5th ICCST 2018, Kota Kinabalu, Malaysia, 2019•Springer, vol. 481, pp. 29–30, 2018, doi: 10.1007/978-981-13-2622-6_34.

S. Wankhede, D. K.-2018 F. International, and undefined 2018, “DoS attack detection using machine learning and neural network,” ieeexplore.ieee.orgS Wankhede, D Kshirsagar2018 Fourth International Conference on Computing Communication, 2018•ieeexplore.ieee.org, Accessed: May 05, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8697702

C. She, W. Wen, Z. Lin, and K. Zheng, “Application-layer ddos detection based on a one-class support vector machine,”

International Journal of Network Security Its Applications, vol. 9, pp. 13–24, 2017.

X. Yuan, C. Li, X. L.-2017 I. international conference on, and undefined 2017, “DeepDefense: identifying DDoS attack via deep learning,” ieeexplore.ieee.orgX Yuan, C Li, X Li2017 IEEE international conference on smart computing (SMARTCOMP), 2017•ieeexplore.ieee.org, Accessed: May 05, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7946998

E. Hodo, X. Bellekens, … A. H.-2016 I., and undefined 2016, “Threat analysis of IoT networks using artificial neural network intrusion detection system,” ieeexplore.ieee.orgE Hodo, X Bellekens, A Hamilton, PL Dubouilh, E Iorkyase, C Tachtatzis, R Atkinson2016 International Symposium on Networks, Computers and, 2016•ieeexplore.ieee.org, Accessed: May 05, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7746067

Published

2025-07-01

Issue

Vol. 44 No. 3 (2025): July Issue

Section

Articles

License

Copyright (c) 2025 Mehran University Research Journal of Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Machine Learning-Driven Approach for DDOS Attacks Detection using Neural-Based Networks: A Proficiency Study. (2025). Mehran University Research Journal of Engineering and Technology, 44(3), 206-218. https://doi.org/10.22581/muet1982.3330

Similar Articles

11-20 of 50

You may also start an advanced similarity search for this article.