Principal Component Analysis-Multilinear Perceptron-based model for Distributed Denial of Service Attack Mitigation

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Opeyemi Oreoluwa Asaolu
Oluwasanmi Segun Adanigbo
Afeez Adekunle Soladoye
https://orcid.org/0000-0002-6349-5173
Nnamdi Stephen Okomba

Abstract

The increasing occurrence of Distributed Denial of Service (DDoS) attacks has caused significant disruptions in global network services, overwhelming targets by flooding them with requests from various sources. This ease of execution and gaining entry to distributed systems for rent has led to increasing financial losses. This paper addresses the growing challenge of IoT devices-targeted Distributed Denial of Service (DDoS) attacks within 4G networks. In this study, a PCA-MLP (Principal Component Analysis-Multi-Layer Perceptron) intrusion detection model combined with a packet-filtering firewall for enhanced prevention is presented. The firewall, utilizing IPtables, selectively permits traffic from trusted sources, successfully blocking nearly 70% of DDoS threats. The PCA-MLP model proposed in this study demonstrated high performance, accurately identifying different types of DDoS attacks with an overall accuracy of 95.35%.

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How to Cite
[1]
O. O. Asaolu, O. S. Adanigbo, A. A. Soladoye, and N. S. Okomba, “Principal Component Analysis-Multilinear Perceptron-based model for Distributed Denial of Service Attack Mitigation”, AJERD, vol. 8, no. 2, pp. 14–24, May 2025.
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References

Ali, M. S., Shah, S. A., Hussain, A., & Wadhwani, S. K. (2022). A novel hybrid approach for lightweight device fingerprinting and anomaly detection in IoT networks. Sensors, 22(3), 822. https://doi.org/10.3390/s22030822

Kumar, S., & Kumar, R. (2016). Denial of service attacks: A comprehensive study. Procedia Computer Science, 78, 396–401. https://doi.org/10.1016/j.procs.2016.02.068

Farhan, M. A., Khan, S. U., & Salah, K. (2018). A review on mitigation techniques for distributed denial-of-service (DDoS) attacks in IoT networks. IEEE Communications Surveys & Tutorials, 20(4), 2058–2085. https://doi.org/10.1109/COMST.2018.2844341

Bica, I., Chifor, B.-C., Arseni, Ș.-C., & Matei, I. (2019). Multi-layer IoT security framework for ambient intelligence environments. Sensors, 19(18), 4038. https://doi.org/10.3390/s19184038

Rao, G., & Subbarao, P. (2023). A novel approach for detection of DoS/DDoS attack in network environment using ensemble machine learning model. International Journal on Recent and Innovation Trends in Computing and Communication, 11, 244–253. https://doi.org/10.17762/ijritcc.v11i9.8340

Tangtode, D., Sayyad, S., Gelye, O., Sawant, S., & Bombale, P. (2024). DDOS attack detection. International Journal of Advanced Research in Science, Communication and Technology, 248–251. https://doi.org/10.48175/IJARSCT-15547

Rossow, C. (2014). Amplification hell: Revisiting network protocols for DDoS abuse. Proceedings of the Network and Distributed System Security Symposium (NDSS).

Tandem Computers. (2006). Securing HP NonStop servers in an open systems world: TCP/IP, OSS, & SQL.

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. https://doi.org/10.1145/1541880.1541882

Kumar, S., Nandeshwar, S., & Kumar, N. (2023). Mechanism, tools, and techniques to mitigate distributed denial of service attacks. International Journal for Research in Applied Science and Engineering Technology, 11, 855–861. https://doi.org/10.22214/ijraset.2023.48675

Nanda, S., Zafari, F., DeCusatis, C., Wedaa, E., & Yang, B. (2016). Predicting network attack patterns in SDN using a machine learning approach. In Proceedings of the 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) (7–10). IEEE. https://doi.org/10.1109/NFV-SDN.2016.7919495

Kodati, S., Vaishali, K., Gachikanti, S., Dastagiraiah, C., & Sreekanth, N. (2022). Design of an ensemble learning method for detection of distributed denial of service attacks in SDN using machine learning techniques. Optics Communication and Networking, 14, 248–259. https://doi.org/10.1364/josac.589634

Tzagkarakis, G., Ntouskos, A., & Ioannidis, S. (2019). A machine learning framework for early detection of DoS attacks in IoT-edge networks. In 2019 IEEE Global Communications Conference (GLOBECOM) (1–6). IEEE. https://doi.org/10.1109/GLOBECOM38437.2019.9013945

Liu, Y., Tang, J., He, X., & Zhong, Y. (2022). A two-tier DDoS attack detection strategy for SDN environments. IEEE Transactions on Network and Service Management, 19(1), 101–113. https://doi.org/10.1109/TNSM.2021.3123504

Salim, F. B., Shafiullah, G., Khan, M. K., & Islam, N. (2021). Deep learning-based framework for vulnerability detection and mitigation of DDoS attacks on IoT devices in smart grids. Sensors, 21(12), 4140. https://doi.org/10.3390/s21124140

Hussain, F., Islam, N., Khan, M. K., & Al-Saggaf, A. M. (2020). A convolutional neural network-based approach for DDoS attack detection in 4G-LTE networks. IEEE Access, 8, 151244–151255. https://doi.org/10.1109/ACCESS.2020.3017242

Sahi, N., Chen, Z., & Tianfield, H. (2017). A deep learning approach for classification and mitigation of DDoS attacks in cloud computing environments. In 2017 International Conference on Cloud Computing and Big Data (ICCCBD) (202–209). IEEE. https://doi.org/10.1109/ICCCBD.2017.8386642

Ozer, E., Purohit, S., & Agrawal, A. (2017). An efficient deep learning-based approach for fast and accurate DDoS attack detection. In 2017 IEEE International Conference on Big Data (Big Data) (4497–4504). IEEE. https://doi.org/10.1109/BigData.2017.8258504

Osanaiye, O., Cai, H., Choo, K. K. R., Dehghantanha, A., Xu, Z., & Dlodlo, M. (2016). Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP Journal on Wireless Communications and Networking, 130, https://doi.org/10.1186/s13638-016-0623-3

Almadhor, A., Altalbe, A., Bouazzi, I., Al Hejaili, A., & Kryvinska, N. (2024). Strengthening network DDoS attack detection in heterogeneous IoT environment with federated XAI learning approach. Scientific Reports, 14(1), 24322. https://doi.org/10.1038/s41598-024-76016-6

Alahmadi, A. A., Aljabri, M., Alhaidari, F., Alharthi, D. J., Rayani, G. E., Marghalani, L. A., Alotaibi, O. B., & Bajandouh, S. A. (2023). DDoS attack detection in IoT-based networks using machine learning models: A survey and research directions. Electronics, 12(14), 3103. https://doi.org/10.3390/electronics12143103

Saravanan, R., Sangeetha, M., & Kavitha, B. (2023). Enhancing DDoS detection in SDIoT through effective feature selection and machine learning. PLOS ONE, 18(11), e0309682. https://doi.org/10.1371/journal.pone.0309682

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