Detection of UDP Flooding DDoS Attacks on IoT Networks Using Recurrent Neural Network

Warcita; Kurniabudi; Eko Arip Winanto

doi:10.23887/janapati.v13i3.79601

Authors

Warcita
Kurniabudi Universitas Dinamika Bangsa
Eko Arip Winanto Universitas Dinamika Bangsa

DOI:

https://doi.org/10.23887/janapati.v13i3.79601

Keywords:

DDoS, UDP Flood, IDS, Deep Learning, RNN

Abstract

Internet of Thing (IoT) is a concept where an object can transfer data through a network without requiring human interaction. Complex IoT networks make it vulnerable to cyber attacks such as DDoS UDP Flood attacks, UDP Flood attacks can disrupt IoT devices. Therefore, this study proposes an attack detection method using a deep learning approach with the Recurrent Neural Network (RNN) method. This study uses Principle Component Analysis (PCA) to reduce the feature dimension, before learning using RNN. The purpose of this study is to test the combined performance of the PCA and RNN methods to detect DDoS UDP Flood attacks on IoT networks. The testing in this study used 10 datasets sourced from CICIOT2023 containing UDP Flood and Benign DDoS traffic data, and the testing was carried out using three epoch parameters (iterations), namely 10, 50, and 100. The test results using RNN epoch 100 were superior, showing satisfactory performance with an accuracy value of 98%, precision of 99%, recall of 99%, and f1-score of 99%. Based on the experimental results, it can be concluded that combining PCA and RNN is able to detect UDP Flooding attacks by showing high accuracy.

References

D. E. Kouicem, A. Bouabdallah, and H. Lakhlef, “Internet of things security: A top-down survey,” Comput. Networks, vol. 141, pp. 199–221, 2018, doi: 10.1016/j.comnet.2018.03.012.

B. Di Martino, M. Rak, M. Ficco, A. Esposito, S. A. Maisto, and S. Nacchia, “Internet of things reference architectures, security and interoperability: A survey,” Internet of Things, vol. 1–2, pp. 99–112, 2018, doi: 10.1016/j.iot.2018.08.008.

I. Khajenasiri, A. Estebsari, M. Verhelst, and G. Gielen, “A review on Internet of Things solutions for intelligent energy control in buildings for smart city applications,” Energy Procedia, vol. 111, no. September 2016, pp. 770–779, 2017, doi: 10.1016/j.egypro.2017.03.239.

R. Kaur, P. Vats, M. Mandot, S. S. Biswas, and R. Garg, “Literature Survey for IoT-based Smart Home Automation: A Comparative Analysis,” in 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021, pp. 1–6. doi: 10.1109/ICRITO51393.2021.9596421.

K. H. Le, M. H. Nguyen, T. D. Tran, and N. D. Tran, “IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT,” Electron., vol. 11, no. 4, pp. 1–16, 2022, doi: 10.3390/electronics11040524.

S. Sharmeen, S. Huda, J. H. Abawajy, W. N. Ismail, and M. M. Hassan, “Malware Threats and Detection for Industrial Mobile-IoT Networks,” IEEE Access, vol. 6, no. c, pp. 15941–15957, 2018, doi: 10.1109/ACCESS.2018.2815660.

H. Mustapha and A. M. Alghamdi, “DDoS attacks on the internet of things and their prevention methods,” Proc. 2nd Int. Conf. Futur. Networks Distrib. Syst. - ICFNDS ’18, pp. 1–5, 2018, doi: 10.1145/3231053.3231057.

V. Subbarayalu, B. Surendiran, and P. Arun Raj Kumar, “Hybrid Network Intrusion Detection System for Smart Environments Based on Internet of Things,” Comput. J., vol. 62, no. 12, pp. 1822–1839, Dec. 2019, doi: 10.1093/comjnl/bxz082.

N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network Intrusion Detection for IoT Security based on Learning Techniques,” IEEE Commun. Surv. Tutorials, vol. PP, no. 0, pp. 1–1, 2019, doi: 10.1109/comst.2019.2896380.

M. Roopak, G. Y. Tian, and J. Chambers, “An Intrusion Detection System Against DDoS Attacks in IoT Networks,” 2020 10th Annu. Comput. Commun. Work. Conf. CCWC 2020, pp. 562–567, 2020, doi: 10.1109/CCWC47524.2020.9031206.

S. Gurung, M. Kanti Ghose, and A. Subedi, “Deep Learning Approach on Network Intrusion Detection System using NSL-KDD Dataset,” Int. J. Comput. Netw. Inf. Secur., vol. 11, no. 3, pp. 8–14, Mar. 2019, doi: 10.5815/ijcnis.2019.03.02.

B. I. Farhan and A. D. Jasim, “Survey of Intrusion Detection Using Deep Learning in the Internet of Things,” Iraqi J. Comput. Sci. Math., vol. 3, no. 1, pp. 83–93, 2022, doi: 10.52866/ijcsm.2022.01.01.009.

R. SaiSindhuTheja and G. K. Shyam, “An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment,” Appl. Soft Comput., vol. 100, Mar. 2021, doi: 10.1016/j.asoc.2020.106997.

B. Hussain, Q. Du, B. Sun, and Z. Han, “Deep Learning-Based DDoS-Attack Detection for Cyber–Physical System Over 5G Network,” IEEE Trans. Ind. Informatics, vol. 17, no. 2, pp. 860–870, 2021, doi: 10.1109/TII.2020.2974520.

L. Feinstein, “Preventing DDoS attack using Data mining Algorithms,” IEEE Cloud Comput., vol. 6, no. 10, p. 390, 2016, [Online]. Available: www.ijsrp.org

B. Tushir, H. Sehgal, R. Nair, B. Dezfouli, and Y. Liu, “The Impact of DoS Attacks on Resource-constrained IoT Devices : A Study on the Mirai Attack,” arXiv Prepr. arXiv2104.09041, 2021.

M. A. Al-shareeda, S. Manickam, and M. A. Saare, “DDoS attacks detection using machine learning and deep learning techniques : analysis and comparison,” vol. 12, no. 2, pp. 930–939, 2023, doi: 10.11591/eei.v12i2.4466.

A. Halbouni, T. S. Gunawan, M. H. Habaebi, M. Halbouni, M. Kartiwi, and R. Ahmad, “Machine Learning and Deep Learning Approaches for CyberSecurity: A Review,” IEEE Access, vol. 10, pp. 19572–19585, 2022, doi: 10.1109/ACCESS.2022.3151248.

E. Carlos et al., “CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment,” 2023, doi: 10.20944/preprints202305.0443.v1.

F. Salo, A. B. Nassif, and A. Essex, “Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection,” Comput. Networks, vol. 148, pp. 164–175, Jan. 2019, doi: 10.1016/j.comnet.2018.11.010.

S. A. Kadom, S. H. Hashem, and S. H. Jafer, “Optimize network intrusion detection system based on PCA feature extraction and three naïve bayes classifiers,” J. Phys. Conf. Ser., vol. 2322, no. 1, 2022, doi: 10.1088/1742-6596/2322/1/012092.

R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.

Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, Jan. 2021, doi: 10.1002/ett.4150.