DDoS Attack Detection in Cloud Computing Using Deep Learning Algorithms
Keywords:
Cloud computing, Deep Learning, distributed denial of serviceAbstract
Distributed cloud computing and its reliance on internet connectivity have more challenges. They offer a great deal of flexibility, and these assets are accessible through the Internet using popular requirements, forms, and protocols for networking according to the cloud service-providing organizations. Attacks like distributed denial of service are a few of the most frequent attacks that severely harm the cloud and lower its performance. Internal attacks cannot be identified using established methods of detection such as firewalls. The attackers frequently modify their skill strategies, because of the increasing amount of data created and stored, conventional detection techniques are inefficient in identifying novel DDoS attacks. Radial Basis Function (RBF) networks are a type of artificial neural network commonly used for function approximation, pattern recognition, and classification tasks. While they have been used in various domains, they are not typically used directly within convolutional neural networks (CNNs) for DDoS (Distributed Denial of Service) detection. This paper presents a hybrid model of Radial Basis Function (RBF) and LSTM networks-based approach for DDoS attack detection and mitigation, aiming to enhance the overall security of cloud computing infrastructures. Our proposed method is evaluated on benchmark dataset CICDDoS2019, demonstrating its effectiveness in identifying DDoS attacks and mitigating their impact on cloud systems.
Downloads
References
Masdari, M., and Jalali, M.: ‘A survey and taxonomy of DoS attacks in cloud computing’, Security and Communication Networks, 2016, 9, (16), pp. 3724-3751
Song Wang, Juan Fernando Balarezo, Karina Gomez Chavez, Akram Al-Hourani, Sithamparanathan Kandeepan, Muhammad Rizwan Asghar, Giovanni Russello, “Detecting flooding DDoS attacks in software defined networks using supervised learning techniques”, Engineering Science and Technology, an International Journal, Volume 35,2022,101-176, ISSN 2215-0986,
Rawashdeh, A., Alkasassbeh, M., and Al-Hawawreh, M.: ‘An anomaly-based approach for DDoS attack detection in cloud environment’, International Journal of Computer Applications in Technology, 2018, 57, (4), pp. 312-324
Wani, A.R., Rana, Q., Saxena, U., and Pandey, N.: ‘Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques’, in Editor (Ed.)^(Eds.): ‘Book Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques’ (IEEE, 2019, edn.), pp. 870-875
Idhammad, M., Afdel, K., and Belouch, M.: ‘Detection system of HTTP DDoS attacks in a cloud environment based on information theoretic entropy and random forest’, Security and Communication Networks, 2018, 2018
Hezavehi, S.M., and Rahmani, R.: ‘An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments’, Cluster Computing, 2020, pp. 1-19
R. K. Gupta et al., “An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large Scale 5G”, Wireless Communications and Mobile Computing 2022.
Khuphiran, Panida, et al. "Performance comparison of machine learning models for ddos attacks detection." 2018 22nd International Computer Science and Engineering Conference (ICSEC). IEEE, 2018.
Farnaaz, Nabila, and M. A. Jabbar. "Random forest modeling for network intrusion detection system." Procedia Computer Science 89 (2016): 213-217.
Sahi, A., Lai, D., Li, Y., and Diykh, M.: ‘An efficient DDoS TCP flood attack detection and prevention system in a cloud environment’, IEEE Access, 2017, 5, pp. 6036-6048
Chen, Z., Jiang, F., Cheng, Y., Gu, X., Liu, W., and Peng, J.: ‘XGBoost classifier for DDoS attack detection and analysis in SDN-based cloud’, in Editor (Ed.)^(Eds.): ‘Book XGBoost classifier for DDoS attack detection and analysis in SDN-based cloud’ (IEEE, 2018, edn.), pp. 251-256
Kingma, D. P., & Ba, J. (2015). Adam: a method for stochastic optimization. 3rd International Conference for Learning Representations, San Diego. Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A. (2019).
Chollet F., “Keras: Python Deep Learning Library,” https://keras.io, Last Visited, 2022. [27] University of New Brunswick. DDoS Evaluation Dataset (CIC-DDoS2019). 2019. Available online: https://www.unb.ca/cic/ datasets/ddos-2019.html (accessed on 20 december 2021).
Sharafaldin, I.; Lashkari, A.H.; Hakak, S.; Ghorbani, A.A. Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In Proceedings of the 2019 International Carnahan Conference on Security Technology (ICCST), Chennai, India, 1–3 October 2019; pp. 1–8
Cheng, J.; Yin, J.; Liu, Y.; Cai, Z.;Wu, C. DDoS attack detection using IP address feature interaction. In Proceedings of the IEEE International Conference on Intelligent Networking and Collaborative Systems, Thessalonika, Greece, 24–26 November 2010; IEEE: Piscataway Township, NJ, USA, 2009; pp. 113–118.
Wang, C.; Zheng, J.; Li, X. Research on DDoS attacks detection based on RDF-SVM. In Proceedings of the 10th International Conference on Intelligent Computation Technology and Automation, Changsha, China, 9–12 October 2017.
Prathyusha, D.J., and Kannayaram, G.: ‘A cognitive mechanism for mitigating DDoS attacks using the artificial immune system in a cloud environment’, Evolutionary Intelligence, 2020, pp. 1-12
Rabbani, M., Wang, Y.L., Khoshkangini, R., Jelodar, H., Zhao, R., and Hu, P.: ‘A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing’, Journal of Network and Computer Applications, 2020, 151, pp. 102507
Zareapoor, M., Shamsolmoali, P., and Alam, M.A.: ‘Advance DDOS detection and mitigation technique for securing cloud’, International Journal of Computational Science and Engineering, 2018, 16, (3), pp. 303-310
Xu, Y., Sun, H., Xiang, F., and Sun, Z.: ‘Efficient DDoS Detection Based on K-FKNN in Software Defined Networks’, IEEE Access, 2019, 7, pp. 160536-160545
Velliangiri, S., and Pandey, H.M.: ‘Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms’, Future Generation Computer Systems, 2020
Kesavamoorthy, R., and Soundar, K.R.: ‘Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system’, Cluster Computing, 2019, 22, (4), pp. 9469-9476
A. Al-Abassi, H. Karimipour, A. Dehghantanha, and R. M. Parizi, “An ensemble deep learning-based cyber-attack detection in industrial control system,” IEEE Access, vol. 8, pp. 83965–83973, 2020.
Y. Liu, M. Dong, K. Ota, J. Li and J. Wu, "Deep Reinforcement Learning based Smart Mitigation of DDoS Flooding in Software-Defined Networks," 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Barcelona, Spain, 2018, pp. 1-6, doi: 10.1109/CAMAD.2018.8514971.
Srikanth yadav M., R. Kalpana, “Recurrent nonsymmetric deep auto encoder approach for network intrusion detection system, Measurement: Sensors, Volume 24, 2022, ISSN 2665-9174
G. Oke, G. Loukas and E. Gelenbe, "Detecting Denial of Service Attacks with Bayesian Classifiers and the Random Neural Network," 2007 IEEE International Fuzzy Systems Conference, London, UK, 2007, pp. 1-6, doi: 10.1109/FUZZY.2007.4295666.
LV Y, Le Q-T, Bui H-B, Bui X-N, Nguyen H, Nguyen-Thoi T, Dou J, Song X. A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer. Applied Sciences. 2020; 10(2):635. https://doi.org/10.3390/app10020.
Singh, C. ., Gangwar, M. ., & Kumar, U. . (2023). Improving Accuracy of Integrated Neuro-Fuzzy Classifier with FCM based Clustering for Diagnosis of Psychiatric Disorder. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 244–248. https://doi.org/10.17762/ijritcc.v11i2s.6143
Hernandez, A., Hughes, W., Silva, D., Pérez, C., & Rodríguez, C. Machine Learning for Predictive Analytics in Engineering Procurement. Kuwait Journal of Machine Learning, 1(2). Retrieved from ttp://kuwaitjournals.com/index.php/kjml/article/view/124
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
