Abstract

Cloud computing environments are vulnerable to Distributed Denial of Service (DDoS) attacks, which can disrupt services and cause substantial financial and reputational damage. The dynamic nature of cloud traffic makes it difficult for traditional detection methods to remain effective, creating a need for more robust and adaptive detection strategies. To detect DDoS attacks accurately and efficiently, this study proposes an optimized Long Short-Term Memory (LSTM) model combined with a Partial Opposition-Based Firefly Algorithm (POFA). LSTM is used because of its ability to process sequential data and capture long-term dependencies, both of which are important for identifying network-traffic patterns associated with DDoS attacks. POFA is employed to optimize the LSTM hyperparameters, thereby reducing false-positive rates and improving detection accuracy. Experimental results indicate that the proposed method improves detection accuracy, processing speed, and scalability compared with the evaluated baseline approaches. These findings suggest that the proposed POFA-LSTM model can strengthen security in cloud-computing environments.

Keywords

Cloud Computing, Long-Short Term Memory, Firefly Algorithm, DDos Attack Detection, Partial Opposition-Based Learning,

Downloads

Download data is not yet available.

References

  1. G. Somani, M.S. Gaur, D. Sanghi, M. Conti, R. Buyya, DDoS attacks in cloud computing: Issues, taxonomy, and future directions. Computer Communications, 107, (2017) 30-48. https://doi.org/10.1016/j.comcom.2017.03.010
  2. F.S.D. Lima Filho, F.A. Silveira, A. de Medeiros Brito Junior, G. Vargas-Solar, L.F. Silveira, Smart detection: an online approach for DoS/DDoS attack detection using machine learning. Security and Communication Networks, 2019(1), (2019) 1574749. https://doi.org/10.1155/2019/1574749
  3. N.V. RajaSekhar Reddy, N. SreeDivya, B.N. Jagadesh, R. Gandikota, K.K. Lella, B. Pydala, R.Vatambeti, Enhancing anomaly detection: a comprehensive approach with MTBO feature selection and TVETBOOptimized Quad-LSTM classification. Computers and Electrical Engineering, 119, (2024) 109536. https://doi.org/10.1016/j.compeleceng.2024.109536
  4. M. Mittal, K. Kumar, S. Behal, DL-2P-DDoSADF: Deep learning-based two-phase DDoS attack detection framework. Journal of Information Security and Applications, 78, (2023) 103609. https://doi.org/10.1016/j.jisa.2023.103609
  5. R. Priyadarshini, R.K. Barik, (2019) A deep learning based intelligent framework to mitigate DDoS attack in fog environment. Journal of King Saud University-Computer and Information Sciences. 1-7. https://doi.org/10.1016/j.jksuci.2019.04.010
  6. O. Pandithurai, C. Venkataiah, S. Tiwari, N. Ramanjaneyulu, DDoS Attack Prediction using a honey badger optimization algorithm based feature selection and Bi-LSTM in cloud environment. Expert Systems with Applications, 241, (2024) 122544. https://doi.org/10.1016/j.eswa.2023.122544
  7. N.K. Muthunambu, S. Prabakaran, B. PrabhuKavin, K.S. Siruvangur, K. Chinnadurai, J. Ali, A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM. Computers, Materials & Continua, 78(3), (2024) 3089-3127. https://doi.org/10.32604/cmc.2023.043172
  8. V. Poornachander, K.S. Kumar, S. Jagadish, DDoS Attack Intrusion Detection System with CNN and LSTM Hybridization. (2024) International Conference on Sustainable Computing and Smart Systems (ICSCSS), IEEE, India. https://doi.org/10.1109/ICSCSS60660.2024.10625330
  9. R. Devendiran, A.V. Turukmane, Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy. Expert Systems with Applications, 245, (2024) 123027. https://doi.org/10.1016/j.eswa.2023.123027
  10. V. Sughanthini, P. Bharathisindhu, (2024) DDoS Attack Detection Using Optimized Long Short-Term Based on Partial Opposition-Based Swarm Intelligence Algorithm. International Conference on Sustainable Computing and Smart Systems (ICSCSS), IEEE, India. https://doi.org/10.1109/ICSCSS60660.2024.10624861
  11. A.D. Vibhute, M. Khan, A. Kanade, C.H. Patil, S.V. Gaikwad, K.K. Patel, J.R. Saini, An LSTM‐based Novel Near‐real‐time multiclass network Intrusion Detection System for Complex Cloud Environments. Concurrency and Computation: Practice and Experience, 36(11), (2024) e8024. https://doi.org/10.1002/cpe.8024
  12. D. Sathish, A. Kavitha, (2024) DDoS Attack Detection Using Optimized Long Short-Term Memory Based on Improved Bacterial Foraging Optimization. International Conference on Sustainable Computing and Smart Systems (ICSCSS), IEEE, India. https://doi.org/10.1109/ICSCSS60660.2024.10624895
  13. A. Thangasamy, B. Sundan, L. Govindaraj, A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques. Computer Systems Science & Engineering, 45(3), (2023) 2553-2567. https://doi.org/10.32604/csse.2023.032078
  14. A.S.A. Issa, Z. Albayrak, DDoS attack intrusion Detection System based on Hybridization of CNN and LSTM. Acta Polytechnica Hungarica, 20(2), (2023)1-19. https://doi.org/10.12700/APH.20.2.2023.2.6
  15. M. Ramzan, M. Shoaib, A. Altaf, S. Arshad, F. Iqbal, Á.K. Castilla, I. Ashraf, Distributed Denial of Service Attack Detection in Network Traffic using Deep Learning Algorithm. Sensors, 23(20), (2023) 8642. https://doi.org/10.3390/s23208642
  16. C. Xu, J. Shen, X. Du, Low-rate DoS attack Detection Method based on Hybrid Deep Neural Networks. Journal of Information Security and Applications, 60, (2021) 102879. https://doi.org/10.1016/j.jisa.2021.102879
  17. A. Bhardwaj, V. Mangat, R. Vig, Hyperband tuned Deep Neural Network with well Posed Stacked Sparse Autoencoder for detection of DDoS attacks in Cloud. IEEE Access, 8, (2020) 181916-181929. https://doi.org/10.1109/ACCESS.2020.3028690
  18. S. Priya, R. Ponmagal, Network Intrusion Detection System based Security System for Cloud Services using Novel Recurrent Neural Network-Autoencoder (nrnn-ae) and Genetic. Advances in Science and Technology, 124, (2023) 729-737. https://doi.org/10.4028/p-076960
  19. E. Deniz, S. Serttaş, Deep learning-based Distributed Denial of Service Detection System in the cloud network. Journal of Scientific Reports-A, 055, (2023) 16-33. https://doi.org/10.59313/jsr-a.1333839
  20. X. Yin, W. Fang, Z. Liu, D. Liu, A novel multi-scale CNN and Bi-LSTM Arbitration Dense Network model for Low-Rate Ddos Attack Detection. Scientific Reports, 14(1), (2024) 5111. https://doi.org/10.1038/s41598-024-55814-y
  21. P. Sathishkumar, A. Gnanabaskaran, M. Saradha, R. Gopinath, DoS Attack Detection using Fuzzy Temporal Deep Long Short-Term Memory Algorithm in Wireless Sensor Network. Ain Shams Engineering Journal, 15(12), (2024) 103052. https://doi.org/10.1016/j.asej.2024.103052
  22. S. Mazumder, S. Neogy, T. Sur, S. Das, A Comparative Assessment of Deep Learning for adaptable DDoS threat detection in cloud computing systems. SN Computer Science, 6(1), (2025) 80. https://doi.org/10.1007/s42979-024-03643-1
  23. P. Kumar, C. Kushwaha, D. Sethi, D. Ghosh, P. Gupta, A. Vidyarthi, Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack. PLoS ONE 20(1) (2025) e0313930. https://doi.org/10.1371/journal.pone.0313930
  24. S. Hochreiter, J. Schmidhuber, Long Short-Term Memory. Neural Computation, 9(8), (1997) 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  25. X.-S. Yang, Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2009. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 5792, (2009) 169-178. https://doi.org/10.1007/978-3-642-04944-6_14
  26. X.-S. Yang, A. Slowik, (2020) Firefly Algorithm. In: Swarm Intelligence Algorithms, CRC Press, 163-174.
  27. J. Revathy, S.K. Jayanthi, (2024) Optimized Long Short-Term Memory Approach using Partial opposition-based Particle Swarm Optimization for Diabetes Detection. In: 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), IEEE, Salem, India. https://doi.org/10.1109/ICAAIC60222.2024.10575455
  28. X.-S. Yang, X. He, Firefly Algorithm: Recent Advances and Applications. International Journal of Swarm Intelligence, 1(1), (2013) 36-50. https://doi.org/10.1504/IJSI.2013.055801
  29. A.M. Anter, M. Ali, Feature Selection Strategy based on Hybrid Crow Search Optimization Algorithm Integrated with Chaos Theory and Fuzzy c-Means Algorithm for Medical Diagnosis Problems. Soft Computing, 24(3), (2020) 1565-1584. https://doi.org/10.1007/s00500-019-03988-3
  30. B. Shao, M. Li, Y. Zhao, G. Bian, Nickel price forecast based on the LSTM neural network optimized by the improved PSO algorithm. Mathematical Problems in Engineering, 2019(1), (2019) 1934796. https://doi.org/10.1155/2019/1934796
  31. P. Wang, J. Zhao, Y. Gao, M.A. Sotelo, Z. Li, Lane Work-schedule of Toll Station based on Queuing Theory and PSO-LSTM model. IEEE Access, IEEE, 8, (2020) 84434-84443. https://doi.org/10.1109/ACCESS.2020.2992070
  32. A.S. Santra, J.-L. Lin, Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting. Energies, 12(11), (2019) 2040. https://doi.org/10.3390/en12112040
  33. X. Liang, T. Znati, (2019)A Long Short-Term Memory Enabled Framework for DDoS detection. In: 2019 IEEE Global Communications Conference (GLOBECOM), IEEE, Waikoloa, HI, USA. https://doi.org/10.1109/GLOBECOM38437.2019.9013450
  34. R.M. Saad, M. Anbar, S. Manickam, E. Alomari, An intelligent ICMPv6 DDoS Flooding-Attack Detection Framework (V6IIDS) using back-Propagation Neural Network. IETE Technical Review, 33(3), (2016) 244-255. https://doi.org/10.1080/02564602.2015.1098576
  35. H.A. Sakr, M.M. El-Tahlawi, A. Abdelhafeez, M.I. El-Afifi, M.R. Abdellah, Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems. Egyptian Informatics Journal, 28, (2024) 100540. https://doi.org/10.1016/j.eij.2024.100540
  36. M. Tavallaee, E. Bagheri, W. Lu, A.A. Ghorbani, (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, IEEE, Ottawa, ON, Canada. https://doi.org/10.1109/CISDA.2009.5356528
  37. A. Shiravi, H. Shiravi, M. Tavallaee, A.A. Ghorbani, Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Computers & Security, 31(3), (2012) 357-374. https://doi.org/10.1016/j.cose.2011.12.012
  38. N. Moustafa, J. Slay, (2015) UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), IEEE, Canberra, ACT, Australia. https://doi.org/10.1109/MilCIS.2015.7348942
  39. I. Sharafaldin, A.H. Lashkari, A.A. Ghorbani, Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSP, 1, (2018) 108-116. https://doi.org/10.5220/0006639801080116
  40. K. Velusamy, R. Amalraj, Cascade correlation neural network with deterministic weight modification for predicting stock market price. In: IOP Conference Series: Materials Science and Engineering, 1110(1), (2021) 012005. https://doi.org/10.1088/1757-899X/1110/1/012005
  41. K. Velusamy, R. Amalraj, (2017) Performance of the Cascade Correlation Neural Network for Predicting the Stock Price. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), IEEE, Coimbatore, India. https://doi.org/10.1109/ICECCT.2017.8117824
  42. H. Wang, W. Wang, H. Sun, Firefly Algorithm with Generalised Opposition-based Learning. International Journal of Wireless and Mobile Computing, 9(4), (2015) 370-376. https://doi.org/10.1504/IJWMC.2015.074028