Abstract

DDoS attacks are among the most dangerous dangers to the digital world, according to recent theoretical and empirical research. Over time, DDoS attack mitigation strategies have developed to guarantee security. In the past, several traditional techniques, including heuristics and signatures, were employed to detect DDoS attacks encoded with different characteristics. The advanced obfuscation strategies used by new generations of DDoS attackers were too formidable for detection tools designed for traditional DDoS attacks. Since DL-based systems beat traditional DDoS attack detection techniques in discovering novel DDoS attack variations, Deep Learning (DL) is being employed more and more in DDoS attacks. Additionally, DL-based methods offer quick DDoS attack prediction together with superior detection rates and DDoS attack analysis. Thus, this work is interested in examining recently suggested DL-based DDoS attack detection systems and their development. It provides a comprehensive examination of the most current advances in DL-based detection methods. This survey's main objective is to give readers a thorough grasp of the applications of DL for detection. The outcome of this review discusses various DL methods, their strengths and weaknesses, datasets, challenges of recent research work, and future enhancements of present works.

Keywords

DDoS Attack Detection, Network Security, Computer Network, Deep Learning,

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References

  1. P. Bojović, I. Bašičević, S. Ocovaj, M. Popović, A practical approach to detection of distributed denial-of-service attacks using a hybrid detection method. Computers & Electrical Engineering, 73, (2019) 84-96. https://doi.org/10.1016/j.compeleceng.2018.11.004
  2. J. Lansky, S. Ali, M. Mohammadi, M.K. Majeed, S.H.T. Karim, S. Rashidi, M. Hosseinzadeh, A. M. Rahmani, Deep learning-based intrusion detection systems: a systematic review. IEEE Access, 9, (2021) 101574-101599. https://doi.org/10.1109/ACCESS.2021.3097247
  3. T. A. Tuan, H. V. Long, L. H. Son, R. Kumar, I. Priyadarshini, N.T.K. Son, Performance evaluation of Botnet DDoS attack detection using machine learning. Evolutionary Intelligence, 13, (2020) 283-294. https://doi.org/10.1007/s12065-019-00310-w
  4. M.T. Hussan, G. V. Reddy, P. Anitha, A. Kanagaraj, P. Naresh, DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization. Cluster Computing, 27(4), (2024) 4469-4490. https://doi.org/10.1007/s10586-023-04187-4
  5. Y. Wu, D. Wei, J. Feng, Network attacks detection methods based on deep learning techniques: A survey. Security and Communication Networks, 2020(1), (2020) 8872923. https://doi.org/10.1155/2020/8872923
  6. A.A. Habib, A. Imtiaz, D. Tripura, M. O. Faruk, M.A. Hossain, I. Ara, S. Sarker, A.F.Z. Abadin, Distributed denial-of-service attack detection short review: issues, challenges, and recommendations. Bulletin of Electrical Engineering and Informatics, 14(1), (2025) 438-446. https://doi.org/10.11591/eei.v14i1.8377
  7. N. K. Almazmomi, Long short-term memory-based intrusion detection system using hybrid grid search and sequential chimp optimization algorithm-based hyperparameter tuning. Intelligent Decision Technologies, 19(2), (2025) 18724981241291422. https://doi.org/10.1177/18724981241291422
  8. Y. Omer, P. Jorge, (2023). DDoS threat report for 2023 Q4. The Cloudflare Blog. https://blog.cloudflare.com/ddos-threat-report-2023-q4/
  9. Q.A. Al‐Haija, A. Droos, A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT). Expert Systems, 42(2), (2025) e13726. https://doi.org/10.1111/exsy.13726
  10. K.M. Abuali, L. Nissirat, A. Al-Samawi, Advancing Network Security with AI: SVM-Based Deep Learning for Intrusion Detection. Sensors, 23(21), (2023) 8959. https://doi.org/10.3390/s23218959
  11. L.D. Tsobdjou, S. Pierre, A. Quintero, An online entropy-based DDoS flooding attack detection system with dynamic threshold. IEEE Transactions on Network and Service Management, 19(2), (2022) 1679-1689. https://doi.org/10.1109/TNSM.2022.3142254
  12. M. Solanki, S. Chaudhari, VLMDALP: design of an efficient VARMA LSTM-based model for identification of DDoS attacks using application-level packet analysis. International Journal of Electronic Security and Digital Forensics, 17(1-2), (2025) 149-168. https://doi.org/10.1504/IJESDF.2025.143476
  13. R. Alguliyev, R. Shikhaliyev, Computer Networks Cybersecurity Monitoring Based on Deep Learning Model," Security and Privacy, 8(1), (2025) e459. https://doi.org/10.1002/spy2.459
  14. S. Kansal, Utilizing Deep Learning Techniques for Effective Zero-Day Attack Detection. Economic Sciences, 21(1), (2025) 246-257. https://doi.org/10.69889/m3jzbt24
  15. Z. Xu, Deep Learning Based DDoS Attack Detection. in ITM Web of Conferences, 70, (2025) 03005. https://doi.org/10.1051/itmconf/20257003005
  16. J. Yan, H. Zhou, W. Wang, Intelligent Network Element: A Programmable Switch Based on Machine Learning to Defend Against DDoS Attacks. Information Systems Frontiers, (2025) 1-20. https://doi.org/10.1007/s10796-024-10577-9
  17. S. Aktar, A.Y. Nur, Towards DDoS attack detection using deep learning approach. Computers & Security, 129, (2023) 103251. https://doi.org/10.1016/j.cose.2023.103251
  18. M. Mittal, K. Kumar, S. Behal, Deep learning approaches for detecting DDoS attacks: A systematic review. Soft computing, 27(18), (2023) 13039-13075. https://doi.org/10.1007/s00500-021-06608-1
  19. M.A. Al-Shareeda, S. Manickam, M.A. Saare, DDoS attacks detection using machine learning and deep learning techniques: Analysis and comparison. Bulletin of Electrical Engineering and Informatics, 12(2), (2023) 930-939. https://doi.org/10.11591/eei.v12i2.4466
  20. S. Hosseini, M. Azizi, The hybrid technique for DDoS detection with supervised learning algorithms. Computer Networks, 158, (2019) 35-45. https://doi.org/10.1016/j.comnet.2019.04.027
  21. V. Hnamte, A.A. Najar, H. Nhung-Nguyen, J. Hussain, M.N. Sugali, DDoS attack detection and mitigation using deep neural network in SDN environment. Computers & Security, 138, (2024) 103661. https://doi.org/10.1016/j.cose.2023.103661
  22. D. Krishnan, S. Hemamalini, P. Cheraku, K. H. Priya, S. Ganesan, R. Balamanigandan, Attack detection using DL based feature selection with improved convolutional neural network. International Journal of Electrical and Electronics Research, 11(2), (2023) 308-314. https://ijeer.forexjournal.co.in/archive/volume-11/ijeer-110209.html
  23. H. Aydın, Z. Orman, M. A. Aydın, A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment. Computers & Security, 118, (2022) 102725. https://doi.org/10.1016/j.cose.2022.102725
  24. N.A. Bajao, J.a. Sarucam, Threats Detection in the Internet of Things Using Convolutional neural networks, long short-term memory, and gated recurrent units. Mesopotamian journal of cybersecurity, 2023, (2023) 22-29. https://doi.org/10.58496/MJCS/2023/005
  25. D.M.B. Lent, M.P. Novaes, L.F. Carvalho, J. Lloret, J.J. Rodrigues, M.L. Proença, A gated recurrent unit deep learning model to detect and mitigate distributed denial of service and portscan attacks. IEEE Access, 10, (2022) 73229-73242. https://doi.org/10.1109/ACCESS.2022.3190008
  26. A.E. Cil, K. Yildiz, A. Buldu, Detection of DDoS attacks with feed forward based deep neural network model. Expert Systems with Applications, 169, (2021) 114520. https://doi.org/10.1016/j.eswa.2020.114520
  27. D. C. Le, N. Zincir-Heywood, M. I. Heywood, "Unsupervised monitoring of network and service behaviour using self-organizing maps. Journal of Cyber Security and Mobility, (2019) 15-52.
  28. D. M. B. Lent, V. G. D. S. Ruffo, L. F. Carvalho, J. Lloret, J. J. Rodrigues, M. L. Proença, An Unsupervised Generative Adversarial Network System to Detect DDoS Attacks in SDN. IEEE Access, (2024) 70690-70706. https://doi.org/10.1109/ACCESS.2024.3402069
  29. R.F. Fouladi, O. Ermiş, E. Anarim, A DDoS attack detection and countermeasure scheme based on DWT and auto-encoder neural network for SDN. Computer Networks, 214, (2022) 109140. https://doi.org/10.1016/j.comnet.2022.109140
  30. X. Qu, L. Yang, K. Guo, L. Ma, M. Sun, M. Ke, M. Li, A survey on the development of self-organizing maps for unsupervised intrusion detection. Mobile networks and applications, 26, (2021) 808-829. https://doi.org/10.1007/s11036-019-01353-0
  31. S. Velliangiri, H.M. Pandey, 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, 110, (2020) 80-90. https://doi.org/10.1016/j.future.2020.03.049
  32. M. Aamir, S.M.A. Zaidi, Clustering based semi-supervised machine learning for DDoS attack classification," Journal of King Saud University-Computer and Information Sciences, 33(4), (2021) 436-446. https://doi.org/10.1016/j.jksuci.2019.02.003
  33. A.A. Samsu Aliar, M. Agoramoorthy, An automated detection of DDoS attack in cloud using optimized weighted fused features and hybrid DBN-GRU architecture. Cybernetics and Systems, 55(7), (2024) 1469-1510. https://doi.org/10.1080/01969722.2022.2157603
  34. L. Chen, Z. Wang, R. Huo, T. Huang, An adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments. Algorithms, 16(4), (2023) 197. https://doi.org/10.3390/a16040197
  35. Y. Wei, J. Jang-Jaccard, F. Sabrina, A. Singh, W. Xu, S. Camtepe, Ae-mlp: A hybrid deep learning approach for ddos detection and classification. IEEE Access, 9, (2021) 146810-146821. https://doi.org/10.1109/ACCESS.2021.3123791
  36. B. Hussain, Q. Du, B. Sun, Z. Han, Deep learning-based DDoS-attack detection for cyber–physical system over 5G network. IEEE Transactions on Industrial Informatics, 17(2), (2020) 860-870. https://doi.org/10.1109/TII.2020.2974520
  37. 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
  38. 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
  39. I. Ortega-Fernandez, M. Sestelo, J.C. Burguillo, C. Piñón-Blanco, Network intrusion detection system for DDoS attacks in ICS using deep autoencoders. Wireless Networks, 30, (2023) 1-17. https://doi.org/10.1007/s11276-022-03214-3
  40. E. Benmohamed, A. Thaljaoui, S. El Khediri, S. Aladhadh, M. Alohali, DDoS attacks detection with half autoencoder-stacked deep neural network. International Journal of Cooperative Information Systems, 33(3), (2023) 2350025. https://doi.org/10.1142/S0218843023500259
  41. A.K. Mousa, M.N. Abdullah, An improved deep learning model for DDoS detection based on hybrid stacked autoencoder and checkpoint network. Future Internet, 15(8), (2023) 278. https://doi.org/10.3390/fi15080278
  42. E. Benmohamed, A. Thaljaoui, S. Elkhediri, S. Aladhadh, M. Alohali, E-SDNN: encoder-stacked deep neural networks for DDOS attack detection. Neural Computing and Applications, 36, (2024) 10431–10443. https://doi.org/10.1007/s00521-024-09622-0
  43. R.K. Batchu, H. Seetha, A hybrid detection system for DDoS attacks based on deep sparse autoencoder and light gradient boost machine. Journal of Information & Knowledge Management, 22(1), (2023) 2250071. https://doi.org/10.1142/S021964922250071X
  44. S. Balasubramaniam, C. Vijesh Joe, T.A. Sivakumar, A. Prasanth, K. Satheesh Kumar, V. Kavitha, R.K.Dhanaraj, Optimization enabled deep learning‐based ddos attack detection in cloud computing. International Journal of Intelligent Systems, 2023(1), (2023) 2039217. https://doi.org/10.1155/2023/2039217
  45. C.M.V.S. Akana, A. Kumar, M. Tiwari, A.Z. Yunus, E. Vijayakumar, M. Singh, (2023) An Optimized DDoS Attack Detection Using Deep Convolutional Generative Adversarial Networks. International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, India. https://doi.org/10.1109/ICIRCA57980.2023.10220745
  46. A. Kandiero, P. Chiurunge, J. Munodawafa, Detection of DDoS Attacks Using Variational Autoencoder-Based Deep Neural Network. Privacy Preservation and Secured Data Storage in Cloud Computing: IGI Global, (2023) 365-404. https://doi.org/10.4018/979-8-3693-0593-5.ch017
  47. U. Shrivastav, M. Kumar, S. Kumar, An Autoencoder-based Efficient Scheme for DDoS Detection. International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), IEEE, India. https://doi.org/10.1109/IC2E357697.2023.10262806
  48. M.A. Hossain M.S. Islam, Ensuring network security with a robust intrusion detection system using ensemble-based machine learning. Array, 19, (2023) 100306. https://doi.org/10.1016/j.array.2023.100306
  49. Z. Hu, L. Wang, L. Qi, Y. Li, W. Yang, A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network. IEEE Access, 8, (2020) 195741-195751. https://doi.org/10.1109/ACCESS.2020.3034015
  50. R. Doriguzzi-Corin, S. Millar, S. Scott-Hayward, J. Martinez-del-Rincon, D. Siracusa, LUCID: A practical, lightweight deep learning solution for DDoS attack detection. IEEE Transactions on Network and Service Management, 17(2), (2020) 876-889. https://doi.org/10.1109/TNSM.2020.2971776
  51. F.O. Catak, A.F. Mustacoglu, Distributed denial of service attack detection using autoencoder and deep neural networks. Journal of Intelligent & Fuzzy Systems, 37(3), (2019) 3969-3979. https://doi.org/10.3233/JIFS-190159
  52. 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
  53. A.A. Awad, A.F. Ali, T. Gaber, An improved long short term memory network for intrusion detection. Plos one, 18(8), (2023) e0284795. https://doi.org/10.1371/journal.pone.0284795
  54. F. Laghrissi, S. Douzi, K. Douzi, B. Hssina, Intrusion detection systems using long short-term memory (LSTM). Journal of Big Data, 8(1), (2021) 65. https://doi.org/10.1186/s40537-021-00448-4
  55. S. Sumathi, R. Rajesh, S. Lim, Recurrent and deep learning neural network models for DDoS attack detection. Journal of Sensors, (2022). https://doi.org/10.1155/2022/8530312
  56. M.Z. Alom, T.M. Taha, Network intrusion detection for cyber security using unsupervised deep learning approaches. IEEE national aerospace and electronics conference (NAECON), IEEE, USA. https://doi.org/10.1109/NAECON.2017.8268746
  57. R. Vinayakumar, M. Alazab, K. Soman, P. Poornachandran, A. Al-Nemrat, S. Venkatraman, Deep learning approach for intelligent intrusion detection system. Ieee Access, 7 (2019) 41525-41550. https://doi.org/10.1109/ACCESS.2019.2895334
  58. T. Su, H. Sun, J. Zhu, S. Wang, Y. Li, BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access, 8 (2020) 29575-29585. https://doi.org/10.1109/ACCESS.2020.2972627
  59. D. Akgun, S. Hizal, U. Cavusoglu, A new DDoS attacks intrusion detection model based on deep learning for cybersecurity. Computers & Security, 118, (2022) 102748. https://doi.org/10.1016/j.cose.2022.102748
  60. S. Shende, S. Thorat, Long short-term memory (LSTM) deep learning method for intrusion detection in network security. International Journal of Engineering Research and Technology, 9(6), (2020). https://doi.org/10.17577/IJERTV9IS061016
  61. Y. Imrana, Y. Xiang, L. Ali, Z. Abdul-Rauf, A bidirectional LSTM deep learning approach for intrusion detection. Expert Systems with Applications, 185, (2021) 115524. https://doi.org/10.1016/j.eswa.2021.115524
  62. A. Halbouni, T.S. Gunawan, M.H. Habaebi, M. Halbouni, M. Kartiwi, R. Ahmad, CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE Access, 10, (2022) 99837-99849. https://doi.org/10.1109/ACCESS.2022.3206425
  63. T. Pooja, P. Shrinivasacharya, Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security. Global Transitions Proceedings, 2(2), (2021) 448-454. https://doi.org/10.1016/j.gltp.2021.08.017
  64. H. Gwon, C. Lee, R. Keum, H. Choi, (2019) Network intrusion detection based on LSTM and feature embedding. arXiv preprint arXiv:1911.11552. https://doi.org/10.48550/arXiv.1911.11552
  65. M.A. Khan, M.R. Karim, Y. Kim, A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry, 11(4), (2019) 583. https://doi.org/10.3390/sym11040583
  66. A.T. Assy, Y. Mostafa, A. Abd El-khaleq, M. Mashaly, Anomaly-Based Intrusion Detection System using One-Dimensional Convolutional Neural Network. Procedia Computer Science, 220, (2023) 78-85. https://doi.org/10.1016/j.procs.2023.03.013
  67. W. Elmasry, A. Akbulut, A.H. Zaim, Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Computer Networks, 168, (2020) 107042. https://doi.org/10.1016/j.comnet.2019.107042
  68. Y. Zhang, Y. Zhang, N. Zhang, M. Xiao, A network intrusion detection method based on deep learning with higher accuracy. Procedia Computer Science, 174, (2020) 50-54. https://doi.org/10.1016/j.procs.2020.06.055
  69. S.M. Kasongo, A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Computer Communications, 199, (2023) 113-125. https://doi.org/10.1016/j.comcom.2022.12.010
  70. G.S.C. Kumar, R.K. Kumar, K.P.V. Kumar, N.R. Sai, M. Brahmaiah, Deep residual convolutional neural Network: An efficient technique for intrusion detection system. Expert Systems with Applications, 238, (2024) 121912. https://doi.org/10.1016/j.eswa.2023.121912
  71. R.U. Khan, X. Zhang, M. Alazab, R. Kumar, (2019) An improved convolutional neural network model for intrusion detection in networks. Cybersecurity and cyberforensics conference (CCC), IEEE, Australia. https://doi.org/10.1109/CCC.2019.000-6
  72. A. Abusitta, M. Bellaiche, M. Dagenais, T. Halabi, A deep learning approach for proactive multi-cloud cooperative intrusion detection system. Future Generation Computer Systems, 98, (2019) 308-318. https://doi.org/10.1016/j.future.2019.03.043
  73. S. Al, M. Dener, STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment. Computers & Security, 110, (2021) 102435. https://doi.org/10.1016/j.cose.2021.102435
  74. G. de Carvalho Bertoli, L. A. P. Junior, O. Saotome, A. L. dos Santos, Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach. Computers & Security, 127, (2023) 103106. https://doi.org/10.1016/j.cose.2023.103106
  75. C. Ieracitano, A. Adeel, F.C. Morabito, A. Hussain, A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing, 387, (2020) 51-62. https://doi.org/10.1016/j.neucom.2019.11.016
  76. I.O. Lopes, D. Zou, I.H. Abdulqadder, S. Akbar, Z. Li, F. Ruambo, W. Pereira, Network intrusion detection based on the temporal convolutional model. Computers & Security, 135, (2023) 103465. https://doi.org/10.1016/j.cose.2023.103465
  77. S.V. Pingale, S.R. Sutar, Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features. Expert Systems with Applications, 210, (2022) 118476. https://doi.org/10.1016/j.eswa.2022.118476
  78. A. Thakkar R. Lohiya, Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System. Information Fusion, 90, (2023) 353-363. https://doi.org/10.1016/j.inffus.2022.09.026
  79. A. Alsirhani, M. M. Alshahrani, A.M. Hassan, A.I. Taloba, R.M. Abd El-Aziz, A.H. Samak, "Implementation of African vulture optimization algorithm based on deep learning for cybersecurity intrusion detection. Alexandria Engineering Journal, 79, (2023) 105-115. https://doi.org/10.1016/j.aej.2023.07.077
  80. J. Lan, X. Liu, B. Li, J. Sun, B. Li, J. Zhao, MEMBER: A multi-task learning model with hybrid deep features for network intrusion detection. Computers & Security, 123, (2022) 102919. https://doi.org/10.1016/j.cose.2022.102919
  81. F.E. Ayo, S.O. Folorunso, A.A. Abayomi-Alli, A.O. Adekunle, J.B. Awotunde, Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection. Information Security Journal: A Global Perspective, 29(6), (2020) 267-283. https://doi.org/10.1080/19393555.2020.1767240
  82. Z. Li, C. Huang, W. Qiu, An intrusion detection method combining variational auto-encoder and generative adversarial networks. Computer Networks, (2024) 110724. https://doi.org/10.1016/j.comnet.2024.110724
  83. S. Ur Rehman, M. Khaliq, S.I. Imtiaz, A. Rasool, M. Shafiq, A.R. Javed, Z. Jalil, A.K. Bashir, DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU). Future Generation Computer Systems, 118, (2021) 453-466. https://doi.org/10.1016/j.future.2021.01.022
  84. A.F. Al-zubidi, A.K. Farhan, S.M. Towfek, Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model. Journal of Intelligent Systems, 33(1), (2024) 20230195. https://doi.org/10.1515/jisys-2023-0195
  85. P. Sun, P. Liu, Q. Li, C. Liu, X. Lu, R. Hao, J. Chen, DL‐IDS: Extracting Features Using CNN‐LSTM Hybrid Network for Intrusion Detection System. Security and communication networks, 2020(1), (2020) 8890306. https://doi.org/10.1155/2020/8890306
  86. S.S. Bamber, A.V.R. Katkuri, S. Sharma, M. Angurala, A hybrid CNN-LSTM approach for intelligent cyber intrusion detection system. Computers & Security, 148, (2025) 104146. https://doi.org/10.1016/j.cose.2024.104146
  87. D. Alghazzawi, O. Bamasag, H. Ullah, M. Z. Asghar, Efficient detection of DDoS attacks using a hybrid deep learning model with improved feature selection. Applied Sciences, 11(24), (2021) 11634. https://doi.org/10.3390/app112411634
  88. B. Deore, S. Bhosale, Hybrid optimization enabled robust CNN-LSTM technique for network intrusion detection. Ieee Access, 10, (2022) 65611-65622. https://doi.org/10.1109/ACCESS.2022.3183213
  89. J. Kaur, B.S. Khehra, A. Singh, Back propagation artificial neural network for diagnose of the heart disease. Journal of Reliable Intelligent Environments, 9(1), (2023) 57-85. https://doi.org/10.1007/s40860-022-00192-3
  90. A.S.A. Issa, Z. Albayrak, DDoS attack intrusion detection system based on hybridization of CNN and LSTM. Acta Polytechnica Hungarica, 20(2), (2023) 105-123. https://doi.org/10.12700/APH.20.2.2023.2.6