EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 43 (2022) ITM Web Conf., 43 (2022) 01003 References
Open Access
Issue
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
Article Number 01003
Number of page(s) 10
DOI https://doi.org/10.1051/itmconf/20224301003
Published online 14 March 2022
  1. F. Salo, M. Injadat, A.B. Nassif, and A. Essex, “Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review,” in International Symposium on Big Data Management and Analytics 2019, BIDMA 2019, 2020. [Google Scholar]
  2. S. Venticinque and A. Amato, “Smart Sensor and Big Data Security and Resilience,” in Security and Resilience in Intelligent Data-Centric Systems and Communication Networks, Elsevier, 2018, pp. 123–141. [CrossRef] [Google Scholar]
  3. A. Drewek-Ossowicka, M. Pietrołaj, and J. Rumiński, “A survey of neural networks usage for intrusion detection systems,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 1, pp. 497–514, 2020, doi:10.1007/s12652-020-02014-x. [Google Scholar]
  4. K. Kim and M.E. Aminanto, “Deep learning in intrusion detection perspective: Overview and further challenges,” in Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security, 2017, pp. 5–10, doi:10.1109/IWBIS.2017.8275095. [Google Scholar]
  5. H. Liu and B. Lang, “Machine Learning and Deep Learning Methods for Intrusion Detection Systems : A Survey,” Appl. Sci., vol. 9, pp. 1–28, 2019, doi:10.3390/app9204396. [Google Scholar]
  6. J. Kim, J. Kim, H. Kim, M. Shim, and E. Choi, “CNN-based network intrusion detection against denial-of-service attacks,” Electron., vol. 9, no. 6, pp. 1–21, 2020, doi:10.3390/electronics9060916. [Google Scholar]
  7. R. Upadhyay and D.V. Pantiukhin, “Application of Convolutional neural networks to intrusion type recognition,” 2017. [Google Scholar]
  8. M.A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” J. Inf. Secur. Appl., vol. 50, p. 102419, 2020, doi:10.1016/j.jisa.2019.102419. [Google Scholar]
  9. S. Gamage and J. Samarabandu, “Deep learning methods in network intrusion detection: A survey and an objective comparison,” J. Netw. Comput. Appl., vol. 169, no. July, p. 102767, 2020, doi:10.1016/j.jnca.2020.102767. [CrossRef] [Google Scholar]
  10. Y. Wu, D. Wei, and J. Feng, “Network attacks detection methods based on deep learning techniques: A survey,” Secur. Commun. Networks, 2020, doi:10.1155/2020/8872923. [Google Scholar]
  11. A. A. A. Lateef, S. T. F. Al-Janabi, and B. Al-Khateeb, “Survey on intrusion detection systems based on deep learning,” Period. Eng. Nat. Sci., vol. 7, no. 3, pp. 1074–1095, 2019, doi:10.21533/pen.v7i3.635. [Google Scholar]
  12. F. Salo, M. Injadat, A.B. Nassif, A. Shami, and A. Essex, “Data mining techniques in intrusion detection systems: A systematic literature review,” IEEE Access, vol. 6, pp. 56046–56058, 2018, doi:10.1109/ACCESS.2018.2872784. [CrossRef] [Google Scholar]
  13. G. Kocher and G. Kumar, “Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges,” Soft Comput., vol. 25, no. 15, pp. 9731–9763, 2021, doi:10.1007/s00500-021-05893-0. [CrossRef] [Google Scholar]
  14. 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, pp. 1–29, 2020, doi:10.1002/ett.4150. [Google Scholar]
  15. Barbara Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” 2007, doi:10.1145/1134285.1134500. [Google Scholar]
  16. R. Vinayakumar, K.P. Soman, and P. Poornachandrany, “Applying convolutional neural network for network intrusion detection,” in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017, vol. 2017-Janua, pp. 1222–1228, doi:10.1109/ICACCI.2017.8126009. [Google Scholar]
  17. Y. Liu, S. Liu, and X. Zhao, “Intrusion Detection Algorithm Based on Convolutional Neural Network,” in 4th International Conference on Engineering Technology and Application (ICETA 2017), 2017, vol. 37, no. 12, pp. 1271–1275, doi:10.15918/j.tbit1001-0645.2017.12.011. [Google Scholar]
  18. W. Wang et al., “HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection,” IEEE Access, vol. 6, pp. 1792–1806, 2017, doi:10.1109/ACCESS.2017.2780250.. [Google Scholar]
  19. L. Mohammadpour, T.C. Ling, C.S. Liew, and C.Y. Chong, “A Convolutional Neural Network for Network Intrusion Detection System,” in Proceedings of the Asia-Pacific Advanced Network, 2018, vol. 46, no. 0, pp. 50–55. [Google Scholar]
  20. W.-H. Lin, H.-C. Lin, P. Wang, B.-H. Wu, and J.-Y. Tsai, “Using convolutional neural networks to network intrusion detection for cyber threats,” in Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, 2018, pp. 1107–1110, doi:10.1109/ICASI.2018.8394474. [Google Scholar]
  21. S. Naseer and Y. Saleem, “Enhanced network intrusion detection using deep convolutional neural networks,” KSII Trans. Internet Inf. Syst., vol. 12, no. 10, pp. 5159–5178, 2018, doi:10.3837/tiis.2018.10.028. [Google Scholar]
  22. Y. Ding and Y. Zhai, “Intrusion detection system for NSL-KDD dataset using convolutional neural networks,” in ACM International Conference Proceeding Series, 2018, pp. 81–85, doi:10.1145/3297156.3297230. [Google Scholar]
  23. K. Wu, Z. Chen, and W. Li, “A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks,” IEEE Access, vol. 6, pp. 50850–50859, 2018, doi:10.1109/ACCESS.2018.2868993. [CrossRef] [Google Scholar]
  24. S. Naseer et al., “Enhanced network anomaly detection based on deep neural networks,” IEEE Access, vol. 6, pp. 48231–48246, 2018, doi:10.1109/ACCESS.2018.2863036. [CrossRef] [Google Scholar]
  25. S. Behera, A. Pradhan, and R. Dash, “Deep Neural Network Architecture for Anomaly Based Intrusion Detection System,” in 2018 5th International Conference on Signal Processing and Integrated Networks, SPIN 2018, 2018, pp. 270–274, doi:10.1109/SPIN.2018.8474162. [Google Scholar]
  26. M. M. U. Chowdhury, F. Hammond, G. Konowicz, C. Xin, H. Wu, and J. Li, “A few-shot deep learning approach for improved intrusion detection,” in 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017, 2018, vol. 2018-Janua, pp. 1–7, doi:10.1109/UEMCON.2017.8249084. [Google Scholar]
  27. R. Blanco, J.J. Cilla, P. Malagón, I. Penas, and J.M. Moya, “Tuning CNN input layout for IDS with genetic algorithms,” Springer Int. Publ. AG, part Springer Nat. 2018, vol. 10870 LNAI, pp. 197–209, 2018, doi:10.1007/978-3-319-92639-1_17. [Google Scholar]
  28. S.-N. Nguyen, V.-Q. Nguyen, J. Choi, and K. Kim, “Design and implementation of intrusion detection system using convolutional neural network for DoS detection,” in International Conference on Advanced Machine Learning and Soft Computing (ICMLSC), 2018, pp. 34–38, doi:10.1145/3184066.3184089. [Google Scholar]
  29. A.K. Verma, P. Kaushik, and G. Shrivastava, “A Network Intrusion Detection Approach Using Variant of Convolution Neural Network,” in Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019, 2019, pp. 409–416, doi:10.1109/ICCES45898.2019.9002221. [Google Scholar]
  30. R.U. Khan, X. Zhang, M. Alazab, and R. Kumar, “An improved convolutional neural network model for intrusion detection in networks,” in Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019, 2019, no. Ccc, pp. 74–77, doi: 10.1109/CCC.2019.000-6. [CrossRef] [Google Scholar]
  31. P. Wu and H. Guo, “LuNet: A Deep Neural Network for Network Intrusion Detection,” in 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, 2019, pp. 617–624, doi:10.1109/SSCI44817.2019.9003126. [CrossRef] [Google Scholar]
  32. Y. Xiao, C. Xing, T. Zhang, and Z. Zhao, “An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks,” IEEE Access, vol. 7, pp. 42210–42219, 2019, doi:10.1109/ACCESS.2019.2904620. [CrossRef] [Google Scholar]
  33. Y. Li and B. Zhang, “An intrusion detection model based on multi-scale CNN,” in Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019, 2019, pp. 214–218, doi:10.1109/ITNEC.2019.8729261. [Google Scholar]
  34. X. Zhang, J. Ran, and J. Mi, “An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic,” in Proceedings of IEEE 7th International Conference on Computer Science and Network Technology, ICCSNT 2019, 2019, pp. 456–460, doi:10.1109/ICCSNT47585.2019.8962490. [Google Scholar]
  35. N. Chouhan, A. Khan, and H.-U.-R. Khan, “Network anomaly detection using channel boosted and residual learning based deep convolutional neural network,” Appl. Soft Comput. J., vol. 83, 2019, doi:10.1016/j.asoc.2019.105612. [CrossRef] [Google Scholar]
  36. X. Zhang, J. Chen, Y. Zhou, L. Han, and J. Lin, “A Multiple-Layer Representation Learning Model for Network-Based Attack Detection,” IEEE Access, vol. 7, pp. 91992–92008, 2019, doi:10.1109/ACCESS.2019.2927465. [CrossRef] [Google Scholar]
  37. C.-M. Hsu, H.-Y. Hsieh, S.W. Prakosa, M.Z. Azhari, and J.-S. Leu, “Using long-short-term memory based convolutional neural networks for network intrusion detection,” in 11th EAI International Wireless Internet Conference, 2019, vol. 264, pp. 86–94, doi:10.1007/978-3-030-06158-6_9. [CrossRef] [Google Scholar]
  38. L. Zhang, M. Li, X. Wang, and Y. Huang, “An Improved Network Intrusion Detection Based on Deep Neural Network,” in IOP Conference Series: Materials Science and Engineering, 2019, vol. 563, no. 5, doi:10.1088/1757-899X/563/5/052019. [Google Scholar]
  39. P. Liu, “An intrusion detection system based on convolutional neural network,” in 11th International Conference on Computer and Automation Engineering (ICCAE), 2019, pp. 62–67, doi:10.1145/3313991.3314009. [Google Scholar]
  40. X. Xie, X. Jiang, W. Wang, B. Wang, T. Wan, and H. Yang, “An intrusion detection method based on hierarchical feature learning and its application,” in 11th International Symposium on Cyberspace Safety and Security (CSS), 2019, vol. 11982 LNCS, pp. 13–20, doi:10.1007/978-3-030-37337-5_2. [CrossRef] [Google Scholar]
  41. B. Alsughayyir and A.M. Qamar, “Deep learning-based network attack detection using convolutional and recurrent neural networks,” Int. J. Eng. Res. Technol., vol. 12, no. 12, pp. 3027–3303, 2019. [Google Scholar]
  42. J. Zhang, Y. Ling, X. Fu, X. Yang, G. Xiong, and R. Zhang, “Model of the intrusion detection system based on the integration of spatial-temporal features,” Comput. Secur., vol. 89, 2019, doi:10.1016/j.cose.2019.101681. [Google Scholar]
  43. L. Heng and T. Weise, “Intrusion Detection System Using Convolutional Neuronal Networks: A Cognitive Computing Approach for Anomaly Detection based on Deep Learning,” in Proceedings of 2019 IEEE 18th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019, 2019, pp. 34–40, doi:10.1109/ICCICC46617.2019.9146088. [CrossRef] [Google Scholar]
  44. H. Yang and F. Wang, “Wireless network intrusion detection based on improved convolutional neural network,” IEEE Access, vol. 7, pp. 64366–64374, 2019, doi:10.1109/ACCESS.2019.2917299. [CrossRef] [Google Scholar]
  45. X. Wang, S. Yin, H. Li, J. Wang, and L. Teng, “A Network Intrusion Detection Method Based on Deep Multi-scale Convolutional Neural Network,” Int. J. Wirel. Inf. Networks, vol. 27, no. 4, pp. 503–517, 2020, doi:10.1007/s10776-020-00495-3. [CrossRef] [Google Scholar]
  46. S. Al-Emadi, A. Al-Mohannadi, and F. Al-Senaid, “Using Deep Learning Techniques for Network Intrusion Detection,” in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020, 2020, pp. 171–176, doi:10.1109/ICIoT48696.2020.9089524. [Google Scholar]
  47. L. Mohammadpour, T.C. Ling, C.S. Liew, and A. Aryanfar, “A Mean Convolutional Layer for Intrusion Detection System,” Secur. Commun. Networks, vol. 2020, 2020, doi:10.1155/2020/8891185. [Google Scholar]
  48. A. Kim, M. Park, and D.H. Lee, “AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection,” IEEE Access, vol. 8, pp. 70245–70261, 2020, doi:10.1109/ACCESS.2020.2986882. [CrossRef] [Google Scholar]
  49. G. Liu and J. Zhang, “CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network,” Discret. Dyn. Nat. Soc., vol. 2020, 2020, doi:10.1155/2020/4705982. [Google Scholar]
  50. P. Sun et al., “DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system,” Secur. Commun. Networks, vol. 2020, 2020, doi:10.1155/2020/8890306. [Google Scholar]
  51. H. Wang, Z. Cao, and B. Hong, “A network intrusion detection system based on convolutional neural network,” J. Intell. Fuzzy Syst., vol. 38, no. 6, pp. 7623–7637, 2020, doi:10.3233/JIFS-179833. [CrossRef] [Google Scholar]
  52. M. T. Nguyen and K. Kim, “Genetic convolutional neural network for intrusion detection systems,” Futur. Gener. Comput. Syst., vol. 113, pp. 418–427, 2020, doi:10.1016/j.future.2020.07.042. [CrossRef] [Google Scholar]
  53. W.-F. Zheng, “Intrusion Detection Based on Convolutional Neural Network,” in 2020 International Conference on Computer Engineering and Application (ICCEA), 2020, pp. 273–277, doi:10.1109/ICCEA50009.2020.00066. [CrossRef] [Google Scholar]
  54. K. Jiang, W. Wang, A. Wang, and H. Wu, “Network Intrusion Detection Combined Hybrid Sampling with Deep Hierarchical Network,” IEEE Access, vol. 8, pp. 32464–32476, 2020, doi:10.1109/ACCESS.2020.2973730. [CrossRef] [Google Scholar]
  55. C.-M. Hsu, M.Z. Azhari, H.-Y. Hsieh, S.W. Prakosa, and J.-S. Leu, “Robust Network Intrusion Detection Scheme Using Long-Short Term Memory Based Convolutional Neural Networks,” Mob. Networks Appl., 2020, doi:10.1007/s11036-020-01623-2. [Google Scholar]
  56. V. Kumar, K. Rana, J. Malik, and A. Tomar, “Evaluating hybrid cnn-mlp architecture for analyzing novel network traffic attacks,” Int. J. Sci. Technol. Res., vol. 9, no. 3, pp. 4889–4896, 2020. [Google Scholar]
  57. H. Zhang, L. Huang, C.Q. Wu, and Z. Li, “An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset,” Comput. Networks, vol. 177, 2020, doi:10.1016/j.comnet.2020.107315. [CrossRef] [Google Scholar]
  58. B. Wang, Y. Su, M. Zhang, and J. Nie, “A deep hierarchical network for packet-level malicious traffic detection,” IEEE Access, vol. 8, pp. 201728–201740, 2020, doi:10.1109/ACCESS.2020.3035967. [CrossRef] [Google Scholar]
  59. P. Kumar, A.A. Kumar, C. Sahayakingsly, and A. Udayakumar, “Analysis of intrusion detection in cyber attacks using DEEP learning neural networks,” Peer-to-Peer Netw. Appl., 2020, doi:10.1007/s12083-020-00999-y. [Google Scholar]
  60. V. Maheshwar Reddy, I. Ravi Prakash Reddy, and K. Adi Narayana Reddy, “An efficient intrusion detection system with convolutional neural network,” Adv. Comput. Intell. Informatics, Lect. Notes Networks Syst., vol. 119, pp. 177–185, 2020, doi:10.1007/978-981-15-3338-9_22. [Google Scholar]
  61. W. Tao, W. Zhang, C. Hu, and C. Hu, “A Network Intrusion Detection Model Based on Convolutional Neural Network,” Adv. Intell. Syst. Comput., vol. 895, pp. 771–783, 2020, doi:10.1007/978-3-030-16946-6_63. [Google Scholar]
  62. S. Mishra, R. Dwivedula, V. Kshirsagar, and C. Hota, “Robust Detection of Network Intrusion using Tree-based Convolutional Neural Networks,” in ACM International Conference Proceeding Series, 2020, pp. 233–237, doi:10.1145/3430984.3431036. [Google Scholar]
  63. D. Niu, J. Zhang, L. Wang, K. Yan, T. Fu, and X. Chen, “A Network Traffic anomaly Detection method based on CNN and XGBoost,” in Proceedings - 2020 Chinese Automation Congress, CAC 2020, 2020, pp. 5453–5457, doi:10.1109/CAC51589.2020.9327030. [Google Scholar]
  64. X. Xie et al., “Research and application of intrusion detection method based on hierarchical features,” Concurr. Comput. Pract. Exp., 2020, doi:10.1002/cpe.5799. [Google Scholar]
  65. M. Ahsan and K.E. Nygard, “Convolutional neural networks with LSTM for intrusion detection,” Epic Ser. Comput., vol. 69, pp. 69–79, 2020, doi:10.29007/j35r. [CrossRef] [Google Scholar]
  66. V. Pham, E. Seo, and T.-M. Chung, “Lightweight convolutional neural network based intrusion detection system,” J. Commun., vol. 15, no. 11, pp. 808–817, 2020, doi:10.12720/jcm.15.11.808-817. [CrossRef] [Google Scholar]
  67. S. Sriram, A. Shashank, R. Vinayakumar, and K.P. Soman, “DCNN-IDS: Deep Convolutional Neural Network Based Intrusion Detection System,” Commun. Comput. Inf. Sci., vol. 1213, pp. 85–92, 2020, doi:10.1007/978-981-15-9700-8_7. [Google Scholar]
  68. J. Zhang, F. Li, and F. Ye, “An Ensemble-based Network Intrusion Detection Scheme with Bayesian Deep Learning,” in IEEE International Conference on Communications, 2020, vol. 2020-June, doi:10.1109/ICC40277.2020.9149402. [Google Scholar]
  69. M. Azizjon, A. Jumabek, and W. Kim, “1D CNN based network intrusion detection with normalization on imbalanced data,” in 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, 2020, pp. 218–224, doi:10.1109/ICAIIC48513.2020.9064976. [Google Scholar]
  70. S. Li, “Network Intrusion Detection Model Based on Improved Convolutional Neural Network,” Adv. Intell. Syst. Comput., vol. 1146 AISC, pp. 18–24, 2020, doi:10.1007/978-3-030-43306-2_3. [Google Scholar]
  71. Y. Li, “Research on Application of Convolutional Neural Network in Intrusion Detection,” in Proceedings - 2020 7th International Forum on Electrical Engineering and Automation, IFEEA 2020, 2020, pp. 720–723, doi:10.1109/IFEEA51475.2020.00153. [Google Scholar]
  72. X. Han et al., “STIDM: A spatial and temporal aware intrusion detection model,” in Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020, 2020, pp. 370–377, doi:10.1109/TrustCom50675.2020.00058. [CrossRef] [Google Scholar]
  73. L. Chen, X. Kuang, A. Xu, S. Suo, and Y. Yang, “A Novel Network Intrusion Detection System Based on CNN,” in Proceedings - 2020 8th International Conference on Advanced Cloud and Big Data, CBD 2020, 2020, pp. 243–247, doi:10.1109/CBD51900.2020.00051. [Google Scholar]
  74. A. Andalib and V.T. Vakili, “An autonomous intrusion detection system using an ensemble of advanced learners,” 2020, doi:10.1109/ICEE50131.2020.9260808. [Google Scholar]
  75. V. Manikandan, K. Gowsic, T. Prince, R. Umamaheswari, B.F. Ibrahim, and A. Sampathkumar, “DRCNN-IDS Approach for Intelligent Intrusion Detection System,” 2020, doi:10.1109/ICCIT-144147971.2020.9213779. [Google Scholar]
  76. Y. Zhou, X. Zhu, S. Hu, D. Lin, and Y. Gao, “Intrusion Detection Based on Convolutional Neural Network in Complex Network Environment,” Artif. Intell. China, vol. 572 LNEE, pp. 229–238, 2020, doi:10.1007/978-981-15-0187-6_26. [Google Scholar]
  77. M. Gamal, H. Abbas, and R. Sadek, “Hybrid Approach for Improving Intrusion Detection Based on Deep Learning and Machine Learning Techniques,” in Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), 2020, vol. 1153 AISC, pp. 225–236, doi:10.1007/978-3-030-44289-7_22. [Google Scholar]
  78. Z. Hu, L. Wang, L. Qi, Y. Li, and W. Yang, “A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network,” IEEE Access, vol. 8, pp. 195741–195751, 2020, doi:10.1109/ACCESS.2020.3034015. [CrossRef] [Google Scholar]
  79. Y. Chen, S. Chen, M. Xuan, Q. Lin, and W. Wei, “Evolutionary Convolutional Neural Network: An Application to Intrusion Detection,” in 2021 13th International Conference on Advanced Computational Intelligence, ICACI 2021, 2021, pp. 245–252, doi:10.1109/ICACI52617.2021.9435859. [Google Scholar]
  80. R. V. Mendonca et al., “Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network,” IEEE Access, vol. 9, pp. 61024–61034, 2021, doi:10.1109/ACCESS.2021.3074664. [CrossRef] [Google Scholar]
  81. L. Tian and Y. Lu, “An intrusion detection model based on SMOTE and convolutional neural network ensemble,” in Journal of Physics: Conference Series, 2021, vol. 1828, no. 1, doi:10.1088/1742-6596/1828/1/012024. [Google Scholar]
  82. Q. Zhou, M. Tan, and H. Xi, “ACGANs-CNN: A Novel Intrusion Detection Method,” in Journal of Physics: Conference Series, 2021, vol. 1757, no. 1, doi:10.1088/1742-6596/1757/1/012012. [Google Scholar]
  83. W. Yue, J. Yiming, and L. Julong, “A Fast Deep Learning Method for Network Intrusion Detection without Manual Feature Extraction,” in Journal of Physics: Conference Series, 2021, vol. 1738, no. 1, doi:10.1088/1742-6596/1738/1/012127. [Google Scholar]
  84. S. Kabir, S. Sakib, M.A. Hossain, S. Islam, and M.I. Hossain, “A Convolutional Neural Network based Model with Improved Activation Function and Optimizer for Effective Intrusion Detection and Classification,” in 2021 International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2021, 2021, pp. 373–378, doi:10.1109/ICACITE51222.2021.9404584. [Google Scholar]
  85. J. Man and G. Sun, “A Residual Learning-Based Network Intrusion Detection System,” Secur. Commun. Networks, vol. 2021, 2021, doi:10.1155/2021/5593435. [Google Scholar]
  86. Y. Wang, Y. Jiang, and J. Lan, “FCNN: An Efficient Intrusion Detection Method Based on Raw Network Traffic,” Secur. Commun. Networks, vol. 2021, 2021, doi:10.1155/2021/5533269. [Google Scholar]
  87. I. Al-Turaiki and N. Altwaijry, “A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection,” Big Data, vol. 9, no. 3, pp. 233–252, 2021, doi:10.1089/big.2020.0263. [CrossRef] [Google Scholar]
  88. M.A. Khan, “HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system,” Processes, vol. 9, no. 5, 2021, doi:10.3390/pr9050834. [Google Scholar]
  89. K. He, “Study on Intrusion detection model based on improved convolutional neural network,” in 2021 International Conference on Advances in Optics and Computational Sciences, 2021, vol. 1865, no. 4, doi:10.1088/1742-6596/1865/4/042097. [Google Scholar]
  90. H. Altunay and Z. Albayrak, “Network Intrusion Detection Approach Based on Convolutional Neural Network,” Eur. J. Sci. Technol., vol. 26, no. July, pp. 22–29, 2021, doi:10.31590/ejosat.954966. [Google Scholar]
  91. Y. Liu, J. Kang, Y. Li, and B. Ji, “A Network Intrusion Detection Method Based on CNN and CBAM,” 2021. [Google Scholar]
  92. S. Ho, S. Al Jufout, K. Dajani, and M. Mozumdar, “A Novel Intrusion Detection Model for Detecting Known and Innovative Cyberattacks Using Convolutional Neural Network,” IEEE Open J. Comput. Soc., vol. 2, no. October 2020, pp. 14–25, 2021, doi:10.1109/ojcs.2021.3050917. [CrossRef] [Google Scholar]
  93. A. Krishnan and S.T. Mithra, “A Modified 1D-CNN Based Network Intrusion Detection System,” Int. J. Res. Eng. Sci. Manag., vol. 4, no. 6, pp. 291–294, 2021. [Google Scholar]
  94. L. Yu et al., “PBCNN: Packet Bytes-based Convolutional Neural Network for Network Intrusion Detection,” Comput. Networks, vol. 194, no. March, p. 108117, 2021, doi:10.1016/j.comnet.2021.108117. [CrossRef] [Google Scholar]
  95. P. Rajesh Kanna and P. Santhi, “Unified Deep Learning approach for Efficient Intrusion Detection System using Integrated Spatial–Temporal Features[Formula presented],” Knowledge-Based Syst., vol. 226, p. 107132, 2021, doi:10.1016/j.knosys.2021.107132. [CrossRef] [Google Scholar]
  96. M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Comput. Secur., vol. 86, pp. 147–167, 2019, doi: https://doi.org/10.1016/j.cose.2019.06.005. [CrossRef] [Google Scholar]
  97. S. T. Brugger and J. Chow, “An Assessment of the DARPA IDS Evaluation Dataset Using Snort,” Dept. Comput. Sci., UCDAVIS, Tech. Rep., vol. 1, pp. 1–19, 2005. [Google Scholar]
  98. G. Gu, P. Fogla, D. Dagon, W. Lee, and B. Skorić, “Measuring Intrusion Detection Capability: An Information-Theoretic Approach,” in Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security, 2006, pp. 90–101, doi:10.1145/1128817.1128834. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.