Deep Learning Models in Network Intrusion Detection Systems

[1] Ketepalli, G., & Bulla, P. (2020). Review on Generative Deep Learning Models and Datasets for Intrusion Detection Systems. Revue d’Intelligence Artificielle, 34(2). KetepalliG. & BullaP. (2020). Review on Generative Deep Learning Models and Datasets for Intrusion Detection Systems. Revue d’Intelligence Artificielle, 34(2). Search in Google Scholar

[2] Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE transactions on emerging topics in computational intelligence, 2(1), 41-50. ShoneN.NgocT. N.PhaiV. D. & ShiQ. (2018). A deep learning approach to network intrusion detection. IEEE transactions on emerging topics in computational intelligence, 2(1), 41-50. Search in Google Scholar

[3] Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016, May). A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) (pp. 21-26). JavaidA.NiyazQ.SunW. & AlamM. (2016, May). A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) (pp. 21-26). Search in Google Scholar

[4] Ashiku, L., & Dagli, C. (2021). Network intrusion detection system using deep learning. Procedia Computer Science, 185, 239-247. AshikuL. & DagliC. (2021). Network intrusion detection system using deep learning. Procedia Computer Science, 185, 239-247. Search in Google Scholar

[5] Yan, J., Jin, D., Lee, C. W., & Liu, P. (2018, July). A comparative study of off-line deep learning based network intrusion detection. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 299-304). IEEE. YanJ.JinD.LeeC. W. & LiuP. (2018, July). A comparative study of off-line deep learning based network intrusion detection. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 299-304). IEEE. Search in Google Scholar

[6] Fernández, G. C., & Xu, S. (2019, November). A case study on using deep learning for network intrusion detection. In MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM) (pp. 1-6). IEEE. FernándezG. C. & XuS. (2019, November). A case study on using deep learning for network intrusion detection. In MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM) (pp. 1-6). IEEE. Search in Google Scholar

[7] Al Lail, M., Garcia, A., & Olivo, S. (2023). Machine learning for network intrusion detection—a comparative study. Future Internet, 15(7), 243. Al LailM.GarciaA. & OlivoS. (2023). Machine learning for network intrusion detection—a comparative study. Future Internet, 15(7), 243. Search in Google Scholar

[8] Dong, B., & Wang, X. (2016, June). Comparison deep learning method to traditional methods using for network intrusion detection. In 2016 8th IEEE international conference on communication software and networks (ICCSN) (pp. 581-585). IEEE. DongB. & WangX. (2016, June). Comparison deep learning method to traditional methods using for network intrusion detection. In 2016 8th IEEE international conference on communication software and networks (ICCSN) (pp. 581-585). IEEE. Search in Google Scholar

[9] Yang, H., Cheng, L., & Chuah, M. C. (2019, June). Deep-learning-based network intrusion detection for SCADA systems. In 2019 IEEE Conference on Communications and Network Security (CNS) (pp. 1-7). IEEE. YangH.ChengL. & ChuahM. C. (2019, June). Deep-learning-based network intrusion detection for SCADA systems. In 2019 IEEE Conference on Communications and Network Security (CNS) (pp. 1-7). IEEE. Search in Google Scholar

[10] Roy, S. S., Mallik, A., Gulati, R., Obaidat, M. S., & Krishna, P. V. (2017). A deep learning based artificial neural network approach for intrusion detection. In Mathematics and Computing: Third International Conference, ICMC 2017, Haldia, India, January 17-21, 2017, Proceedings 3 (pp. 44-53). Springer Singapore. RoyS. S.MallikA.GulatiR.ObaidatM. S. & KrishnaP. V. (2017). A deep learning based artificial neural network approach for intrusion detection. In Mathematics and Computing: Third International Conference, ICMC 2017, Haldia, India, January 17-21, 2017, Proceedings 3 (pp. 44-53). Springer Singapore. Search in Google Scholar

[11] Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R., & Ghogho, M. (2016, October). Deep learning approach for network intrusion detection in software defined networking. In 2016 international conference on wireless networks and mobile communications (WINCOM) (pp. 258-263). IEEE. TangT. A.MhamdiL.McLernonD.ZaidiS. A. R. & GhoghoM. (2016, October). Deep learning approach for network intrusion detection in software defined networking. In 2016 international conference on wireless networks and mobile communications (WINCOM) (pp. 258-263). IEEE. Search in Google Scholar

[12] Alom, M. Z., & Taha, T. M. (2017, June). Network intrusion detection for cyber security using unsupervised deep learning approaches. In 2017 IEEE national aerospace and electronics conference (NAECON) (pp. 63-69). IEEE. AlomM. Z. & TahaT. M. (2017, June). Network intrusion detection for cyber security using unsupervised deep learning approaches. In 2017 IEEE national aerospace and electronics conference (NAECON) (pp. 63-69). IEEE. Search in Google Scholar

[13] Peng, W., Kong, X., Peng, G., Li, X., & Wang, Z. (2019, July). Network intrusion detection based on deep learning. In 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) (pp. 431-435). IEEE. PengW.KongX.PengG.LiX. & WangZ. (2019, July). Network intrusion detection based on deep learning. In 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) (pp. 431-435). IEEE. Search in Google Scholar

[14] Gamage, S., & Samarabandu, J. (2020). Deep learning methods in network intrusion detection: A survey and an objective comparison. Journal of Network and Computer Applications, 169, 102767. GamageS. & SamarabanduJ. (2020). Deep learning methods in network intrusion detection: A survey and an objective comparison. Journal of Network and Computer Applications, 169, 102767. Search in Google Scholar

[15] Qazi, E. U. H., Faheem, M. H., & Zia, T. (2023). HDLNIDS: hybrid deep-learning-based network intrusion detection system. Applied Sciences, 13(8), 4921. QaziE. U. H.FaheemM. H. & ZiaT. (2023). HDLNIDS: hybrid deep-learning-based network intrusion detection system. Applied Sciences, 13(8), 4921. Search in Google Scholar

[16] Fu, Y., Du, Y., Cao, Z., Li, Q., & Xiang, W. (2022). A deep learning model for network intrusion detection with imbalanced data. Electronics, 11(6), 898. FuY.DuY.CaoZ.LiQ. & XiangW. (2022). A deep learning model for network intrusion detection with imbalanced data. Electronics, 11(6), 898. Search in Google Scholar

[17] Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. FerragM. A.MaglarasL.MoschoyiannisS. & JanickeH. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. Search in Google Scholar

[18] Vu, L., Nguyen, Q. U., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2022). Deep generative learning models for cloud intrusion detection systems. IEEE Transactions on Cybernetics, 53(1), 565-577. VuL.NguyenQ. U.NguyenD. N.HoangD. T. & DutkiewiczE. (2022). Deep generative learning models for cloud intrusion detection systems. IEEE Transactions on Cybernetics, 53(1), 565-577. Search in Google Scholar

[19] Halvorsen, J., Izurieta, C., Cai, H., & Gebremedhin, A. (2024). Applying generative machine learning to intrusion detection: A systematic mapping study and review. ACM Computing Surveys, 56(10), 1-33. HalvorsenJ.IzurietaC.CaiH. & GebremedhinA. (2024). Applying generative machine learning to intrusion detection: A systematic mapping study and review. ACM Computing Surveys, 56(10), 1-33. Search in Google Scholar

[20] Abdalgawad, N., Sajun, A., Kaddoura, Y., Zualkernan, I. A., & Aloul, F. (2021). Generative deep learning to detect cyberattacks for the IoT-23 dataset. IEEE Access, 10, 6430-6441. AbdalgawadN.SajunA.KaddouraY.ZualkernanI. A. & AloulF. (2021). Generative deep learning to detect cyberattacks for the IoT-23 dataset. IEEE Access, 10, 6430-6441. Search in Google Scholar

[21] Khan, F. A., Gumaei, A., Derhab, A., & Hussain, A. (2019). A novel two-stage deep learning model for efficient network intrusion detection. Ieee Access, 7, 30373-30385. KhanF. A.GumaeiA.DerhabA. & HussainA. (2019). A novel two-stage deep learning model for efficient network intrusion detection. Ieee Access, 7, 30373-30385. Search in Google Scholar

[22] Saharkhizan, M., Azmoodeh, A., HaddadPajouh, H., Dehghantanha, A., Parizi, R. M., & Srivastava, G. (2020). A hybrid deep generative local metric learning method for intrusion detection. Handbook of Big Data Privacy, 343-357. SaharkhizanM.AzmoodehA.HaddadPajouhH.DehghantanhaA.PariziR. M. & SrivastavaG. (2020). A hybrid deep generative local metric learning method for intrusion detection. Handbook of Big Data Privacy, 343-357. Search in Google Scholar

[23] Wanjau, S. K., Wambugu, G. M., Oirere, A. M., & Muketha, G. M. (2024). Discriminative spatial-temporal feature learning for modeling network intrusion detection systems. Journal of Computer Security, 32(1), 1-30. WanjauS. K.WambuguG. M.OirereA. M. & MukethaG. M. (2024). Discriminative spatial-temporal feature learning for modeling network intrusion detection systems. Journal of Computer Security, 32(1), 1-30. Search in Google Scholar

[24] Andresini, G., Appice, A., Di Mauro, N., Loglisci, C., & Malerba, D. (2020). Multi-channel deep feature learning for intrusion detection. IEEE Access, 8, 53346-53359. AndresiniG.AppiceA.Di MauroN.LoglisciC. & MalerbaD. (2020). Multi-channel deep feature learning for intrusion detection. IEEE Access, 8, 53346-53359. Search in Google Scholar

[25] Abdulhammed, R., Musafer, H., Alessa, A., Faezipour, M., & Abuzneid, A. (2019). Features dimensionality reduction approaches for machine learning based network intrusion detection. Electronics, 8(3), 322. AbdulhammedR.MusaferH.AlessaA.FaezipourM. & AbuzneidA. (2019). Features dimensionality reduction approaches for machine learning based network intrusion detection. Electronics, 8(3), 322. Search in Google Scholar

[26] Alhajjar, E., Maxwell, P., & Bastian, N. (2021). Adversarial machine learning in network intrusion detection systems. Expert Systems with Applications, 186, 115782. AlhajjarE.MaxwellP. & BastianN. (2021). Adversarial machine learning in network intrusion detection systems. Expert Systems with Applications, 186, 115782. Search in Google Scholar

[27] Iliyasu, A. S., & Deng, H. (2022). N-GAN: a novel anomaly-based network intrusion detection with generative adversarial networks. International Journal of Information Technology, 14(7), 3365-3375. IliyasuA. S. & DengH. (2022). N-GAN: a novel anomaly-based network intrusion detection with generative adversarial networks. International Journal of Information Technology, 14(7), 3365-3375. Search in Google Scholar

[28] Park, C., Lee, J., Kim, Y., Park, J. G., Kim, H., & Hong, D. (2022). An enhanced AI-based network intrusion detection system using generative adversarial networks. IEEE Internet of Things Journal, 10(3), 2330-2345. ParkC.LeeJ.KimY.ParkJ. G.KimH. & HongD. (2022). An enhanced AI-based network intrusion detection system using generative adversarial networks. IEEE Internet of Things Journal, 10(3), 2330-2345. Search in Google Scholar

[29] Caville, E., Lo, W. W., Layeghy, S., & Portmann, M. (2022). Anomal-E: A self-supervised network intrusion detection system based on graph neural networks. Knowledge-Based Systems, 258, 110030. CavilleE.LoW. W.LayeghyS. & PortmannM. (2022). Anomal-E: A self-supervised network intrusion detection system based on graph neural networks. Knowledge-Based Systems, 258, 110030. Search in Google Scholar

[30] Lo, W. W., Layeghy, S., Sarhan, M., Gallagher, M., & Portmann, M. (2022, April). E-graphsage: A graph neural network based intrusion detection system for iot. In NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium (pp. 1-9). IEEE. LoW. W.LayeghyS.SarhanM.GallagherM. & PortmannM. (2022, April). E-graphsage: A graph neural network based intrusion detection system for iot. In NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium (pp. 1-9). IEEE. Search in Google Scholar

[31] Khodaee Pouya,Esfahanipour Akbar & Mehtari Taheri Hassan. (2022). Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images. Engineering Applications of Artificial Intelligence PouyaKhodaeeAkbarEsfahanipour & HassanMehtari Taheri. (2022). Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images. Engineering Applications of Artificial Intelligence Search in Google Scholar

[32] Jian Wang,Chuangeng Chen,Bingsheng Liu,Juezhe Wang & Songtao Wang. (2024). Pipeline Landmark Classification of Miniature Pipeline Robot π-II Based on Residual Network ResNet18. Machines(8),563-563. WangJianChenChuangengLiuBingshengWangJuezhe & WangSongtao. (2024). Pipeline Landmark Classification of Miniature Pipeline Robot π-II Based on Residual Network ResNet18. Machines(8),563-563. Search in Google Scholar

[33] Zhang Yu,Zuo Xin,Zheng Xuxu,Gao Xiaoyong,Wang Bo & Hu Weiming. (2023). Improving metric-based few-shot learning with dynamically scaled softmax loss. Image and Vision Computing. YuZhangXinZuoXuxuZhengXiaoyongGaoBoWang & WeimingHu. (2023). Improving metric-based few-shot learning with dynamically scaled softmax loss. Image and Vision Computing. Search in Google Scholar

[34] Zhang Xiao,Yu Ali,Wang Xin & Zhang Xue. (2023). Sports Work Strategy of College Counselors Based on MySQL Database Big Data Analysis. International Journal of Information Technology and Web Engineering (IJITWE)(1),1-14. XiaoZhangAliYuXinWang & XueZhang. (2023). Sports Work Strategy of College Counselors Based on MySQL Database Big Data Analysis. International Journal of Information Technology and Web Engineering (IJITWE)(1),1-14. Search in Google Scholar

[35] Jared M. Peterson,Taghi M. Khoshgoftaar & Joffrey L. Leevy. (2022). Composition analysis of the Bot-IoT dataset. International Journal of Internet of Things and Cyber-Assurance(1),31-44. PetersonJared M.KhoshgoftaarTaghi M. & LeevyJoffrey L.. (2022). Composition analysis of the Bot-IoT dataset. International Journal of Internet of Things and Cyber-Assurance(1),31-44. Search in Google Scholar

[36] Tareq Imad,Elbagoury Bassant M.,ElRegaily Salsabil & ElHorbaty ElSayed M. (2022). Analysis of ToNIoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT. Applied Sciences(19), 9572-9572. ImadTareqElbagoury BassantM.SalsabilElRegaily & ElHorbaty ElSayedM. (2022). Analysis of ToNIoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT. Applied Sciences(19), 9572-9572. Search in Google Scholar