arXiv HTML resource

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behaviors evolve over time, realistic datasets are difficult to obtain, traffic records may be incomplete, attack classes are…

Licence: OPEN CC-BY-4.0
Authors: Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar, Qixu Gong, Wenbin Jia…
Published: 2026-07-01 · arXiv
Language: en
Length: 20383 words
Type: narrative text

Cites 94 works

Open original ↗

Extracted text · References Words 12,131–18,356 of 18,356

← Previous

References

J. P. Anderson (1980) Computer security threat monitoring and surveillance. Technical Report, James P. Anderson Company. Cited by: §I, §II.
D. E. Denning (1987) An intrusion-detection model. IEEE Trans. Software Eng. 13 (2), pp. 222–232. External Links: Link, Document Cited by: §I, §II.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas (2017) Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA, A. Singh and X. (. Zhu (Eds.), Proceedings of Machine Learning Research, Vol. 54, pp. 1273–1282. External Links: Link Cited by: §I, §IV, §IV.
H. Jmila and M. I. Khedher (2022) Adversarial machine learning for network intrusion detection: A comparative study. Comput. Networks 214, pp. 109073. External Links: Link, Document Cited by: §II-A.
M. Tavallaee, E. Bagheri, W. Lu, and 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, pp. 1–6. Cited by: §II-A, TABLE II.
N. Moustafa and 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), pp. 1–6. Cited by: §II-A, §II-E, TABLE II.
A. Alotaibi and M. A. Rassam (2023) Adversarial machine learning attacks against intrusion detection systems: a survey on strategies and defense. Future Internet 15 (2). External Links: Link, ISSN 1999-5903, Document Cited by: §II-A.
Z. Yang, X. Liu, T. Li, D. Wu, J. Wang, Y. Zhao, and H. Han (2022) A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Comput. Secur. 116, pp. 102675. External Links: Link, Document Cited by: §II-B.
S. Hajj, R. El Sibai, J. Bou Abdo, J. Demerjian, A. Makhoul, and C. Guyeux (2021) Anomaly-based intrusion detection systems: the requirements, methods, measurements, and datasets. Transactions on Emerging Telecommunications Technologies 32 (4), pp. e4240. Cited by: §II-B.
S. S. Kumar, M. Selvi, and A. Kannan (2023) A comprehensive survey on machine learning-based intrusion detection systems for secure communication in internet of things. Computational Intelligence and Neuroscience: CIN 2023. Cited by: §II-C.
P. Jayalaxmi, R. Saha, G. Kumar, M. Conti, and T. Kim (2022) Machine and deep learning solutions for intrusion detection and prevention in iots: A survey. IEEE Access 10, pp. 121173–121192. External Links: Link, Document Cited by: §II-C.
N. Moustafa, N. Koroniotis, M. Keshk, A. Y. Zomaya, and Z. Tari (2023) Explainable intrusion detection for cyber defences in the internet of things: opportunities and solutions. IEEE Communications Surveys & Tutorials 25 (3), pp. 1775–1807. External Links: Document Cited by: §II-D.
S. Neupane, J. Ables, W. Anderson, S. Mittal, S. Rahimi, I. Banicescu, and M. Seale (2022) Explainable intrusion detection systems (X-IDS): A survey of current methods, challenges, and opportunities. IEEE Access 10, pp. 112392–112415. External Links: Link, Document Cited by: §II-D.
D. Gunning and D. Aha (2019) DARPA’s explainable artificial intelligence (xai) program. AI magazine 40 (2), pp. 44–58. Cited by: §II-D.
A. Divekar, M. Parekh, V. Savla, R. Mishra, and M. Shirole (2018) Benchmarking datasets for anomaly-based network intrusion detection: kdd cup 99 alternatives. In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), pp. 1–8. Cited by: TABLE II.
I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization.. ICISSp 1, pp. 108–116. Cited by: §II-E, TABLE II, TABLE II.
I. Sharafaldin, A. H. Lashkari, S. Hakak, and A. A. Ghorbani (2019) Developing realistic distributed denial of service (ddos) attack dataset and taxonomy. In 2019 International Carnahan Conference on Security Technology (ICCST), pp. 1–8. Cited by: §II-E, TABLE II.
H. Kang, B. I. Kwak, Y. H. Lee, H. Lee, H. Lee, and H. K. Kim (2021) Car hacking: attack & defense challenge 2020 dataset. IEEE Dataport. External Links: Document, Link Cited by: TABLE II.
P. Kumar, J. Liu, A. S. M. Tayeen, S. Misra, H. Cao, J. Harikumar, and O. Perez (2023) FLNET2023: realistic network intrusion detection dataset for federated learning. In MILCOM 2023-2023 IEEE Military Communications Conference (MILCOM), pp. 345–350. Cited by: §II-E, TABLE II, §IV-E.
E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani (2023) CICIoT2023: a real-time dataset and benchmark for large-scale attacks in iot environment. Sensors 23 (13), pp. 5941. Cited by: TABLE II.
E. C. P. Neto, H. Taslimasa, S. Dadkhah, S. Iqbal, P. Xiong, T. Rahman, and A. A. Ghorbani (2024) CICIoV2024: advancing realistic ids approaches against dos and spoofing attack in iov can bus. Internet of Things 26, pp. 101209. Cited by: TABLE II.
S. Jeong, S. Lee, H. Lee, and H. K. Kim (2024) X-canids: signal-aware explainable intrusion detection system for controller area network-based in-vehicle network. IEEE Transactions on Vehicular Technology 73 (3), pp. 3230–3246. External Links: Document Cited by: TABLE II.
M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho (2019) A survey of network-based intrusion detection data sets. Computers & Security 86, pp. 147–167. Cited by: §II-E.
A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2 (1), pp. 1–22. Cited by: §II-E.
D. P. Kingma and M. Welling (2014) Auto-encoding variational bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Y. Bengio and Y. LeCun (Eds.), External Links: Link Cited by: §III-A.
S. Zavrak and M. Iskefiyeli (2020) Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access 8, pp. 108346–108358. External Links: Link, Document Cited by: §III-A1.
X. Xu, J. Li, Y. Yang, and F. Shen (2021) Toward effective intrusion detection using log-cosh conditional variational autoencoder. IEEE Internet Things J. 8 (8), pp. 6187–6196. External Links: Link, Document Cited by: §III-A1.
S. Khanam, I. Ahmedy, M. Y. I. B. Idris, and M. H. Jaward (2022) Towards an effective intrusion detection model using focal loss variational autoencoder for internet of things (iot). Sensors 22 (15), pp. 5822. External Links: Link, Document Cited by: §III-A1.
Q. P. Nguyen, K. W. Lim, D. M. Divakaran, K. H. Low, and M. C. Chan (2019) GEE: A gradient-based explainable variational autoencoder for network anomaly detection. In 7th IEEE Conference on Communications and Network Security, CNS 2019, Washington, DC, USA, June 10-12, 2019, pp. 91–99. External Links: Link, Document Cited by: §III-A1.
E. Pantelidis, G. Bendiab, S. Shiaeles, and N. Kolokotronis (2021) Insider threat detection using deep autoencoder and variational autoencoder neural networks. In IEEE International Conference on Cyber Security and Resilience, CSR 2021, Rhodes, Greece, July 26-28, 2021, pp. 129–134. External Links: Link, Document Cited by: §III-A1.
R. Zheng and J. Gu (2019) Anomaly detection for power system forecasting under data corruption based on variational auto-encoder. In 8th Renewable Power Generation Conference (RPG 2019), pp. 1–6. Cited by: §III-A1.
A. Takiddin, M. Ismail, U. Zafar, and E. Serpedin (2020) Variational auto-encoder-based detection of electricity stealth cyber-attacks in AMI networks. In 28th European Signal Processing Conference, EUSIPCO 2020, Amsterdam, Netherlands, January 18-21, 2021, pp. 1590–1594. External Links: Link, Document Cited by: §III-A1.
H. Xie, A. Li, R. Jiang, Y. Jia, L. Huang, and W. Han (2019) Intrusion detection results analysis based on variational auto-encoder. In Fourth IEEE International Conference on Data Science in Cyberspace, DSC 2019, Hangzhou, China, June 23-25, 2019, pp. 516–521. External Links: Link, Document Cited by: §III-A1.
S. Azmin and A. B. M. A. A. Islam (2020) Network intrusion detection system based on conditional variational laplace autoencoder. In 7th NSysS 2020: 7th International Conference on Networking, Systems and Security, Dhaka, Bangladesh, 22-24 December, 2020, pp. 82–88. External Links: Link, Document Cited by: §III-A1.
V. Q. Nguyen, V. H. Nguyen, T. H. Hoang, and N. Shone (2022) A novel deep clustering variational auto-encoder for anomaly-based network intrusion detection. In 14th International Conference on Knowledge and Systems Engineering, KSE 2022, Nha Trang, Vietnam, October 19-21, 2022, N. B. Hung, L. S. Vinh, N. L. Minh, H. M. Hoang, B. T. Lam, P. X. Hieu, N. T. Lap, V. D. Hieu, and V. H. Q. Nhat (Eds.), pp. 1–7. External Links: Link, Document Cited by: §III-A1.
A. Hannan, C. Gruhl, and B. Sick (2021) Anomaly based resilient network intrusion detection using inferential autoencoders. In IEEE International Conference on Cyber Security and Resilience, CSR 2021, Rhodes, Greece, July 26-28, 2021, pp. 1–7. External Links: Link, Document Cited by: §III-A1.
Y. Ren, K. Feng, F. Hu, L. Chen, and Y. Chen (2023) A lightweight unsupervised intrusion detection model based on variational auto-encoder. Sensors 23 (20), pp. 8407. External Links: Link, Document Cited by: §III-A2.
Y. Lin, Z. Liu, R. Hwang, V. Nguyen, P. Lin, and Y. Lai (2022) Machine learning with variational autoencoder for imbalanced datasets in intrusion detection. IEEE Access 10, pp. 15247–15260. External Links: Link, Document Cited by: §III-A2.
M. L. Martín, B. Carro, and A. Sánchez-Esguevillas (2019) Variational data generative model for intrusion detection. Knowl. Inf. Syst. 60 (1), pp. 569–590. External Links: Link, Document Cited by: §III-A2.
M. L. Martín, B. Carro, A. Sánchez-Esguevillas, and J. Lloret (2017) Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors 17 (9), pp. 1967. External Links: Link, Document Cited by: §III-A2.
P. V. Dinh, Q. U. Nguyen, D. T. Hoang, D. N. Nguyen, S. P. Bao, and E. Dutkiewicz (2024) Constrained twin variational auto-encoder for intrusion detection in iot systems. IEEE Internet Things J. 11 (8), pp. 14789–14803. External Links: Link, Document Cited by: §III-A2.
Y. Yang, K. Zheng, C. Wu, and Y. Yang (2019) Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors 19 (11), pp. 2528. External Links: Link, Document Cited by: §III-A2, §III-A3.
P. Chuang and P. Huang (2023) B-VAE: a new dataset balancing approach using batched variational autoencoders to enhance network intrusion detection. J. Supercomput. 79 (12), pp. 13262–13286. External Links: Link, Document Cited by: §III-A2.
Y. Yang, K. Zheng, B. Wu, Y. Yang, and X. Wang (2020) Network intrusion detection based on supervised adversarial variational auto-encoder with regularization. IEEE access 8, pp. 42169–42184. Cited by: §III-A2.
L. Gjorgiev and S. Gievska (2020) Time series anomaly detection with variational autoencoder using mahalanobis distance. In ICT Innovations 2020. Machine Learning and Applications - 12th International Conference, ICT Innovations 2020, Skopje, North Macedonia, September 24-26, 2020, Proceedings, V. Dimitrova and I. Dimitrovski (Eds.), Communications in Computer and Information Science, Vol. 1316, pp. 42–55. External Links: Link, Document Cited by: §III-A2.
D. Guha, R. Chatterjee, and B. Sikdar (2023) Anomaly detection using lstm-based variational autoencoder in unsupervised data in power grid. IEEE Syst. J. 17 (3), pp. 4313–4323. External Links: Link, Document Cited by: §III-A2.
U. Sabeel, S. Shah-Heydari, K. Elgazzar, and K. El-Khatib (2021) CVAE-AN: atypical attack flow detection using incremental adversarial learning. In IEEE Global Communications Conference, GLOBECOM 2021, Madrid, Spain, December 7-11, 2021, pp. 1–6. External Links: Link, Document Cited by: §III-A3.
M. Mohamed (2023) Comparative evaluation of vaes, vae-gans and aaes for anomaly detection in network intrusion data. EMITTER International Journal of Engineering Technology 11 (2), pp. 160–173. Cited by: §III-A3.
K. S. Ahmed and L. Yue (2023) Improving intrusion detection system using improved variational autoencoder. In 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), Vol. , pp. 215–219. External Links: Document Cited by: §III-A3.
P. Chuang and D. Wu (2019) Applying deep learning to balancing network intrusion detection datasets. In 11th IEEE International Conference on Advanced Infocomm Technology, ICAIT 2019, Jinan, China, October 18-20, 2019, pp. 213–217. External Links: Link, Document Cited by: §III-A3.
V. Thakur, D. Vaithiyanathan, P. Verma, and B. Kaur (2024) A comparative evaluation of intrusion detection systems: icvae vs. cvae. In 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), Vol. , pp. 1–6. External Links: Document Cited by: §III-A3.
C. Liu, R. Antypenko, I. Sushko, and O. Zakharchenko (2022) Intrusion detection system after data augmentation schemes based on the VAE and CVAE. IEEE Trans. Reliab. 71 (2), pp. 1000–1010. External Links: Link, Document Cited by: §III-A3.
R. Yaegashi, E. Takeshita, and Y. Nakayama (2022) Two-stage ddos mitigation with variational auto-encoder and cyclic queuing. In IEEE International Conference on Communications, ICC 2022, Seoul, Korea, May 16-20, 2022, pp. 5421–5426. External Links: Link, Document Cited by: §III-A3.
J. Yang, X. Chen, S. Chen, X. Jiang, and X. Tan (2021) Conditional variational auto-encoder and extreme value theory aided two-stage learning approach for intelligent fine-grained known/unknown intrusion detection. IEEE Trans. Inf. Forensics Secur. 16, pp. 3538–3553. External Links: Link, Document Cited by: §III-A3, §IV-A.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio (2014) Generative adversarial nets. Advances in neural information processing systems 27. Cited by: §III-B.
M. Arjovsky, S. Chintala, and L. Bottou (2017) Wasserstein GAN. CoRR abs/1701.07875. External Links: Link, 1701.07875 Cited by: §III-B.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville (2017) Improved training of wasserstein gans. Advances in neural information processing systems 30. Cited by: §III-B.
N. Park, M. Mohammadi, K. Gorde, S. Jajodia, H. Park, and Y. Kim (2018) Data synthesis based on generative adversarial networks. Proc. VLDB Endow. 11 (10), pp. 1071–1083. External Links: Link, Document Cited by: §III-B1.
L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni (2019) Modeling tabular data using conditional gan. In Advances in Neural Information Processing Systems, Cited by: §III-B1.
F. Li, H. Shen, J. Mai, T. Wang, Y. Dai, and X. Miao (2024) Pre-trained language model-enhanced conditional generative adversarial networks for intrusion detection. Peer Peer Netw. Appl. 17 (1), pp. 227–245. External Links: Link, Document Cited by: §III-B1.
C. Park, J. Lee, Y. Kim, J. Park, H. Kim, and D. Hong (2022) An enhanced ai-based network intrusion detection system using generative adversarial networks. IEEE Internet of Things Journal 10 (3), pp. 2330–2345. Cited by: §III-B2.
S. Huang and K. Lei (2020) IGAN-ids: an imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Networks 105, pp. 102177. Cited by: §III-B2.
R. Sauber-Cole and T. M. Khoshgoftaar (2022) The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey. J. Big Data 9 (1), pp. 98. External Links: Link, Document Cited by: §III-B2.
V. Sampath, I. Maurtua, J. J. A. Martín, and A. Gutierrez (2021) A survey on generative adversarial networks for imbalance problems in computer vision tasks. J. Big Data 8 (1), pp. 27. External Links: Link, Document Cited by: §III-B2.
J. Kim, D. Tae, and J. Seok (2020) A survey of missing data imputation using generative adversarial networks. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 454–456. External Links: Link, Document Cited by: §III-B2.
R. Shahbazian and S. Greco (2023) Generative adversarial networks assist missing data imputation: A comprehensive survey and evaluation. IEEE Access 11, pp. 88908–88928. External Links: Link, Document Cited by: §III-B2.
P. F. de Araujo-Filho, M. Naili, G. Kaddoum, E. T. Fapi, and Z. Zhu (2023) Unsupervised gan-based intrusion detection system using temporal convolutional networks and self-attention. IEEE Transactions on Network and Service Management. Cited by: §III-B2.
S. Aldhaheri and A. Alhuzali (2023) SGAN-IDS: self-attention-based generative adversarial network against intrusion detection systems. Sensors 23 (18), pp. 7796. External Links: Link, Document Cited by: §III-B2, §IV-B.
D. Shu, N. O. Leslie, C. A. Kamhoua, and C. S. Tucker (2020) Generative adversarial attacks against intrusion detection systems using active learning. In Proceedings of the 2nd ACM workshop on wireless security and machine learning, pp. 1–6. Cited by: §III-B2.
X. Zhao, K. W. Fok, and V. L. Thing (2024) Enhancing network intrusion detection performance using generative adversarial networks. arXiv preprint arXiv:2404.07464. Cited by: §III-B2.
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, F. R. Bach and D. M. Blei (Eds.), JMLR Workshop and Conference Proceedings, Vol. 37, pp. 2256–2265. External Links: Link Cited by: §III-C.
J. Ho, A. Jain, and P. Abbeel (2020) Denoising diffusion probabilistic models. Advances in neural information processing systems 33, pp. 6840–6851. Cited by: §III-C.
H. Cao, C. Tan, Z. Gao, Y. Xu, G. Chen, P. Heng, and S. Z. Li (2024) A survey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering. Cited by: §III-C.
Q. Yi, X. Chen, C. Zhang, Z. Zhou, L. Zhu, and X. Kong (2024) Diffusion models in text generation: a survey. PeerJ Comput. Sci. 10, pp. e1905. External Links: Link, Document Cited by: §III-C.
Y. Li, K. Zhou, W. X. Zhao, and J. Wen (2023) Diffusion models for non-autoregressive text generation: A survey. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China, pp. 6692–6701. External Links: Link, Document Cited by: §III-C.
P. Dhariwal and A. Nichol (2021) Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34, pp. 8780–8794. Cited by: §III-C, §III-E.
S. Villanueva, A. S. M. Tayeen, Q. Gong, S. Misra, A. Dogar, H. Cao, J. Liu, P. Kumar, and J. Harikumar (2025) NeTIF: network traffic to image features for robust intrusion detection. In MILCOM 2025-2025 IEEE Military Communications Conference (MILCOM), pp. 1–6. Cited by: §III-C.
L. Yang and A. Shami (2022) A transfer learning and optimized cnn based intrusion detection system for internet of vehicles. In ICC 2022-IEEE International Conference on Communications, pp. 2774–2779. Cited by: §III-C.
H. Navidan, P. F. Moshiri, M. Nabati, R. Shahbazian, S. A. Ghorashi, V. Shah-Mansouri, and D. Windridge (2021) Generative adversarial networks (gans) in networking: A comprehensive survey & evaluation. Comput. Networks 194, pp. 108149. External Links: Link, Document Cited by: §III-C1.
A. Dunmore, J. Jang-Jaccard, F. Sabrina, and J. Kwak (2023) A comprehensive survey of generative adversarial networks (gans) in cybersecurity intrusion detection. IEEE Access 11, pp. 76071–76094. External Links: Link, Document Cited by: §III-C1.
C. Zhang, S. Yu, Z. Tian, and J. J. Q. Yu (2024) Generative adversarial networks: A survey on attack and defense perspective. ACM Comput. Surv. 56 (4), pp. 91:1–91:35. External Links: Link, Document Cited by: §III-C1.
I. K. Dutta, B. Ghosh, A. H. Carlson, M. W. Totaro, and M. A. Bayoumi (2020) Generative adversarial networks in security: A survey. In 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, UEMCON 2020, New York City, NY, USA, October 28-31, 2020, pp. 399–405. External Links: Link, Document Cited by: §III-C1.
W. Nie et al. (2022) Diffusion models for adversarial purification. In International Conference on Machine Learning (ICML), Cited by: §III-C1.
X. Chen et al. (2023) AdvDiffuser: natural adversarial example synthesis with diffusion models. In Proceedings of the IEEE/CVF ICCV, pp. 4562–4572. Cited by: §III-C1.
P. Kumar, A. S. M. Tayeen, S. Misra, H. Cao, J. Liu, Q. Gong, and J. Harikumar (2026) NetDiffuser: deceiving dnn-based network attack detection systems with diffusion-generated adversarial traffic. arXiv preprint arXiv:2603.08901. Cited by: §III-C1.
A. Hooda, N. Mangaokar, R. Feng, K. Fawaz, S. Jha, and A. Prakash (2024) D4: detection of adversarial diffusion deepfakes using disjoint ensembles. In IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2024, Waikoloa, HI, USA, January 3-8, 2024, pp. 3800–3810. External Links: Link, Document Cited by: §III-C1.
Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman (2018) PixelDefend: leveraging generative models to understand and defend against adversarial examples. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, External Links: Link Cited by: §III-C2.
W. Nie, B. Guo, Y. Huang, C. Xiao, A. Vahdat, and A. Anandkumar (2022) Diffusion models for adversarial purification. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári, G. Niu, and S. Sabato (Eds.), Proceedings of Machine Learning Research, Vol. 162, pp. 16805–16827. External Links: Link Cited by: §III-C2.
J. Wang, Z. Lyu, D. Lin, B. Dai, and H. Fu (2022) Guided diffusion model for adversarial purification. CoRR abs/2205.14969. External Links: Link, Document, 2205.14969 Cited by: §III-C2.
Q. Wu, H. Ye, and Y. Gu (2022) Guided diffusion model for adversarial purification from random noise. CoRR abs/2206.10875. External Links: Link, Document, 2206.10875 Cited by: §III-C2.
Y. Liu, K. Liu, Y. Xiao, Z. Dong, X. Xu, P. Wei, and L. Lin (2024) Towards better adversarial purification via adversarial denoising diffusion training. CoRR abs/2404.14309. External Links: Link, Document, 2404.14309 Cited by: §III-C2.
E. Bao, C. Chang, H. H. Nguyen, and I. Echizen (2023) From deconstruction to reconstruction: A plug-in module for diffusion-based purification of adversarial examples. In Digital Forensics and Watermarking - 22nd International Workshop, IWDW 2023, Jinan, China, November 25-26, 2023, Revised Selected Papers, B. Ma, J. Li, and Q. Li (Eds.), Lecture Notes in Computer Science, Vol. 14511, pp. 48–62. External Links: Link, Document Cited by: §III-C2.
J. Sun, W. Nie, Z. Yu, Z. M. Mao, and C. Xiao (2022) PointDP: diffusion-driven purification against adversarial attacks on 3d point cloud recognition. CoRR abs/2208.09801. External Links: Link, Document, 2208.09801 Cited by: §III-C2.
M. Gulsen, B. Cengiz, Y. H. Sahin, and G. Unal (2024) PCLD: point cloud layerwise diffusion for adversarial purification. CoRR abs/2403.06698. External Links: Link, Document, 2403.06698 Cited by: §III-C2.
X. Ren, J. Wu, H. Xu, and X. Chen (2024) Diffusion model based secure semantic communications with adversarial purification. In 10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024, New York City, NY, USA, May 10-12, 2024, pp. 130–134. External Links: Link, Document Cited by: §III-C2.
G. Lin, Z. Tao, J. Zhang, T. Tanaka, and Q. Zhao (2024) Robust diffusion models for adversarial purification. CoRR abs/2403.16067. External Links: Link, Document, 2403.16067 Cited by: §III-C2.
Y. Bai and X. Zhang (2023) Diffusion-based adversarial purification for speaker verification. CoRR abs/2310.14270. External Links: Link, Document, 2310.14270 Cited by: §III-C2.
N. Carlini, F. Tramèr, K. (. Dvijotham, L. Rice, M. Sun, and J. Z. Kolter (2023) (Certified!!) adversarial robustness for free!. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, External Links: Link Cited by: §III-C2.
M. Lee and D. Kim (2023) Robust evaluation of diffusion-based adversarial purification. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pp. 134–144. External Links: Link, Document Cited by: §III-C2.
M. Kang, D. Song, and B. Li (2023) DiffAttack: evasion attacks against diffusion-based adversarial purification. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds.), External Links: Link Cited by: §III-C2.
I. J. Goodfellow, J. Shlens, and C. Szegedy (2015) Explaining and harnessing adversarial examples. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun (Eds.), External Links: Link Cited by: §III-C3.
L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, and A. Madry (2018) Adversarially robust generalization requires more data. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), pp. 5019–5031. External Links: Link Cited by: §III-C3.
D. Stutz, M. Hein, and B. Schiele (2019) Disentangling adversarial robustness and generalization. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 6976–6987. External Links: Link, Document Cited by: §III-C3.
Y. Dong, Z. Deng, T. Pang, J. Zhu, and H. Su (2020) Adversarial distributional training for robust deep learning. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin (Eds.), External Links: Link Cited by: §III-C3.
H. Wang and C. Yu (2019) A direct approach to robust deep learning using adversarial networks. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, External Links: Link Cited by: §III-C3.
H. Jiang, Z. Chen, Y. Shi, B. Dai, and T. Zhao (2021) Learning to defend by learning to attack. In The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event, A. Banerjee and K. Fukumizu (Eds.), Proceedings of Machine Learning Research, Vol. 130, pp. 577–585. External Links: Link Cited by: §III-C3.
Z. Wang, T. Pang, C. Du, M. Lin, W. Liu, and S. Yan (2023) Better diffusion models further improve adversarial training. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett (Eds.), Proceedings of Machine Learning Research, Vol. 202, pp. 36246–36263. External Links: Link Cited by: §III-C3.
J. Yu, H. Oh, and J. Yang (2023) Adversarial denoising diffusion model for unsupervised anomaly detection. CoRR abs/2312.04382. External Links: Link, Document, 2312.04382 Cited by: §III-C3.
J. Liu, Q. Gong, W. Jiang, P. Kumar, A. S. M. Tayeen, H. Cao, S. Misra, and J. Harikumar (2025) Is synthetic flow data from generative models ready for network intrusion detection systems?. In MILCOM 2025-2025 IEEE Military Communications Conference (MILCOM), pp. 1–8. Cited by: §III-C4, §III-C4.
A. Kotelnikov, D. Baranchuk, I. Rubachev, and A. Babenko (2023) TabDDPM: modelling tabular data with diffusion models. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett (Eds.), Proceedings of Machine Learning Research, Vol. 202, pp. 17564–17579. External Links: Link Cited by: §III-C4.
B. Tang, Y. Lu, Q. Li, Y. Bai, J. Yu, and X. Yu (2023) A diffusion model based on network intrusion detection method for industrial cyber-physical systems. Sensors 23 (3), pp. 1141. External Links: Link, Document Cited by: §III-C4.
Y. Wang, J. Ding, X. He, Q. Wei, S. Yuan, and J. Zhang (2023) Intrusion detection method based on denoising diffusion probabilistic models for uav networks. Mobile Networks and Applications, pp. 1–10. Cited by: §III-C4.
S. Lee, B. Kim, and H. Lee (2023) Data augmentation using generative models for track intrusion detection. Science Progress 106 (4), pp. 00368504231212769. Cited by: §III-C4.
O. G. Lira, A. Marroquin, and M. A. To (2024) Harnessing the advanced capabilities of LLM for adaptive intrusion detection systems. In Advanced Information Networking and Applications - Proceedings of the 38th International Conference on Advanced Information Networking and Applications (AINA-2024), Kitakyushu, Japan, 17-19 April, 2024, Volume 6, L. Barolli (Ed.), Lecture Notes on Data Engineering and Communications Technologies, Vol. 204, pp. 453–464. External Links: Link, Document Cited by: §III-D1.
L. D. Manocchio, S. Layeghy, W. W. Lo, G. K. Kulatilleke, M. Sarhan, and M. Portmann (2024) FlowTransformer: A transformer framework for flow-based network intrusion detection systems. Expert Syst. Appl. 241, pp. 122564. External Links: Link, Document Cited by: §III-D1.
G. Rjoub, J. Bentahar, O. A. Wahab, R. Mizouni, A. Song, R. Cohen, H. Otrok, and A. Mourad (2023) A survey on explainable artificial intelligence for cybersecurity. IEEE Transactions on Network and Service Management 20 (4), pp. 5115–5140. Cited by: §III-D2.
V. Jüttner, M. Grimmer, and E. Buchmann (2023) Chatids: explainable cybersecurity using generative ai. arXiv preprint arXiv:2306.14504. Cited by: §III-D2.
T. Ali and P. Kostakos (2023) HuntGPT: integrating machine learning-based anomaly detection and explainable ai with large language models (llms). arXiv preprint arXiv:2309.16021. Cited by: §III-D2.
D. Rao and S. Mane (2021) Zero-shot learning approach to adaptive cybersecurity using explainable ai. arXiv preprint arXiv:2106.14647. Cited by: §III-D2.
C. Zhong, Q. Ni, and P. Chen (2023) Predicting analysts’ needs for explainable artificial intelligence (XAI) in cybersecurity analysis. In 29th Americas Conference on Information Systems, AMCIS 2023, Panama City, Panama, August 10-12, 2023, P. A. Pavlou, V. Midha, A. Animesh, T. A. Carte, A. R. Graeml, and A. Mitchell (Eds.), External Links: Link Cited by: §III-D2.
J. Kim, T. Kim, and J. Choo (2024) Exploring prompting methods for mitigating class imbalance through synthetic data generation with large language models. External Links: 2404.12404 Cited by: §III-D3.
N. Seedat, N. Huynh, B. van Breugel, and M. van der Schaar (2023) Curated llm: synergy of llms and data curation for tabular augmentation in ultra low-data regimes. arXiv preprint arXiv:2312.12112. Cited by: §III-D3.
T. V. Tran and L. Xiong (2024) Differentially private tabular data synthesis using large language models. arXiv preprint arXiv:2406.01457. Cited by: §III-D3.
Y. Einy, T. Milo, and S. Novgorodov (2024) Cost-effective llm utilization for machine learning tasks over tabular data. In Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI, pp. 45–49. Cited by: §III-D3.
S. Xu, C. Lee, M. Sharma, R. B. Yousuf, N. Muralidhar, and N. Ramakrishnan (2024) Are llms naturally good at synthetic tabular data generation?. arXiv preprint arXiv:2406.14541. Cited by: §III-D3.
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling (2016) Improved variational inference with inverse autoregressive flow. Advances in neural information processing systems 29. Cited by: §III-E.
M. Arjovsky and L. Bottou (2017) Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862. Cited by: §III-E.
T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li (2017) Mode regularized generative adversarial networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, External Links: Link Cited by: §III-E.
A. Q. Nichol and P. Dhariwal (2021) Improved denoising diffusion probabilistic models. In International conference on machine learning, pp. 8162–8171. Cited by: §III-E.
S. Kullback and R. A. Leibler (1951) On information and sufficiency. The annals of mathematical statistics 22 (1), pp. 79–86. Cited by: §III-E.
J. Lin (1991) Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37 (1), pp. 145–151. Cited by: §III-E.
C. Villani et al. (2009) Optimal transport: old and new. Vol. 338, Springer. Cited by: §III-E.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30. Cited by: §III-E.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola (2012) A kernel two-sample test. The Journal of Machine Learning Research 13 (1), pp. 723–773. Cited by: §III-E.
T. Karras, S. Laine, and T. Aila (2019) A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4401–4410. Cited by: §III-E.
G. J. Székely and M. L. Rizzo (2013) Energy statistics: a class of statistics based on distances. Journal of statistical planning and inference 143 (8), pp. 1249–1272. Cited by: §III-E.
M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly (2018) Assessing generative models via precision and recall. Advances in neural information processing systems 31. Cited by: §III-E.
M. Schmidt, A. Bartezzaghi, and N. T. Vu (2024) Prompting-based synthetic data generation for few-shot question answering. arXiv preprint arXiv:2405.09335. Cited by: §III-E.
G. DeSalvo, J. Kagy, L. Karydas, A. Rostamizadeh, and S. Kumar (2024) No more hard prompts: softsrv prompting for synthetic data generation. arXiv preprint arXiv:2410.16534. Cited by: §III-E.
Y. Wang, C. Xu, Q. Sun, H. Hu, C. Tao, X. Geng, and D. Jiang (2022) PromDA: prompt-based data augmentation for low-resource NLU tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, S. Muresan, P. Nakov, and A. Villavicencio (Eds.), pp. 4242–4255. External Links: Link, Document Cited by: §III-E.
P. Munaweera, S. Prasad, T. Hewa, Y. Siriwardhana, and M. Ylianttila (2024) Federated learning-powered ddos attack detection for securing cyber physical systems in 5g and beyond networks. In Proceedings of the 14th International Conference on the Internet of Things, pp. 273–278. Cited by: §IV.
J. Liu, H. Cao, A. S. M. Tayeen, S. Misra, P. Kumar, and J. Harikumar (2023) Multi-model-based federated learning to overcome local class imbalance issues. In 2023 International Conference on Machine Learning and Applications (ICMLA), pp. 265–270. Cited by: §IV.
C. Zha, Z. Wang, Y. Fan, X. Zhang, B. Bai, Y. Zhang, S. Shi, and R. Zhang (2024) SKT-ids: unknown attack detection method based on sigmoid kernel transformation and encoder–decoder architecture. Computers & Security 146, pp. 104056. Cited by: §IV-A.
R. Tang, Z. Yang, Z. Li, W. Meng, H. Wang, Q. Li, Y. Sun, D. Pei, T. Wei, Y. Xu, et al. (2020) Zerowall: detecting zero-day web attacks through encoder-decoder recurrent neural networks. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 2479–2488. Cited by: §IV-A.
Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai (2018) Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089. Cited by: §IV-A.
A. S. M. Tayeen, S. Misra, H. Cao, and J. Harikumar (2023) CAFNet: compressed autoencoder-based federated network for anomaly detection. In MILCOM 2023-2023 IEEE Military Communications Conference (MILCOM), pp. 325–330. Cited by: §IV-A.
M. N, S. H. K. G, S. R, and J. I. R. NR (2024) Federated transfer learning for intrusion detection system in industrial iot 4.0. Multim. Tools Appl. 83 (19), pp. 57913–57941. External Links: Link, Document Cited by: §IV-A.
Z. Lin, Y. Shi, and Z. Xue (2022) IDSGAN: generative adversarial networks for attack generation against intrusion detection. In Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part III, J. Gama, T. Li, Y. Yu, E. Chen, Y. Zheng, and F. Teng (Eds.), Lecture Notes in Computer Science, Vol. 13282, pp. 79–91. External Links: Link, Document Cited by: §IV-B.
Y. Zhang, Y. Zhang, Z. Zhang, H. Bai, T. Zhong, and M. Song (2022) Evaluation of data poisoning attacks on federated learning-based network intrusion detection system. In 24th IEEE Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application, HPCC/DSS/SmartCity/DependSys 2022, Hainan, China, December 18-20, 2022, pp. 2235–2242. External Links: Link, Document Cited by: §IV-B.
N. C. Vy, N. H. Quyen, P. T. Duy, and V. Pham (2021) Federated learning-based intrusion detection in the context of iiot networks: poisoning attack and defense. In Network and System Security - 15th International Conference, NSS 2021, Tianjin, China, October 23, 2021, Proceedings, M. Yang, C. Chen, and Y. Liu (Eds.), Lecture Notes in Computer Science, Vol. 13041, pp. 131–147. External Links: Link, Document Cited by: §IV-B.
A. Tabassum, A. Erbad, W. Lebda, A. Mohamed, and M. Guizani (2022) FEDGAN-IDS: privacy-preserving IDS using GAN and federated learning. Comput. Commun. 192, pp. 299–310. External Links: Link, Document Cited by: §IV-B.
M. Li, J. Lin, Y. Ding, Z. Liu, J. Zhu, and S. Han (2020) Gan compression: efficient architectures for interactive conditional gans. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5284–5294. Cited by: §IV-B.
X. Hu, Z. Gan, J. Wang, Z. Yang, Z. Liu, Y. Lu, and L. Wang (2022) Scaling up vision-language pre-training for image captioning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 17980–17989. Cited by: §IV-B.
F. V. S. Jothiraj and A. Mashhadi (2024) Phoenix: A federated generative diffusion model. In Companion Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, Singapore, May 13-17, 2024, T. Chua, C. Ngo, R. K. Lee, R. Kumar, and H. W. Lauw (Eds.), pp. 1568–1577. External Links: Link, Document Cited by: §IV-C.
H. Yang, M. Ge, K. Xiang, X. Bai, and H. Li (2023) Fedvae: communication-efficient federated learning with non-iid private data. IEEE Systems Journal 17 (3), pp. 4798–4808. Cited by: §IV-E.
M. Morafah, M. Reisser, B. Lin, and C. Louizos (2024) Stable diffusion-based data augmentation for federated learning with non-iid data. arXiv preprint arXiv:2405.07925. Cited by: §IV-E.
J. Liu, Z. Zhao, X. Luo, P. Li, G. Min, and H. Li (2024) SlaugFL: efficient edge federated learning with selective gan-based data augmentation. IEEE Transactions on Mobile Computing. Cited by: §IV-E.
J. Vora, N. Bouacida, A. Krishnan, and P. Mohapatra (2024) FedDM: enhancing communication efficiency and handling data heterogeneity in federated diffusion models. arXiv preprint arXiv:2407.14730. Cited by: §IV-E.
S. Ahn, S. Kim, Y. Kwon, J. Park, J. Youn, and S. Cho (2022) Communication-efficient diffusion strategy for performance improvement of federated learning with non-iid data. arXiv preprint arXiv:2207.07493. Cited by: §IV-E.

Metadata record

One description, two standard projections

Built from what the sources declared and what the gates observed. Nothing absent has been filled in here. 1 value read out of the text by the enrichment rules and 94 links to or from other resources — a lab's files, the pages it links, the works it cites stand under their elements, marked inferred, and are kept apart in the exports.

Valid

Profile: aimpro-oer-profile/1 built 2026-10-09
Content language: en declared English

Export Dublin Core LOM Internal record

Show empty elements

DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection. the standard ↗

The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.

Identity 3/3

Identifier dcterms:identifier this engine resource_id source resource/arxiv_id: tag:aim-pro.eu,2026:oer/f40027e54d10
https://arxiv.org/abs/2607.01305
arxiv:2607.01305

This engine's id, the source URI, and any persistent identifier the source published.
Title dcterms:title source resource/title: Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

The title of this resource, not of the record or repository it came from.
Type dcterms:type source resource: dcmitype:Text
preprint

A DCMI Type term for the form held, plus the source's own genre label as declared.

Responsibility 1/3

Creator dcterms:creator

source 8 assertions

Jiefei Liu
Abu Saleh Md Tayeen
Pratyay Kumar
Qixu Gong
Wenbin Jiang
Huiping Cao
Satyajayant Misra
Jayashree Harikumar

Only agents the source named as creators. A repository owner is not made an author by default.

Contributor dcterms:contributor

source 8 assertions

none found — the source did not declare it

Everyone else named, with the role as declared and the scope they were declared at.

Publisher dcterms:publisher

none found — the source did not declare it

Only when the source names a publisher of the work. The platform it was collected from is recorded as provenance instead.

Content 3/5

Description dcterms:description

source resource/summary

Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behaviors evolve over time, realistic datasets are difficult to obtain, traffic records may be incomplete, attack classes are often imbalanced, and privacy constraints limit centralized data collection. Recent advances in generative artificial intelligence (AI) and Federated Learning (FL) provide new opportunities to address these limitations. Generative models can support anomaly detection, synthetic traffic generation, data augmentation, data imputation, adversarial traffic generation, and IDS alert explanation. FL enables distributed IDS training without directly sharing local network traffic, making it suitable for privacy-sensitive and geographically distributed environments. This survey provides a structured review of generative AI and FL techniques for IDS. We first summarize representative IDS research directions, including adversarial machine learning, anomaly-based detection, IoT-oriented IDS, explainable IDS, and benchmark datasets. We then categorize generative AI applications in IDS according to model families and task objectives, covering autoencoder-based models, Generative Adversarial Networks (GANs), diffusion models, and Large Language Models (LLMs). Finally, we review emerging studies that integrate generative AI with FL-based IDS and discuss open challenges, including synthetic data quality, realistic traffic generation, dual-use adversarial risks, non-IID client distributions, communication-efficient model sharing, federated IDS benchmarking, and domain-specific LLMs for network security.

The abstract, and the depositor's additional notes after it when the source has a field for them.

Subject dcterms:subject

source resource/categories/0

cs.CR (arxiv)
cs.AI (arxiv)
cs.LG (arxiv)

Declared keywords and taxonomy terms, each carrying its scheme.

Language dcterms:language

language gate read the declared field

The language of the content as the language gate read it.

Audience dcterms:audience

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when the source declares an intended audience. Never inferred.

Education level dcterms:educationLevel

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when the source declares an educational context.

Rights 3/4

Licence dcterms:license

licence gate arXiv:arXiv/arXiv:license

http://creativecommons.org/licenses/by/4.0/

The licence document, as a URI where the gate resolved one.

Rights dcterms:rights

licence gate arXiv:arXiv/arXiv:license

CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive

The verdict and the conditions, in words.

Rights holder dcterms:rightsHolder

none found — the source did not declare it

Only when named at source.

Access rights dcterms:accessRights

source resource

open

The access condition the source declared. Not the licence.

Form and dates 3/10

Format dcterms:format

conversion latexml-html

text/markdown

One value per form held — the original and the extracted text.

Extent dcterms:extent

conversion latexml-html

137025 bytes (extracted text)

Sizes in bytes, each saying which form it measures.

Issued dcterms:issued

source resource

2026-07-01

When the resource was published. Not when it was collected.

Modified dcterms:modified

source resource

none found — the source did not declare it

When the source's own record was last changed.

Created dcterms:created

source resource

none found — the source did not declare it

Accepted dcterms:dateAccepted

source resource

none found — the source did not declare it

When a publisher accepted the work, where the source records it.

Submitted dcterms:dateSubmitted

source resource

none found — the source did not declare it

When the work was submitted, where the source records it.

Available dcterms:available

source resource

none found — the source did not declare it

Valid dcterms:valid

source resource

none found — the source did not declare it

Bibliographic citation dcterms:bibliographicCitation

none found — the source did not declare it

Where the work was published, in the source's own words: a journal with its volume and pages, a conference, an imprint.

Relations and custody 1/9

Is part of dcterms:isPartOf

none found — the source did not declare it

The repository, book or record this resource was found inside.

Has part dcterms:hasPart

none found — the source did not declare it

What this resource is made of, when the source lists its parts.

Is version of dcterms:isVersionOf

none found — the source did not declare it

Has version dcterms:hasVersion

none found — the source did not declare it

References dcterms:references

inferred read its reference list, line 421 citation

doi:10.1109/tse.1987.232894
doi:10.1016/j.comnet.2022.109073
doi:10.3390/fi15020062
doi:10.1016/j.cose.2022.102675
doi:10.1109/access.2022.3220622
doi:10.1109/comst.2023.3280465
doi:10.1109/access.2022.3216617
doi:10.21227/qvr7-n418
doi:10.1109/tvt.2023.3327275
arXiv:1312.6114
doi:10.1109/access.2020.3001350
doi:10.1109/jiot.2020.3034621
and 82 more, every one of them in the export

Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.

Is referenced by dcterms:isReferencedBy

none found — the source did not declare it

What links to or cites this one, when a source declares it; inferred from the texts held otherwise, and set apart.

Requires dcterms:requires

none found — the source did not declare it

What this resource needs in order to be used, when a source declares it. The files a lab works on are inferred, and stand under it apart.

Is required by dcterms:isRequiredBy

none found — the source did not declare it

What needs this resource, when a source declares it. The lab a component belongs to is inferred, and stands under it apart.

Provenance dcterms:provenance

this engine source
conversion latexml-html

Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it.

Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.

IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other. the standard ↗

1 General 5/8

Identifier 1.1

this engine resource_id
source resource/arxiv_id

URI: tag:aim-pro.eu,2026:oer/f40027e54d10
URI: https://arxiv.org/abs/2607.01305
ARXIV: 2607.01305

This engine's identifier for the resource, the source URI, and any persistent identifier the source published.

Title 1.2

source resource/title

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

Language 1.3

language gate read the declared field

The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.

Description 1.4

source resource/summary

Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behaviors evolve over time, realistic datasets are difficult to obtain, traffic records may be incomplete, attack classes are often imbalanced, and privacy constraints limit centralized data collection. Recent advances in generative artificial intelligence (AI) and Federated Learning (FL) provide new opportunities to address these limitations. Generative models can support anomaly detection, synthetic traffic generation, data augmentation, data imputation, adversarial traffic generation, and IDS alert explanation. FL enables distributed IDS training without directly sharing local network traffic, making it suitable for privacy-sensitive and geographically distributed environments. This survey provides a structured review of generative AI and FL techniques for IDS. We first summarize representative IDS research directions, including adversarial machine learning, anomaly-based detection, IoT-oriented IDS, explainable IDS, and benchmark datasets. We then categorize generative AI applications in IDS according to model families and task objectives, covering autoencoder-based models, Generative Adversarial Networks (GANs), diffusion models, and Large Language Models (LLMs). Finally, we review emerging studies that integrate generative AI with FL-based IDS and discuss open challenges, including synthetic data quality, realistic traffic generation, dual-use adversarial risks, non-IID client distributions, communication-efficient model sharing, federated IDS benchmarking, and domain-specific LLMs for network security.

The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.

Keyword 1.5

source resource/categories/0

cs.CR
cs.AI
cs.LG

Coverage 1.6

not collected — this library does not fill it

The time, place or culture the resource applies to. No source declares it.

Structure 1.7

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

Aggregation level 1.8

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

2 Life cycle 2/3

Version 2.1

source resource

Status 2.2

none found — the source did not declare it

Draft, final, revised or unavailable, and only when the source says so. A first version is not thereby final.

Contribute 2.3

source 8 assertions

Jiefei Liu — author
Abu Saleh Md Tayeen — author
Pratyay Kumar — author
Qixu Gong — author
Wenbin Jiang — author
Huiping Cao — author
Satyajayant Misra — author
Jayashree Harikumar — author

Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.

3 Meta-metadata 4/4

Identifier 3.1 this engine resource_id: URI: tag:aim-pro.eu,2026:oer/f40027e54d10/record

The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute 3.2 this engine source: AIM-PRO WP3 OER harvester (arXiv) — creator

Who generated this record and when — a statement about the record, not about the resource.
Metadata schema 3.3: LOMv1.0
aimpro-oer-profile/1

LOMv1.0, and the profile this was built against.
Language 3.4 language gate read the declared field: en

4 Technical 3/7

Format 4.1

conversion latexml-html

text/markdown

One value per form held: the original as the source published it, and the Markdown this engine extracted.

Size 4.2

conversion latexml-html

137025

Bytes. The original's, because the resource is the file and not our conversion of it.

Location 4.3

this engine resource_id

https://arxiv.org/abs/2607.01305

Where the source serves it.

Requirement 4.4

not collected — this library does not fill it

Software or hardware needed to use it. No source declares it.

Installation remarks 4.5

not collected — this library does not fill it

No source declares it.

Other platform requirements 4.6

not collected — this library does not fill it

No source declares it.

Duration 4.7

not collected — this library does not fill it

Playing time, for audio and video. The corpus holds neither.

5 Educational 1/11

Interactivity type 5.1

not collected — this library does not fill it

Active, expositive or mixed. A judgement about how the resource is used; no source declares it.

Learning resource type 5.2

source resource
mapped learning_resource_type/preprint

narrative text

Only from a declaration that maps onto LOM's vocabulary. A file format is not a declaration.

Interactivity level 5.3

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Semantic density 5.4

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Intended end user role 5.5

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Context 5.6

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Typical age range 5.7

not collected — this library does not fill it

No source declares it.

Difficulty 5.8

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared and never estimated.

Typical learning time 5.9

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared. Never computed from a word count.

Description 5.10

mapped learning_resource_type/preprint

none found — the source did not declare it

An explicit statement about educational use, when the source makes one.

Language 5.11

not collected — this library does not fill it

The language of the intended learner. Not assumed to be the content's: a lesson in English may be written for learners of it.

6 Rights 3/3

Cost 6.1 licence gate arXiv:arXiv/arXiv:license: no — free to use

No: the licence gate refuses anything it cannot establish open reuse terms for, before the bytes are requested.
Copyright and other restrictions 6.2 licence gate arXiv:arXiv/arXiv:license: yes — attribution required (CC-BY-4.0)

Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
Description 6.3 licence gate arXiv:arXiv/arXiv:license: CC-BY-4.0 · http://creativecommons.org/licenses/by/4.0/. Conditions: adaptation permitted, attribution required.

The SPDX id, the licence URI and the conditions. LOM has no element for any of the three, so this is where they survive.

7 Relation 0/2

Kind 7.1: none found — the source did not declare it
Resource 7.2 inferred read its reference list, line 421 citation: references: doi:10.1109/tse.1987.232894
references: doi:10.1016/j.comnet.2022.109073
references: doi:10.3390/fi15020062
references: doi:10.1016/j.cose.2022.102675
references: doi:10.1109/access.2022.3220622
references: doi:10.1109/comst.2023.3280465
references: doi:10.1109/access.2022.3216617
references: doi:10.21227/qvr7-n418
references: doi:10.1109/tvt.2023.3327275
references: arXiv:1312.6114
references: doi:10.1109/access.2020.3001350
references: doi:10.1109/jiot.2020.3034621
and 82 more, every one of them in the export

The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.

8 Annotation 0/3

Entity 8.1

not collected — this library does not fill it

Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.

Date 8.2

not collected — this library does not fill it

Description 8.3

not collected — this library does not fill it

9 Classification 0/4

Purpose 9.1

not collected — this library does not fill it

Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.

Taxon path 9.2

not collected — this library does not fill it

Where the competency framework goes. Empty in the record for the reason above.

Description 9.3

not collected — this library does not fill it

Keyword 9.4

not collected — this library does not fill it

What could not be established3

Where the source's metadata could not be carried over as it was — missing, contradictory, with no matching term in the standard, restructured, or taken from the repository — and what was done instead. Without these notes, an empty element would look like something the harvester missed.

Status	Field	Why
Not available	`rights_holder`	no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available	`publisher`	the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available	`educational`	the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them