Cyber-defense Powered by Generative AI: A Comprehensive State of the Art Review
محتوى المقالة الرئيسي
الملخص
Cyberattacks are becoming more advanced, challenging the traditional defenses based on signature-based and rule-based approaches. The current literature on generative artificial intelligence (GenAI) in the context of cybersecurity is disjointed based on model family or area of application, and it does not combine technical performance indicators with practical implementation limitations, making a comprehensive picture impossible. To fill this gap, the study conducts a comprehensive review of peer-reviewed articles and conference papers (2021-2026) indexed in Scopus and ScienceDirect, and Springer databases, involving the use of GenAI as a defensive cybersecurity tool. These papers are divided into (I) GenAI model family: generative adversarial networks (GAN), large language models (LLM), variational autoencoders (VAE), diffusion models, and hybrid GenAI; (II) application domain: intrusion detection, malware detection, anomaly detection, threat intelligence, privacy-preservation, vulnerability detection, and phishing and spamming detection; and (III) defense strategy: reactive, proactive, and adversarial. GenAI typically increases the accuracy of detection and data efficiency and provides active defense. Nevertheless, the practical validation is usually limited to offline tests that apply imprecise metrics. The paper provides the performance–efficacy trade-off model, which relates technical standards and realistic implementation limitations. It also identifies a research roadmap in the future focused on creating autonomous, privacy-protective, and trustful GenAI-powered cyber defenses and suggests a living review platform to keep track of advances in the fast-changing area.
تفاصيل المقالة
القسم
هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.
كيفية الاقتباس
المراجع
V. T. Truong, L. B. Dang, and L. B. Le, "Attacks and defenses for generative diffusion models: A comprehensive survey," ACM Comput. Surv., vol. 57, no. 8, Art. no. 216, p. 216, 2025, doi: 10.1145/3721479.
W. Ibrar, Author, Author, Author, Author, and Author, "Generative AI: A double-edged sword in the cyber threat landscape," Artif. Intell. Rev., vol. 58, p. 285, 2025, doi: 10.1007/s10462-025-11285-9.
S. Ayyaz and S. M. Malik, "A comprehensive study of generative adversarial networks (GAN) and generative pre-trained transformers (GPT) in cybersecurity," in Proc. Int. Conf. Intell. Comput. Data Sci. (ICDS), 2024, pp. 1–8, doi: 10.1109/ICDS62089.2024.10756505.
J. L. L. Delgado and J. A. L. Ramos, "A comprehensive survey on generative AI solutions in IoT security," Electronics, vol. 13, no. 24, p. 4965, 2024, doi: 10.3390/electronics13244965.
F. Li, I. Udoidiok, and J. Zhang, "A Comprehensive Study on Lightweight Convolution Techniques for Malicious Traffic Synthesis in Diffusion Models," in IEEE Access, vol. 13, 2025, pp. 88306–88317.
W. Kasri, Author, Author, Author, Author, and Author, "From vulnerability to defense: The role of large language models in enhancing cybersecurity," Computation, vol. 13, no. 2, p. 30, 2025, doi: 10.3390/computation13020030.
M. Uddin, Author, Author, Author, Author, and Author, "Generative AI revolution in cybersecurity: A comprehensive review of threat intelligence and operations," Artif. Intell. Rev., vol. 58, p. 236, 2025, doi: 10.1007/s10462-025-11219-5.
R. Vadisetty and A. Polamarasetti, "Generative AI for cyber threat simulation and defense," in Proc. 12th Int. Conf. Control, Mechatronics Autom. (ICCMA), 2024, pp. 272–277, doi: 10.1109/ICCMA63715.2024.10843938.
P. Radanliev, O. Santos, and U. D. Ani, "Generative AI cybersecurity and resilience," Front. Artif. Intell., vol. 8, p. 1568360, 2025, doi: 10.3389/frai.2025.1568360.
M. Gupta, C. Akiri, K. Aryal, E. Parker, and L. Praharaj, "From ChatGPT to ThreatGPT: Impact of generative AI in cybersecurity and privacy," IEEE Access, vol. 11, pp. 80218–80245, 2023, doi: 10.1109/ACCESS.2023.3300381.
M. Alanezi and R. M. A. Al-Azzawi, "AI-Powered Cyber Threats: A Systematic Review," Mesopotamian J. Cybersecurity, vol. 4, no. 3, pp. 166–188, 2024, doi: 10.58496/MJCS/2024/021.
L. O. Ofusori, T. Bokaba, and S. Mhlongo, "Artificial intelligence in cybersecurity: A comprehensive review and future direction," Applied Artificial Intelligence, vol. 38, no. 1, p. 2439609, 2024, doi: 10.1080/08839514.2024.2439609.
M. Ring, S. Wunderlich, D. Scheuring, D. Lands, and A. Hotho, "A survey of network-based intrusion detection data sets," Computers & Security, vol. 86, pp. 147–167, 2019, doi: 10.1016/j.cose.2019.06.005.
I. J. Goodfellow, Author, Author, Author, Author, and Author, "Generative adversarial nets," in Proc. Adv. Neural Inf. Process. Syst., 2014, vol. 27, pp. 2672–2680, doi: 10.5555/2969033.2969125.
D. P. Kingma and M. Welling, "Auto-encoding variational Bayes," in Proc. Int. Conf. Learn. Represent (ICLR), 2014, doi: 10.48550/arXiv.1312.6114.
L. Xu, Author, Author, Author, Author, and Author, "G-VAE: Variational autoencoder-based adversarial attacks and defenses in industrial control systems," Computers and Electrical Engineering, vol. 124, p. 110290, 2025, doi: 10.1016/j.compeleceng.2025.110290.
A. Vaswani, Author, Author, Author, Author, and Author, "Attention is all you need," Adv. Neural Inf. Process. Syst., vol. 30, 2017, doi: 10.5555/3295222.3295349.
F. Chiarello, Author, Author, Author, Author, and Author, "Future applications of generative large language models: A data-driven case study on ChatGPT," Technovation, vol. 133, 2024, doi: 10.1016/j.technovation.2024.103002.
H. Pearce, Author, Author, Author, Author, and Author, "Examining zero-shot vulnerability repair with large language models," in Proc. 44th Int. Conf. Softw. Eng. (ICSE), 2022, pp. 1490–1501, doi: 10.1109/SP46215.2023.10179420.
J. Sohl-Dickstein, Author, Author, Author, Author, and Author, "Deep unsupervised learning using nonequilibrium thermodynamics," ed, 2015.
P. Dhariwal and A. Nichol, "Diffusion models beat GANs on image synthesis," Adv. Neural Inf. Process. Syst., vol. 34, pp. 8780–8794, 2021, doi: 10.5555/3540261.3540933.
T. Karras, M. Aittala, S. Laine, and T. Aila, "Elucidating the design space of diffusion-based generative models," Adv. Neural Inf. Process. Syst., vol. 35, pp. 26565–26577, 2022, doi: 10.5555/3600270.3602196.
M. Al-Ajlan and M. Ykhlef, "A review of generative adversarial networks for intrusion detection systems: Advances, challenges, and future directions," Comput., Mater. Contin., vol. 81, no. 2, pp. 2053–2095, 2024, doi: 10.32604/cmc.2024.055891.
S. R. Sindiramutty, Author, Author, Author, Author, and Author, "Generative AI in network security and intrusion detection," in Advanced Applications of Generative AI and Natural Language Processing Models, IGI Global, 2025, pp. 77–123.
Z. Deng, Author, Author, Author, Author, and Author, "Generative AI in intrusion detection systems for Internet of Things: A systematic literature review," IEEE Open J. Commun. Soc., vol. 6, pp. 4689–4715, 2025, doi: 10.1109/OJCOMS.2025.3573194.
M. Andreoni, Author, Author, Author, Author, and Author, "Enhancing autonomous system security and resilience with generative AI: A comprehensive survey," IEEE Access, vol. 12, pp. 109470–109495, 2024, doi: 10.1109/ACCESS.2024.3439363.
O. Emehin, Author, Author, Author, Author, and Author, "Generative AI in forensic data analysis: Opportunities and ethical implications for cloud-based investigations," Int. J. Res. Publ. Rev., vol. 5, no. 10, pp. 2941–2957, 2024, doi: 10.55248/gengpi.5.1024.2904.
M. A. Ferrag, Author, Author, Author, Author, and Author, "Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities," Internet Things Cyber-Phys. Syst., vol. 5, pp. 1–46, 2025, doi: 10.1016/j.iotcps.2025.01.001.
S. Sai, Author, Author, Author, Author, and Author, "Generative AI for cyber security: Analyzing the potential of ChatGPT, DALL-E, and other models for enhancing the security space," IEEE Access, vol. 12, pp. 53497–53522, 2024, doi: 10.1109/ACCESS.2024.3385107.
H. Alqahtani and G. Kumar, "A comprehensive review of generative AI techniques and their impact on cybersecurity," Soft Comput., vol. 29, pp. 4945–4982, 2025, doi: 10.1007/s00500-025-10702-z.
S. L. Mirtaheri, N. Movahed, R. Shahbazian, V. Pascucci, and A. Pugliese, "Cybersecurity in the age of generative AI: A systematic taxonomy of AI-powered vulnerability assessment and risk management," Future Generation Computer Systems, vol. 175, p. 108107, 2026/02/01/ 2026, doi: https://doi.org/10.1016/j.future.2025.108107.
E. Hilario, Author, Author, Author, Author, and Author, "Generative AI for pentesting: The good, the bad, the ugly," Int. J. Inf. Secur., vol. 23, pp. 2075–2097, 2024, doi: 10.1007/s10207-024-00835-x.
K. Curran, Author, Author, Author, Author, and Author, "The role of generative AI in cyber security," Metaverse, vol. 5, no. 2, p. 2796, 2024, doi: 10.54517/m.v5i2.2796.
G. Andresini, Author, Author, Author, Author, and Author, "GAN augmentation to deal with imbalance in imaging-based intrusion detection," Future Gener. Comput. Syst., vol. 123, pp. 108–127, 2021, doi: 10.1016/j.future.2021.04.017.
X. Zhao, K. W. Fok, and V. L. L. Thing, "Enhancing network intrusion detection performance using generative adversarial networks," Computers & Security, vol. 145, p. 104005, 2024, doi: 10.1016/j.cose.2024.104005.
H. Zouhri and A. Idri, "A novel CTGAN-ENN hybrid approach to enhance the performance and interpretability of machine learning black-box models in intrusion detection and IoT," Future Gener. Comput. Syst., vol. 173, p. 107882, 2025, doi: 10.1016/j.future.2025.107882.
M. Arafah, I. Phillips, A. Adnane, M. Alauthman, and N. Aslam, "Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks," Applied Soft Computing, vol. 168, p. 112455, 2024, doi: 10.1016/j.asoc.2024.112455.
M. S. Alshehri, Author, Author, Author, Author, and Author, "A hybrid Wasserstein GAN and autoencoder model for robust intrusion detection in IoT," Computer Modeling in Engineering & Sciences, 2025, doi: 10.32604/cmes.2025.064874.
S. Li, Author, Author, Author, Author, and Author, "HDA-IDS: A Hybrid DoS Attacks Intrusion Detection System for IoT by using semi-supervised CL-GAN," Expert Systems With Applications, vol. 238, p. 122198, 2024, doi: 10.1016/j.eswa.2023.122198.
J. Qin, Author, Author, Author, Author, and Author, "GPIDS: GAN assisted contextual pattern-aware intrusion detection system for IVN," IEEE Trans. Veh. Technol., vol. 73, no. 9, 2024, doi: 10.1109/TVT.2024.3383449.
J. D. Yoo, H. Kim, and H. K. Kim, "GUIDE: GAN-based UAV IDS Enhancement," Comput. Secur., vol. 147, p. 104073, 2024, doi: 10.1016/j.cose.2024.104073.
A. Tabassum, Author, Author, Author, Author, and Author, "FEDGAN-IDS: Privacy-preserving IDS using GAN and Federated Learning," Comput. Commun., vol. 192, pp. 299–310, 2022, doi: 10.1016/j.comcom.2022.06.015.
F. Cirillo and C. Esposito, "Intrusion detection using quantum generative adversarial networks: a federated approach with noisy simulators," in IET Space and Communications Conference 2025, 2025, pp. 31–35, doi: 10.1049/icp.2025.2226.
M. Sekhar, V. Alukapelly, T. Neelima, R. Kotoju, and V. Veerabhadram, "Generative Adversarial Networks for Cyber Threat Simulation and Defence Strategies," Journal of Theoretical and Applied Information Technology, vol. 103, no. 4, pp. 1388–1400, 2025.
V. S. Mfogo, A. Zemkoho, L. Njilla, M. J. Nkenlifack, and C. A. Kamhoua, "GAN-AIIPot: GAN-Based Cyber Deception for Probing Attacks on IoT Devices," in IEEE Transactions on Network and Service Management, vol. 23, 2026, pp. 417–431.
F. S. Melícias, T. F. R. Ribeiro, C. Rabadão, L. Santos, and R. L. D. C. Costa, "GPT and Interpolation-Based Data Augmentation for Multiclass Intrusion Detection in IIoT," in IEEE Access, vol. 12, 2024, pp. 17945–17965.
P. Fernández-Saura, J. Bernal-Bernabé, and A. Skarmeta-Gómez, "Enhancing federated intrusion detection through LLM-Driven alert enrichment and collaborative threat information sharing," Future Gener. Comput. Syst., vol. 178, 2026, doi: 10.1016/j.future.2025.108319.
R. H. Hwang, Y. H. Hsiao, Y. D. Lin, and Y. C. Lai, "LLM+m: Dual-model chatGPT-based product training and testing with adversarial attack and defense," Future Generation Computer Systems, vol. 179, p. 108328, 2026, doi: 10.1016/j.future.2025.108328.
L. Yang, A. Ye, Y. Liu, W. Lu, and C. Huang, "LLM-APTDS: A high-precision advanced persistent threat detection system for imbalanced data based on large language models with strong interpretability," Future Generation Computer Systems, vol. 178, p. 108315, 2026, doi: 10.1016/j.future.2025.108315.
Z. Li, C. Huang, and W. Qiu, "An intrusion detection method combining variational auto-encoder and generative adversarial networks," Computer Networks, vol. 253, p. 110724, 2024, doi: 10.1016/j.comnet.2024.110724.
Y. Wan, Y. Qu, L. Gao, and Y. Xiang, "Differentially Privacy-Preserving Federated Learning Using Wasserstein Generative Adversarial Network," in 2021 IEEE Symposium on Computers and Communications (ISCC), 2021, pp. 1–6, doi: 10.1109/ISCC53001.2021.9631541.
A. Kotal and A. Joshi, "Differentially Private Synthetic Data Generation Using Context-Aware GANs," in 2024 IEEE International Conference on Big Data (BigData), 2024, pp. 6289–6297, doi: 10.1109/BigData62323.2024.10826047.
S. Han, Author, Author, Author, Author, and Author, "Fed-GAN: Federated generative adversarial network with privacy-preserving for cross-device scenarios," IEEE Trans. Dependable Secure Comput., 2025, doi: 10.1109/TDSC.2025.3567564.
A. Torfi, E. A. Fox, and C. K. Reddy, "Differentially private synthetic medical data generation using convolutional GANs," Inf. Sci., vol. 586, pp. 485–500, 2021, doi: 10.1016/j.ins.2021.12.018.
H. Montenegro and J. S. Cardoso, "Anonymizing medical case-based explanations through disentanglement," Medical Image Analysis, vol. 95, p. 103209, 2024, doi: 10.1016/j.media.2024.103209.
G. Aceto, Author, Author, Author, Author, and Author, "Synthetic and privacy-preserving traffic trace generation using generative AI models for training network intrusion detection systems," J. Netw. Comput. Appl., vol. 229, p. 103926, 2024, doi: 10.1016/j.jnca.2024.103926.
P. C. Hsu, Z. Yu, N. Ghafoori, S. Mise, and H. Miyaji, "Anonymous-Diffusion: Blockchain-Based Privacy-Preserving Stable Diffusion," in 2025 1st International Conference on Consumer Technology (ICCT-Pacific), 2025, pp. 1–4, doi: 10.1109/ICCT-Pacific63901.2025.11012859.
M. A. Ferrag, Author, Author, Author, Author, and Author, "Revolutionizing Cyber Threat Detection With Large Language Models: A Privacy-Preserving BERT-Based Lightweight Model for IoT/IIoT Devices," in IEEE Access, vol. 12, 2024, pp. 23733–23750.
D. Rodriguez, I. Yang, J. M. D. Alamo, and N. Sadeh, "Large language models: A new approach for privacy policy analysis at scale," Computing, vol. 106, pp. 3879–3903, 2024, doi: 10.1007/s00607-024-01331-9.
H. Montenegro, W. Silva, and J. S. Cardoso, "Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis," in IEEE Access, vol. 9, 2021, pp. 148037–148047.
V. R. Saddi, S. K. Gopal, A. S. Mohammed, S. Dhanasekaran, and M. S. Naruka, "Examine the role of generative AI in enhancing threat intelligence and cyber security measures," in 2024 2nd International Conference on Disruptive Technologies (ICDT), 2024, pp. 537–541, doi: 10.1109/ICDT61202.2024.00089.
L. Zhi and W. Wang, "Research on modeling of the imbalanced fraudulent transaction detection problem based on embedding-aware conditional GAN," Big Data Research, vol. 41, p. 100557, 2025, doi: 10.1016/j.bdr.2025.100557.
P. Ghadekar, R. Paimode, S. Pandav, P. Patange, A. Pardeshi, and P. Patil, "GAN AI for Predictive Threat Detection with Explainable Risk Insights," in 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI), 2025, pp. 1446–1452, doi: 10.1109/ICCSAI64074.2025.11063972.
A. p. Durai pandian, "Variational Autoencoders using Convolutional neural network for highly advanced cyber threats," in 2024 IEEE Integrated STEM Education Conference (ISEC), 2024, pp. 1–6, doi: 10.1109/ISEC61299.2024.10664944.
S. Jalalipour and B. Rekabdar, "Noisy-Defense Variational Auto-Encoder (ND-VAE): An Adversarial Defense Framework to Eliminate Adversarial Attacks," in 2023 Fifth International Conference on Transdisciplinary AI (TransAI), 2023, pp. 50–57, doi: 10.1109/TransAI60598.2023.00018.
F. Li, H. Wu, and J. Zhang, "Lightweight Diffusion Model for Synthesizing Malicious Network Traffic," in NAECON 2024 - IEEE National Aerospace and Electronics Conference, 2024, pp. 409–413, doi: 10.1109/NAECON61878.2024.10670640.
M. M. Helal, M. Abu-Elkheir, and M. Mashaly, "A Generative AI Framework for Cloud Security: Automated Attack Simulation and Threat Detection," in 2025 International Conference on Innovation in Artificial Intelligence and Internet of Things (AIIT), Jeddah, 2025, pp. 1–8, doi: 10.1109/AIIT63112.2025.11082823.
M. M. Yamin, E. Hashmi, M. Ullah, and B. Katt, "Applications of LLMs for generating cybersecurity exercise scenarios," IEEE Access, vol. 12, pp. 143806–143819, 2024, doi: 10.1109/ACCESS.2024.3468914.
R. Honnalli and J. Farooq, "LLM-Powered Agentic AI Approach to Securing EV Charging Systems Against Cyber Threats," in 2025 IEEE 26th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2025, pp. 266–274, doi: 10.1109/WoWMoM65615.2025.00053.
A. Patel, R. C. Sachan, H. Ragothaman, A. Sheth, P. Pandey, and S. K. Udayakumar, "Leveraging Generative AI for Proactive Cybersecurity Threat Detection in Cloud Environments," in 2025 8th International Conference on Information and Computer Technologies (ICICT), 2025, pp. 80–85, doi: 10.1109/ICICT64582.2025.00019.
J. Lee, D. Jung, J. Moon, and S. Rho, "Advanced R-GAN: Generating anomaly data for improved detection in imbalanced datasets using regularized generative adversarial networks," Alexandria Engineering Journal, vol. 111, pp. 491–510, 2025, doi: 10.1016/j.aej.2024.10.084.
W. Yin, C. Wang, and Y. Qin, "T-GAN: Transformer-based Generative Adversarial Network for Network Traffic Anomaly Detection," in Proc. 2025 2nd Int. Conf. Comput. Netw. Cloud Comput. (CNCC), 2025, pp. 64–69, doi: 10.1145/3744451.3744461.
R. Changala, S. Kayalvili, M. Farooq, L. M. Rao, V. S. Rao, and S. Muthuperumal, "Using Generative Adversarial Networks for Anomaly Detection in Network Traffic: Advancements in AI Cybersecurity," in 2024 International Conference on Data Science and Network Security (ICDSNS), 2024, pp. 1–6, doi: 10.1109/ICDSNS62112.2024.10690857.
C. K. Akiri, K. Jayabalan, J. Lopes, S. A. Kareem, and A. Tabbassum, "Generative AI for Real-Time Cloud Security: Advanced Anomaly Detection Using GPT Models," in 2025 IEEE Conference on Computer Applications (ICCA), Yangon, Myanmar, 2025, pp. 1–6, doi: 10.1109/ICCA65395.2025.11011269.
L. Feng, J. Yu, S. Li, and S. Jiu, "VAE-SMOTE Augmented Diffusion for Anomaly Detection," in Proc. 2025 2nd Int. Conf. Generative Artif. Intell. Inf. Secur, 2025, pp. 163–170, doi: 10.1145/3728725.3728751.
Y. Zhao, F. Ullah, C. Chen, M. Amoon, and S. Kumari, "Efficient malware detection using hybrid approach of transfer learning and generative adversarial examples with image representation," Expert Systems, vol. 42, no. 5, p. 13693, 2025, doi: 10.1111/exsy.13693.
F. B. Khan, Author, Author, Author, Author, and Author, "Design and performance analysis of an anti-malware system based on generative adversarial network framework," IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3358454.
H. H. Reshi and K. Singh, "Enhancing Malware Detection using Deep Learning Approach," in 2024 International Conference on Automation and Computation (AUTOCOM), Dehradun, India, 2024, pp. 497–501, doi: 10.1109/AUTOCOM60220.2024.10486116.
Y. Ban, J. H. Yi, and H. Cho, "Augmenting Android malware using conditional variational autoencoder for the malware family classification," Computer Systems Science & Engineering, vol. 46, no. 2, 2023, doi: 10.32604/csse.2023.036555.
D. Devadiga, Author, Author, Author, Author, and Author, "GLEAM: GAN and LLM for Evasive Adversarial Malware," in 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, 2023, pp. 53–58, doi: 10.1109/ICTC58733.2023.10393706.
J. Haurogné, N. Basheer, and S. Islam, "Vulnerability detection using BERT based LLM model with transparency obligation practice towards trustworthy AI," Machine Learning with Applications, vol. 18, p. 100598, 2024, doi: 10.1016/j.mlwa.2024.100598.
H. Ding, Y. Liu, X. Piao, H. Song, and Z. Ji, "SmartGuard: An LLM-enhanced framework for smart contract vulnerability detection," Expert Systems With Applications, vol. 269, p. 126479, 2025, doi: 10.1016/j.eswa.2025.126479.
G. Lu, X. Ju, X. Chen, W. Pei, and Z. Cai, "GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning," The Journal of Systems & Software, vol. 212, p. 112031, 2024, doi: 10.1016/j.jss.2024.112031.
Y. Guo, C. Patsakis, Q. Hu, Q. Tang, and F. Casino, "Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection," in Proc. 29th Eur. Symp. Res. Comput. Secur. (ESORICS), 2024, pp. 271–289, doi: 10.1007/978-3-031-70879-4_14.
S. L. Mirtaheri and A. Pugliese, "Leveraging Generative AI to Enhance Automated Vulnerability Scoring," in 2024 IEEE Conference on Dependable, Autonomic and Secure Computing (DASC), 2024, doi: 10.1109/DASC64200.2024.00014.
V. Akuthota, R. Kasula, S. T. Sumona, M. Mohiuddin, M. T. Reza, and M. M. Rahman, "Vulnerability Detection and Monitoring Using LLM," in 2023 IEEE 9th International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 2023, pp. 309–314, doi: 10.1109/WIECON-ECE60392.2023.10456393.
F. Z. Qachfar, R. M. Verma, and A. Mukherjee, "Leveraging Synthetic Data and PU Learning For Phishing Email Detection," in Proc. Twelveth ACM Conf. Data Appl. Secur. Privacy, 2022, doi: 10.1145/3508398.3511524.
L. Jovanovic, Author, Author, Author, Author, and Author, "Generative Adversarial Networks for Synthetic Training Data Replacement in Phishing Email Detection Using Natural Language Processing," Smart Data Intelligence, 2024, doi: 10.1007/978-981-97-3191-6_46.
M. Rathakrishnan, S. Gayan, S. Edirisinghe, and H. Inaltekin, "A Multi-Model Framework for Synthesizing High-Fidelity Network Intrusion Data Using Generative AI," in 2025 5th International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2025, pp. 1–6, doi: 10.1109/ICARC64760.2025.10963129.
A. Rashidi, M. Salehi, and S. Najari, "CGANS: a code-based GAN for spam detection in social media," Social Network Analysis and Mining, vol. 14, p. 218, 2024, doi: 10.1007/s13278-024-01379-7.
M. K. Prabakaran, P. M. Sundaram, and A. D. Chandrasekar, "An enhanced deep learning-based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders," IET Information Security, vol. 17, no. 3, pp. 423–440, 2023, doi: 10.1049/ise2.12106.
C. Thanomwong, K. Puangnak, N. Rachsiriwatcharabul, M. Tiawongsuwan, and K. Puangnak, "Applying Generative AI for Fraud and Cybercrime Prevention in Thailand," in 2025 11th International Conference on Engineering, Applied Sciences, and Technology (ICEAST), Thailand, 2025, pp. 65–68, doi: 10.1109/ICEAST64767.2025.11088187.
A. B. Beydemir, U. Sezgin, U. Doğan, B. E. Aşıklar, F. A. Yerlikaya, and Ş. Bahtiyar, "A Dynamically Selected GPT Model for Phishing Detection," in 2024 14th International Conference on Advanced Computer Information Technologies (ACIT), Ceske Budejovice, Czech Republic, 2024, pp. 481–484, doi: 10.1109/ACIT62333.2024.10712553.
R. Nair, F. Abbasi, and S. Pervez, "PhishEmailLLM: A meta model approach to detect phishing emails by leveraging LLMs and machine learning models," Australasian Computer Science Week (ACSW), 2025, doi: 10.1145/3727166.3727169.
A. Alqarni, Author, Author, Author, Author, and Author, "How Generative AI Transforms Spam Detection," in Tech Fusion in Business and Society, 2025, p. 233.
K. A. Vedros, C. Kolias, D. Barbara, and R. C. Ivans, "From Code to EM Signals: A Generative Approach to Side Channel Analysis-based Anomaly Detection," in Proc. 19th Int. Conf. Availability, Rel. Secur. (ARES), 2024, doi: 10.1145/3664476.3664520.
R. Talibzade, F. Bergadano, and I. Drago, "On LLM Embeddings for Vulnerability Management," in 2025 9th Network Traffic Measurement and Analysis Conference (TMA), 2025, pp. 1–4, doi: 10.23919/TMA66427.2025.11097007.
S. Cai, Author, Author, Author, Author, and Author, "DDP-DAR: Network intrusion detection based on denoising diffusion probabilistic model and dual-attention residual network," Neural Netw., vol. 184, p. 107064, 2025, doi: 10.1016/j.neunet.2024.107064.
S. Gul, S. Arshad, S. M. U. Saeed, A. Akram, and M. A. Azam, "WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches," Computers, vol. 14, no. 4, pp. 1–21, 2024, doi: 10.3390/computers14010004.
C. Park, J. Lee, Y. Kim, J. G. Park, H. Kim, and D. Hong, "An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks," IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2330–2345, 2023, doi: 10.1109/JIOT.2022.3211346.
Z. Ullah, Author, Author, Author, Author, and Author, "Interpretable and Adaptive GAN-BiLSTM Approach for Cyber Threat Detection in IoMT-based Healthcare 5.0," IEEE Journal of Biomedical and Health Informatics, 2025, doi: 10.1109/JBHI.2025.3573097.
Y. Feng, Y. Si, J. Zhang, Z. Cai, and H. Zhao, "SA-WGAN based data enhancement method for industrial Internet intrusion detection," Computational Materials and Continua, vol. 84, no. 3, pp. 4432–4448, 2025, doi: 10.32604/cmc.2025.064696.
S. S. Khatami, M. Shoeibi, A. E. oskouei, D. Martín, and M. K. Dashliboroun, "5DGWO-GAN: A novel five-dimensional gray wolf optimizer for generative adversarial network-enabled intrusion detection in IoT systems," Computers, Materials & Continua, vol. 82, no. 1, pp. 882–910, 2025, doi: 10.32604/cmc.2024.059999.
M. Jamoos, A. M. Mora, M. Alkhanafseh, and O. Surakhi, "A new data-balancing approach based on generative adversarial network for network intrusion detection system," Electronics, vol. 12, no. 12, p. 2851, 2023, doi: 10.3390/electronics12132851.
M. S. Siddique, Author, Author, Author, Author, and Author, "An intelligent intrusion detection system for cyber-physical systems using GAN-LSTM networks," Franklin Open, vol. 11, p. 100281, 2025, doi: 10.1016/j.fraope.2025.100281.
X. Wang, Y. Xu, Y. Xu, Z. Wang, and Y. Wu, "Intrusion Detection System for In-Vehicle CAN-FD Bus ID Based on GAN Model," IEEE Access, vol. 12, pp. 82402–82412, 2024, doi: 10.1109/ACCESS.2024.3412933.
H. N. Nguyen, T. Lan-Phan, and C. J. Song, "Generative Adversarial Network-Based Network Intrusion Detection System for Supervisory Control and Data Acquisition System," in 2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 2024, pp. 1–3, doi: 10.1109/ICCE-Asia63397.2024.10773791.
A. Rahman, Author, Author, Author, Author, and Author, "SYN-GAN: A robust intrusion detection system using GAN-based synthetic data for IoT security," Internet Things, vol. 26, p. 101212, 2024, doi: 10.1016/j.iot.2024.101212.
U. Kumaran, S. Thangam, T. V. N. Prabhakar, J. Selvaganesan, and H. N. Vishwas, "Adversarial defense: A GAN-IF based cyber-security model for intrusion detection in software piracy," Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 14, no. 4, pp. 96–114, 2023, doi: 10.58346/JOWUA.2023.I4.008.
S. Aldhaheri and A. Alhuzali, "SGAN-IDS: Self-attention-based generative adversarial network against intrusion detection systems," Sensors, vol. 23, no. 18, p. 7796, 2023, doi: 10.3390/s23187796.
M. A. Ferrag, D. Hamouda, M. Debbah, L. Maglaras, and A. Lakas, "Generative Adversarial Networks-Driven Cyber Threat Intelligence Detection Framework for Securing Internet of Things," in 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Cyprus, 2023, pp. 196–200, doi: 10.1109/DCOSS-IoT58021.2023.00042.
S. Koparde, S. Kothari, A. Pawar, P. Vyas, S. Sawant, and P. Tank, "Intrusion detection system (IDS) using generative adversarial networks (GAN) & XGBoost (XGB)," in Proc. Int. Conf. Appl. Mach. Intell. Data Analytics (ICAMIDA), 2025, pp. 1–6, doi: 10.1109/ICAMIDA64673.2025.11208916.
K. Indhumathi, G. Pandiselvi, P. J, N. M. F, P. E, and A. B, "Utilizing Generative Adversarial Networks for Synthetic Data Generation in Cybersecurity Applications," in 2025 2nd Int Conf Intelligent Algorithms for Computational Intelligence Systems (IACIS), 2025, pp. 1–7, doi: 10.1109/IACIS65746.2025.11211266.
M. N. Halgamuge, Author, Author, Author, Author, and Author, "LLM-Driven Adaptive Security for the Internet of Energy (IoE)," IEEE Network, vol. 40, no. 1, pp. 35–43, 2026, doi: 10.1109/MNET.2025.3620562.
A. Appenzeller, M. Leitner, P. Philipp, E. Krempel, and J. Beyerer, "Privacy and utility of private synthetic data for medical data analyses," Applied Sciences, vol. 12, no. 23, p. 12320, 2022, doi: 10.3390/app122312320.
S. Imtiaz, M. Arsalan, V. Vlassov, and R. Sadre, "Synthetic and Private Smart Health Care Data Generation using GAN," in 2021 International Conference on Computer Communications and Networks (ICCCN), 2021, pp. 1–7, doi: 10.1109/ICCCN52240.2021.9522203.
S. Hu, Author, Author, Author, Author, and Author, "Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer," in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 14994–15003, doi: 10.1109/CVPR52688.2022.01459.
S. Ho, Author, Author, Author, Author, and Author, "DP-GAN: Differentially private consecutive data publishing using generative adversarial nets," Journal of Network and Computer Applications, vol. 185, p. 103066, 2021, doi: 10.1016/j.jnca.2021.103066.
Y. Li, Q. Tan, and B. S. Shin, "CryptoGAN: Privacy-Preserving Federated Generative Adversarial Networks With Homomorphic Encryption in Healthcare Systems," IEEE Transactions on Computational Social Systems, vol. 12, no. 6, pp. 5330–5341, 2025, doi: 10.1109/TCSS.2025.3570990.
P. C. Hsu, Z. Yu, S. Mise, and H. Miyaji, "Privacy-Diffusion: Privacy-preserving stable diffusion without FHE and differential privacy," IEEE Access, vol. 13, pp. 75914–75203, 2025, doi: 10.1109/ACCESS.2025.3562563.
S. Misbah, Author, Author, Author, Author, and Author, "Generative AI-Driven Smart Contract Optimization for Secure and Scalable Smart City Services," Smart Cities, vol. 8, no. 4, p. 118, 2025, doi: 10.3390/smartcities8040118.
S. Yin, X. Zhang, and L. Zuo, "Defending against adversarial attacks using spherical sampling-based variational auto-encoder," Neurocomputing, vol. 478, pp. 1–10, 2022, doi: 10.1016/j.neucom.2021.12.080.
X. Zeng, Y. Zhuo, T. Liao, and J. Guo, "Cloud-GAN: Cloud generation adversarial networks for anomaly detection," Pattern Recognit., vol. 157, p. 110866, 2025, doi: 10.1016/j.patcog.2024.110866.
F. Xiao, J. Zhou, K. Han, H. Hu, and J. Fan, "Unsupervised anomaly detection using inverse generative adversarial networks," Inf. Sci., vol. 689, p. 121435, 2025, doi: 10.1016/j.ins.2024.121435.
A. Baral, B. K. Paikaray, and S. Baral, "Enhancing cyber threat detection with generative adversarial networks and anomaly detection techniques," in Proc. 2nd Int. Conf. Circuits, Power Intell. Syst. (CCPIS), 2025, pp. 1–6, doi: 10.1109/CCPIS65231.2025.11234235.
S. Badakhshan and J. Zhang, "Generative AI-Enhanced Real-Time Anomaly Detection in Integrated Energy Systems," in IEEE Transactions on Smart Grid, 2025.
A. J, V. Ebenezer, A. J. Isaac, J. Marshell, P. Pradeepa, and V. Naveen, "Adversarial Attacks on Generative AI Anomaly Detection in the Quantum Era," in 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2023, pp. 1833–1840, doi: 10.1109/ICECA58529.2023.10395092.
J. Lee, Author, Author, Author, Author, and Author, "Anomaly Detection Using Generative Language Models and Deep Feature-Based Time Series Similarity," in IEEE Access, vol. 13, 2025, pp. 157147–157159.
A. Choi, A. Jiang, S. Jumani, D. Luong, and F. D. Troia, "Synthetic Malware Using Deep Variational Autoencoders and Generative Adversarial Networks," EAI Endorsed Transactions on Internet of Things, vol. 10, 2024, doi: 10.4108/eetiot.6566.
T. Taylor and A. Eleyan, "Using variational autoencoders to increase the performance of malware classification," in 2021 International Symposium on Networks, Computers and Communications (ISNCC), 2021, pp. 1–8, doi: 10.1109/ISNCC52172.2021.9615643.
T. Bao, K. Trousil, Q. D. Tran, F. D. Troia, and Y. Park, "Generating synthetic malware samples using generative AI," IEEE Access, vol. 13, pp. 59725–59736, 2025, doi: 10.1109/ACCESS.2025.3556704.