A new survey paper published on arXiv explores the integration of generative artificial intelligence (AI) and federated learning (FL) for enhancing Intrusion Detection Systems (IDS). The paper highlights how generative models can address challenges like evolving attack behaviors and data scarcity by supporting anomaly detection, synthetic data generation, and alert explanation. Federated learning is presented as a method to train IDS models collaboratively without sharing sensitive local network traffic, making it suitable for privacy-conscious and distributed environments. The survey categorizes generative AI applications in IDS, including autoencoders, Generative Adversarial Networks (GANs), diffusion models, and Large Language Models (LLMs), and discusses their combined use with FL to tackle issues such as synthetic data quality, realistic traffic generation, and domain-specific LLMs for network security. AI
IMPACT This research could lead to more robust and privacy-preserving intrusion detection systems, crucial for securing networks and IoT devices against evolving cyber threats.
RANK_REASON The cluster contains a survey paper published on arXiv detailing research in AI and federated learning for intrusion detection systems. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- federated learning
- generative adversarial network
- generative artificial intelligence
- Hugging Face
- Internet of Things
- Intrusion Detection Systems
- large-language models
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