Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI
In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats.
Authors
- N. Sopova (ORCID: https://orcid.org/0000-0003-0597-5598)
- Santiago M. López García (ORCID: https://orcid.org/0000-0001-5652-379X)
- Wilfred Blessing N. R
- C. Seldev Christopher (ORCID: https://orcid.org/0000-0002-5228-029X)
Institutions
- College of Applied Sciences, Nizwa (OM)
- Saveetha University (IN)
- St Xavier’s College (IN)
Publication Details
- Published
- 2024-11-12
- DOI
- https://doi.org/10.1109/iccda64887.2024.10867379
- Citations
- 4
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 1.40