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.

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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
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article

Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI

N. Sopova, Santiago M. López García, Wilfred Blessing N. R, C. Seldev Christopher
4 citations
Network Security and Intrusion Detection
1.40
article

Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI

N. Sopova, Santiago M. López García, Wilfred Blessing N. R, C. Seldev Christopher
article en
4 citations

Abstract

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.

College of Applied Sciences, Nizwa (OM), Saveetha University (IN), St Xavier’s College (IN)
Openalex Percentile: Top 16%
Network Security and Intrusion Detection
1.40
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