Leveraging Graph-Based AI for Large-Scale Cyber Threat Detection and Autonomous Response Mechanisms
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I5P101Keywords:
Graph Neural Networks (Gnns), Cyber Threat Detection, Autonomous Response, Anomaly Detection, Network Security, AI-Driven Cybersecurity, Real-Time Threat MitigationAbstract
Digital infrastructures are growing much faster, and as such, have increased the complexity and volumes of cyber threats. Conventional cyber security programs tend to fail to protect and counter complex attacks on the fly. This essay discusses how to adopt the use of graph-based Artificial Intelligence (AI) frameworks in detecting and responding to cyber threats on a grand scale. The graph-based AI relies on the existing relationships between the entities of a network, which results in the ability to have a holistic view of the pattern of attack. Our plan is to introduce a multiple-layer system that comprises graph neural networks (GNNs), anomaly detecting algorithms, and autonomous response templates that would help to optimize cybersecurity systems. We have a scalable, real-time detection, and proactive threat mitigation approach to things. The experimental findings reveal that they have a great deal better detection accuracy and response latency than traditional signature-based systems. The results highlight the promise of graph-based AI in enhancing cyber defenses and it offers a roadmap on how to introduce autonomous security systems in a dynamic network environment
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