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Next-Generation Defence on Innovations in Data-Driven Cyber Security for Threat Detection and Mitigation
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Author(s): T. Sharath (Bharath Institute of Higher Education and Research, India)and A. Muthukumaravel (Bharath Institute of Higher Education and Research, India)
Copyright: 2025
Pages: 16
Source title:
Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World
Source Author(s)/Editor(s): Sonia Singh (Toss Global Management, UK), Slim Hadoussa (Brest Business School, France), Thangaraja Arumugam (Vellore Institute of Technology, Chennai, India)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-9375-8.ch027
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Abstract
Within the current digital landscape, the issue of cybersecurity remains a prominent and urgent matter. The ever-evolving cyber threats pose significant risks to organizations across the globe. In cybersecurity, conventional methods frequently find themselves grappling to keep up with the ever-changing landscape of threats, resulting in potential weaknesses and breaches. A new approach called dynamic threat profiling using graph neural networks (GNNs) is proposed to tackle this challenge. One major issue is the lack of effectiveness in current cybersecurity methods when identifying and addressing emerging threats. GNNs provide a solution by utilizing cutting-edge machine learning methods to analyze real-time network data. They create dynamic graphs that capture the intricate connections between network entities. By extracting features and identifying anomalies within these graphs, GNNs facilitate threat detection and respond to it, thereby mitigating the consequences of cyber attacks.
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