Generative Adversarial Network for Detecting Cyber Threats in Industrial Systems

Krundyshev, Vasiliy; Kalinin, Maxim O.

doi:10.1007/978-981-33-6632-9_1

Cited by 5 publications

(1 citation statement)

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“…It emphasizes the need for further research and development to address challenges and optimize GAN-based solutions for practical implementation in enterprise security frameworks. The intersection of adaptive generative models and cybersecurity explained by Krundyshev V. and Kalinin M. [5]. Investigating the dynamic nature of threats within enterprise networks, the study explores the development and application of adaptive generative models to enhance threat detection capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive generative AI for dynamic cybersecurity threat detection in enterprises

Naveen Vemuri,

Naresh Thaneeru,

Venkata Manoj Tatikonda

2024

Int. J. Sci. Res. Arch.

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This research paper provides a thorough examination of the application of Generative Artificial Intelligence (AI) in the context of dynamic cybersecurity threat detection within enterprises. Recognizing the evolving nature of cyber threats, the study focuses on adaptive generative AI models designed to enhance threat detection capabilities. Through an extensive review of existing literature and case studies, the paper explores various Adaptive Generative AI methodologies, including machine learning algorithms, continuous learning mechanisms, and real-time data processing. The analysis encompasses the strengths and limitations of these approaches, shedding light on their efficacy in addressing the complex and dynamic cybersecurity landscape. By offering a comprehensive overview, this research aims to guide the development and implementation of adaptive generative AI solutions for effective threat detection and mitigation in enterprise cybersecurity.

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Section: Literature Reviewmentioning

confidence: 99%