Using AI to Identify and Mitigate Cybersecurity Threats in Fog Computing Environments

Mohan Harish Maturi, Srikar Podicheti, Karthik Meduri, Geeta Sandeep Nadella

Abstract


Fog computing represents a transformative paradigm shift, bridging the gap between centralized cloud architectures and distributed edge computing environments. This research explores the cybersecurity challenges introduced by the dispersed nature of fog computing and evaluates the effectiveness of artificial intelligence (AI) in mitigating these risks. Traditional security solutions are often inadequate for these dynamic environments. This study examines the applicability of AI-driven techniques of AI machine-learning Models like (Random Forest, Gaussian Naive Bayes, Logistic Regression, and KNN) in addressing these cybersecurity challenges and producing a high accuracy score of prediction of threats. Machine learning models are employed to detect anomalies and potential security breaches in algorithms that analyze complex data streams for subtle deviations, and natural language processing techniques improve the analysis of security logs and communication patterns. The research highlights the strategic value of AI in safeguarding decentralized computing resources and provides a foundation for future exploration and practical implementation of AI-driven cybersecurity measures in fog computing contexts.


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References


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