Ai-driven Threat Detection in Cloud-based Applications
Year: 2024
Type: article
Abstract: This comprehensive article examines the evolution and implementation of artificial intelligence and machine learning technologies in cloud security environments. The article analyzes the transformation from traditional security approaches to AI-driven solutions, focusing on deep learning architectures, reinforcement learning applications, and emerging technologies. It addresses the critical challenges in training AI models for cloud security, including data-related issues and their mitigation strategies. The article demonstrates significant improvements in threat detection, response capabilities, and cost-effectiveness through a comparative analysis of traditional versus AI-driven detection methods. The article further explores future directions in automated response systems, emerging technologies such as quantum computing integration, zero-trust architectures, and blockchain-based audit trails while identifying key research opportunities in explainable AI, real-time model updating, and cross-platform threat intelligence sharing.
Author: Researcher
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893105474
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