人工智能驱动的网络安全:利用机器学习和深度学习技术在复杂的IT网络中进行实时威胁检测、分析和缓解

Dabi Dabouabi Dalo Alionsi
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引用次数: 0

摘要

随着IT网络复杂性的不断升级和网络威胁的激增,对先进、实时安全解决方案的需求从未如此重要。机器学习(ML)和深度学习(DL)为增强这些复杂网络中的威胁检测、分析和缓解提供了有希望的途径。本文深入研究了机器学习和深度学习技术在网络安全领域的融合,重点研究了它们在IT基础设施中实时威胁检测的应用。根据最近的研究和发展,该研究强调了这些技术在超越传统安全模型方面的潜力,同时也揭示了固有的挑战和未来探索的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-driven cybersecurity: Utilizing machine learning and deep learning techniques for real-time threat detection, analysis, and mitigation in complex IT networks
With the escalating complexity of IT networks and the surge in cyber threats, the need for advanced, real-time security solutions has never been more paramount. Machine learning (ML) and deep learning (DL) present promising avenues for enhancing the detection, analysis, and mitigation of threats in these intricate networks. The paper delves into the confluence of ML and DL techniques in the realm of cybersecurity, focusing on their application for real-time threat detection within IT infrastructures. Drawing from recent research and developments, the study underscores the potential of these techniques in outmaneuvering conventional security models, while also shedding light on the inherent challenges and areas for future exploration.
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