综述AI在入侵检测中的应用趋势与技术对比
Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
- 基于对比分析，梳理主流AI技术在入侵检测中的应用
- 涵盖生成式AI、联邦学习等新兴方法，评估其性能表现
- 适合关注安全AI落地与可解释性的研究人员和工程师
人工智能（AI）因其模式识别、任务自动化及降低时间和成本的能力，在诸多领域广泛应用。其在网络安全中的融合日益受到关注，尤其体现在入侵检测、恶意软件分析、网络钓鱼或垃圾邮件检测等方面。随着AI与网络安全的不断发展，新方法与新思路不断涌现。当前趋势包括生成式AI、自然语言处理、用于隐私保护协同训练的联邦学习，以及确保可解释性与可信度的可解释AI，这些在网络安全中至关重要。本文对当前基于AI的网络安全趋势进行了深入综述，重点聚焦于入侵检测方法，旨在通过基于所用AI技术及其报告性能的对比分析，揭示有意义的洞察。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as intrusion detection, malware analysis, and phishing or spam detection. As AI and cybersecurity evolve, new methods and approaches emerge regularly. Current trends include the use of Generative AI, Natural Language Processing, Federated Learning for privacy-preserving collaborative training, and eXplainable AI to ensure interpretability and trust, which are vital in cybersecurity. This paper presents an interesting review of current AI-based cybersecurity trends, focusing on intrusion detection approaches and aiming to uncover meaningful insights through comparative analysis based on the employed AI techniques and reported performance.
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