用机器学习提升物联网实时入侵检测能力
Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
- 基于机器学习构建实时入侵检测模型
- 综合评估多种算法在准确率与效率上的表现
- 为安全框架改进提供研究方向参考
随着物联网设备、应用及交互的日益网络化与集成化，针对物联网的攻击正不断上升。网络攻击对关键系统的隐私、安全、功能和可用性构成严重威胁，可能导致运营中断、财务损失、身份盗用和数据泄露。为有效保障物联网设备安全，实时入侵检测至关重要，尤其依赖机器学习技术识别威胁并降低风险。本文综述了当前基于机器学习的物联网入侵检测策略，重点关注实时响应、检测准确率和算法效率。通过检索知名学术数据库中的关键研究，建立了现有方法的分类体系，揭示了当前研究的空白与安全框架的局限性，为未来研究提供了切实可行的方向与洞察。
原文摘要 · Abstract (English)
Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a considerable vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.
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