Scholarlink

<p>The proliferation of the Internet of Things (IoT) has ushered in a new era of connectivity, yet this advancement brings heightened vulnerability to cyber threats. Intrusion Detection Systems (IDS) remain critical for safeguarding IoT ecosystems; however, their effectiveness depends on adapting to IoT-specific challenges. This paper presents a streamlined review of IoT security requirements and IDS approaches while introducing a conceptual evaluation framework for analyzing IDS techniques in terms of scalability, deployment feasibility, and cost-benefit tradeoffs. Existing methods are critiqued, with research gaps identified in areas such as adversarial machine learning resilience, federated IDS for privacy preservation, and benchmarking with IoT-native datasets. Prioritized directions for future work are also proposed. The contribution lies in integrating a comprehensive IDS taxonomy with recent IoT datasets (2020–2023), emphasizing detection strategies, placement, validation, and attack-oriented IDS designs. This holistic synthesis, rarely addressed in prior surveys, provides updated insights and practical guidance for IoT security researchers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comprehensive analysis of intrusion detection systems for enhancing security in internet of things environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

查看全文
  • Hussam Hussein Abu Munshar,
  • Farah Jemili,
  • Ouajdi Korbaa,
  • Mohammad Alauthmaan

摘要

The proliferation of the Internet of Things (IoT) has ushered in a new era of connectivity, yet this advancement brings heightened vulnerability to cyber threats. Intrusion Detection Systems (IDS) remain critical for safeguarding IoT ecosystems; however, their effectiveness depends on adapting to IoT-specific challenges. This paper presents a streamlined review of IoT security requirements and IDS approaches while introducing a conceptual evaluation framework for analyzing IDS techniques in terms of scalability, deployment feasibility, and cost-benefit tradeoffs. Existing methods are critiqued, with research gaps identified in areas such as adversarial machine learning resilience, federated IDS for privacy preservation, and benchmarking with IoT-native datasets. Prioritized directions for future work are also proposed. The contribution lies in integrating a comprehensive IDS taxonomy with recent IoT datasets (2020–2023), emphasizing detection strategies, placement, validation, and attack-oriented IDS designs. This holistic synthesis, rarely addressed in prior surveys, provides updated insights and practical guidance for IoT security researchers.