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.
Munshar et al. (2026) studied this question.