Advancing IoT Security: A Review of Intrusion Detection Systems Challenges and Emerging Solutions

The rapid proliferation of the Internet of Things (IoT) has revolutionized industries through interconnected devices and smart decision-making. However, this expansion has also introduced significant security challenges, as IoT networks are characterized by heterogeneity, resource constraints, and evolving threats. Intrusion Detection Systems (IDS) have emerged as essential mechanisms to complement preventive measures, yet existing solutions fall short of addressing the full spectrum of IoT-specific vulnerabilities. This paper critically reviews state-of-the-art IDS techniques, including advanced methods such as machine learning, federated learning, blockchain, and hybrid detection. Additionally, emerging approaches like Generative Adversarial Networks (GANs), reinforcement learning, and bio-inspired algorithms are explored for their potential to enhance IDS adaptability and scalability. The role of complementary security techniques, such as penetration testing, is highlighted in validating and strengthening IDS implementations. Applications in critical areas such as smart cities are discussed, emphasizing the need for robust and efficient security mechanisms. Key challenges such as interoperability, real-time detection, and resource efficiency are analyzed, and future research directions are proposed to develop comprehensive IDS frameworks tailored to the dynamic and diverse IoT ecosystem.