Recent research in intrusion detection systems for wireless sensor networks focuses on developing intelligent, lightweight, and adaptive mechanisms to identify and mitigate security threats efficiently within resource-constrained environments. Modern IDS frameworks leverage machine learning, ensemble learning, and optimization algorithms to detect various attacks such as blackhole, Sybil, and flooding with high accuracy and minimal energy overhead. Researchers are also exploring self-learning and distributed IDS models that dynamically adapt to changing network conditions while maintaining low false-positive rates. These advancements aim to enhance the resilience, reliability, and security of WSNs without compromising their performance or energy efficiency.