Recent research in cluster-based intrusion detection systems (IDS) for wireless sensor networks emphasizes improving security, scalability, and energy efficiency by organizing sensor nodes into hierarchical clusters. These systems utilize cluster heads to monitor local node behavior, detect anomalies, and communicate aggregated intrusion data to base stations, thereby reducing communication overhead. Advanced approaches integrate machine learning, trust management, and fuzzy logic for accurate threat detection while minimizing false alarms. Studies also explore dynamic clustering, adaptive threshold mechanisms, and lightweight IDS frameworks to enhance resilience against insider attacks, blackhole attacks, and sinkhole intrusions, ensuring reliable and secure WSN operations.