Recent research in attack detection and prevention schemes in wireless sensor networks focuses on developing intelligent, adaptive, and lightweight security frameworks to safeguard against diverse network threats. Modern approaches integrate machine learning, trust management, and anomaly detection techniques to identify malicious behaviors such as sinkhole, wormhole, Sybil, and selective forwarding attacks. Researchers are emphasizing hybrid models that combine statistical analysis, deep learning, and cryptographic methods to enhance detection accuracy while minimizing false positives and energy consumption. These innovations aim to build resilient and energy-efficient WSNs capable of dynamically detecting and preventing security breaches in real-time network environments.