Intrusion Detection Systems (IDS) are a cornerstone of cyber security research, aimed at identifying unauthorized access, malicious activities, and policy violations across networks and systems. Research papers in this domain explore a wide range of IDS approaches, including signature-based, anomaly-based, specification-based, and hybrid detection methods. Recent studies emphasize the application of machine learning, deep learning, and artificial intelligence to improve detection accuracy, reduce false positives, and adapt to evolving attack patterns such as zero-day exploits and advanced persistent threats (APTs). In addition, research highlights IDS deployment in diverse environments such as cloud computing, Internet of Things (IoT), industrial control systems, and software-defined networks (SDN). Emerging works focus on lightweight IDS for resource-constrained devices, distributed IDS for large-scale infrastructures, and privacy-preserving collaborative detection using federated learning and blockchain. Furthermore, attention is given to adversarial robustness, explainability, and integration of IDS into Zero Trust security frameworks to strengthen resilience against sophisticated attacks. Overall, IDS research continues to evolve as a critical defense layer in modern cyber security, addressing challenges of scalability, adaptability, and real-time detection.