Network Intrusion Detection Systems (NIDS) are vital in modern cybersecurity, serving as the first defense against complex threats. This research analyzes signature-based, anomaly-based, and machine learning NIDS across DARPA, NSL-KDD, and CICIDS2017 datasets, measuring accuracy, false positives, and computational cost. Hybrid methods combining signature-based and ML achieved 94.7% accuracy with 2.3% false positives. Deep learning, especially CNNs, excelled at zero-day detection but needed more resources. Findings highlight that integrating AI is essential for evolving threats and encrypted traffic analysis, guiding practitioners in selecting optimal NIDS configurations.
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Keywords: Machine Learning, Hybrid Approaches, Cybersecurity.