Intrusion Detection Systems (IDS) play a critical role in contemporary cybersecurity architectures by identifying and responding to malicious activities within a network.This paper provides a comprehensive overview of IDS, examining both signature-based and anomaly-based detection methods.Signature-based systems operate by matching traffic against known attack patterns, whereas anomaly-based systems learn normal behavior to detect deviations.Each approach has its strengths and limitations in terms of accuracy, adaptability, and resource consumption.The article further explores hybrid IDS models that combine both methods for enhanced detection capability.Real-world implementations are also discussed, including IDS deployment in enterprise networks, cloud-based infrastructures, and IoT environments.Practical case studies illustrate how IDS can thwart unauthorized access, detect data breaches, and provide forensic insights.As cyber threats grow increasingly complex, IDS solutions are evolving with machine learning and AIdriven techniques to improve responsiveness and minimize false positives.Understanding how these systems function and where they are best applied is vital for developing resilient cybersecurity strategies in both corporate and critical infrastructure sectors.
| Jurnal | ТАТУ хабарлари |
|---|---|
| Nashr sanasi | 2025-10-20 |
| DOI | 10.61663/252tuitmct18 |
DOI: 10.61663/252tuitmct18 · Maqolaning asl sahifasi
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