Snort Versus Suricata in Intrusion Detection

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Abstract
In the contemporary digital age, the increasing complexity and frequency of cyber threats underscore the need for efficient network intrusion detection systems (NIDS). This paper provides a comprehensive comparative analysis of two prominent NIDS, Snort and Suricata, focusing on their architecture, detection capabilities, and performance metrics. It explores the historical development, operational frameworks, and technological foundations of these systems, highlighting their respective benefits and limitations in different network environments. Snort, known for its extensive rule-based detection, and Suricata, which leverages multi-threading for high-speed traffic handling, are evaluated based on specific security requirements, including traffic volumes, processing speeds, and threat types. The paper also discusses future advancements in NIDS, particularly through the integration of machine learning and AI, to enhance predictive and adaptive capabilities. This analysis aims to inform cybersecurity professionals about the qualifications and capabilities of Snort and Suricata, providing insights for their effective deployment in modern network security infrastructures. The discussion on future trends emphasizes the importance of continuous improvement in NIDS to address evolving cyber threats)

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