Comparative Analysis of Traditional vs. AI-Driven Network Security

Abstract

The increasing sophistication of cyber threats has driven the evolution of network security mechanisms. Traditional methods like firewalls, IDS, and antivirus software, which rely on established guidelines and signature-based detection, are now limited in addressing new and unknown threats. AI has introduced a new paradigm in network security, utilizing machine learning, behavioral analytics, and automated threat detection. AI-driven methods offer improved efficiency, accuracy, response time, adaptability, scalability, and cost-effectiveness. They analyze large data sets to detect patterns, recognize anomalies, and respond to threats in real time. While AI systems are costly and complex, their advantages in reducing false positives and handling zero-day attacks make them essential. The integration of AI with traditional methods can enhance security, combining strengths for a proactive defense against evolving cyber threats.