Enhancing Threat Detection in Cybersecurity with Machine Learning: Real-Time Analysis for Improved Network Defense
Abstract
As cyber threats evolve, traditional security systems struggle to detect sophisticated attacks in real time. This paper presents a machine learning-based threat detection model that analyzes network traffic patterns to identify anomalies indicative of potential cyberattacks. By utilizing supervised and unsupervised learning algorithms, the model continuously improves its detection capabilities, adapting to new threats as they emerge. Case studies demonstrate the model’s accuracy in detecting zero-day exploits and malicious behaviors, showcasing the role of machine learning in fortifying network defenses and reducing response times in cybersecurity.
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References
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