AI-Augmented Cybersecurity: Machine Learning for Real-Time Threat Detection

Abstract

In the rapidly evolving landscape of cybersecurity, the sophistication and frequency of cyber threats have significantly increased, necessitating advanced strategies for real-time threat detection and mitigation. This paper explores the application of machine learning (ML) techniques within an AI-augmented cybersecurity framework to enhance the detection of threats in real-time environments. By employing a range of ML algorithms, including supervised and unsupervised learning methods, we demonstrate their effectiveness in identifying anomalies and potential security breaches across various network architectures. The study utilizes a comprehensive dataset that simulates real-world cyber-attack scenarios, enabling the training and evaluation of these models in a controlled environment. The results indicate that ML-based approaches outperform traditional rule-based systems, achieving higher detection rates while minimizing false positives. Moreover, we discuss the implications of integrating AI-driven technologies in cybersecurity practices and their potential to significantly bolster an organization's defense mechanisms against evolving threats. This research contributes to the field by providing insights into effective methodologies for implementing machine learning in cybersecurity, establishing a foundation for future advancements in automated threat detection systems.