Using AI for Real-Time Threat Detection and Anomaly Identification

Abstract

Cybersecurity threats have grown in complexity, requiring organizations to adopt advanced technologies for timely
detection and mitigation. Artificial Intelligence (AI) has emerged as a powerful solution for real-time threat detection
and anomaly identification, leveraging its ability to analyze vast amounts of data and recognize patterns indicative of
malicious activity. This paper explores the implementation of AI algorithms, including machine learning and deep learning
techniques, to enhance cybersecurity systems. Key methods such as supervised learning for classification, unsupervised
learning for anomaly detection, and reinforcement learning for adaptive defense are examined. Additionally, the paper
presents a comparative analysis of AI models based on accuracy, speed, and false positive rates. Emphasis is placed on the
advantages of real-time detection systems, particularly in identifying zero-day attacks and sophisticated threats. Despite its
potential, challenges such as data privacy, model bias, and adversarial attacks remain. The study concludes by proposing
future directions for improving AI-powered cybersecurity systems through explainable AI (XAI), federated learning, and
continuous model training. Ultimately, the integration of AI in cybersecurity represents a transformative step toward more
resilient and proactive threat management.