AI-POWERED CYBER THREAT DETECTION AND RESPONSE: LEVERAGING NETWORK, TEXTUAL, AND RELATIONAL DATA

Abstract

To address the acute security threats, there has been a movement toward deep learning techniques to automate and supplement threat detection and response systems. Modern sophisticated attacks like Advanced Persistent Threats (APTs), malware, and phishing are eluding detection by traditional rules and signatures. This study examines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in classifications and interesting to note that the same is studied regarding their detection of malicious behaviour across diverse datasets in cybersecurity. The study intends to go further to examine deep learning models used in real-time monitoring, classification, and incident response of network traffic logs, system logs, user activity logs, and malware samples. The paper will also highlight the impact that exercises such as normalization, Z-scoring, and data imputation for the improvement of model performance can have on the results. Key challenges considered by the paper to affect the AI systems concerned are issues faced with scalability, data imbalance, and adaptation to novel threats. A comparison study indicates the development of hybrid models and real-time detection systems, shedding light on how deep learning could be used to design more adaptable, resource-efficient, and effective cybersecurity solutions. The evolution of models from feedback loops will also run through the paper.