AI-Driven Threat Intelligence for Predicting Advanced Persistent Attacks in Cloud-Based IT Services

Abstract

The adoption of cloud-based IT services has transformed modern enterprise operations, offering flexibility and scalability. However, this evolution has also introduced significant security challenges, particularly from Advanced Persistent Threats (APTs), which are sophisticated, stealthy, and often long-lasting attacks designed to bypass conventional defence mechanisms. Addressing such threats requires a forward-looking approach that emphasizes prediction and early intervention rather than reactive countermeasures. This research presents an innovative artificial intelligence (AI)-based framework that combines threat intelligence with deep learning models to anticipate and detect APTs in cloud environments. The proposed system employs Long Short-Term Memory Autoencoders (LSTM-AE) to uncover abnormal patterns in system behaviours by analysing multiple data sources, including network traffic, system logs, and threat intelligence feeds. The framework is trained and evaluated using publicly available datasets such as CICIDS 2017, along with custom cloud log data. The results highlight the model's ability to achieve high detection accuracy while minimizing false positive rates, outperforming traditional intrusion detection approaches. By integrating contextual threat intelligence with AI-based behavioural analysis, the framework enhances real-time situational awareness and supports proactive cybersecurity measures. This study contributes a scalable and adaptive solution for strengthening cloud infrastructure against evolving and complex threat scenarios.