AI-SCAN: A Scalable AI-Driven IDS for Cyber Threat Detection in Cloud Environments
Khatha Mahendar, Gandla Shivakanth · 2025
AI-SCAN is a CNN-based scalable Intrusion Detection System (IDS) that detects known and unknown cyber-attacks with minimal false positives. AI-SCAN is created to solve contemporary cybersecurity challenges, employing a systematic approach involving data acquisition, preprocessing, feature selection, class balancing, model design, training, and evaluation. The model utilizes the CSE-CICIDS2018 dataset, a benchmark dataset mimicking real-world cloud network traffic with varied attack patterns, to train and test its performance. Using techniques like Z-score normalization, SMOTE class balancing (Synthetic Minority Oversampling Techniques), and a customized CNN architecture that distinguishes between malicious and legitimate network traffic, the model detects attacks with state-of-the-art accuracy. Measures of accuracy, precision, recall, and F1-score demonstrate that AI-SCAN outperformed the current IDS models with a 97.5% accuracy in detecting attacks and high sensitivity to uncommon and novel attack patterns. Balancing strategies and architecture guarantee scalability, robustness, and applicability for deployment in dynamic cloud environments.