AI based anomaly detection for cloud & edge computing

Intrusion Detection Systems (IDS) play a vital role in safeguarding cloud and edge computing environments from cyber threats. Traditional IDS models, primarily signature-based approaches, are ineffective against evolving attack patterns and suffer from high false positive rates. To address these limitations, this research presents an AI-based anomaly detection framework leveraging deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The proposed system is trained using the NSL-KDD and CICIDS2017 datasets, ensuring robustness against a diverse range of network intrusions. Our methodology incorporates feature extraction, data normalization, and advanced deep learning architectures to enhance detection accuracy and minimize false alarms. Experimental results demonstrate significant improvements over conventional IDS solutions, with higher precision, recall, and overall detection efficiency. Additionally, a comparative analysis highlights the effectiveness of AI-driven IDS in handling large-scale, real-time network traffic. Deployment strategies in cloud and edge environments are discussed, along with the computational challenges associated with deep learning-based IDS. Future research will explore federated learning for decentralized IDS and reinforcement learning for automated threat response, ensuring a more adaptive and scalable cybersecurity framework.