Deep-Learning-Based Endpoint Attack Detection System for Digital Asset Management in Enterprise Environments

As cyberattacks become more intelligent and diverse, enterprises’ digital assets face greater challenges. We developed a learning-based endpoint attack detection system (DLEADS) that continuously monitors CPU usage, memory load, disk I/O, network traffic, and other system metrics. By feeding data into a convolutional neural network, the system presents high accuracy in detecting abnormal behavior and classifying various attack types, enabling early warning and rapid incident response. DLEADS demonstrates high performance on real-world enterprise datasets, offering a practical solution for automated cybersecurity management.