NIDS: Neural Network based Intrusion Detection System

Malware detection is an important problem. We propose an efficient real-time system to detect and classify malware based on network behavior using deep neural networks. We show that the splitting of the system into two neural networks, detection and analysis, is the key to increasing accuracy. Therefore, this approach allows for the construction of a real-time monitoring system with very low CPU usage. Finally, we provide a comparison analysis of four machine learning classifiers for this task.