We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).

Critical infrastructures are falsely believed to be safe when they are isolated from the Internet. However, the recent appearance of Stuxnet demonstrated that isolated networks are no longer safe. We observe that a better intrusion detection scheme can be established based on the unique features of critical infrastructures. In this paper, we propose a whitelist-based detection system. Network and application-level whitelists are proposed, which are combined to form a novel cross-layer whitelist. Through experiments, we confirm that the proposed whitelists can exactly detect attack packets, which cannot be achieved by existing schemes.