Improving Malware Detection Accuracy by Extracting Icon Information

Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extract information from icons. Our proposed approach consists of two steps: 1) extracting icon features using summary statics, histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features. Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models. In particular, our experiments show an average accuracy increase of 10% when icon clusters are used in the prediction model.Comment: Full version. IEEE MIPR 201

Complexity and Behind the Horizon Cut Off

Motivated by $T{\overline T}$ deformation of a conformal field theory we compute holographic complexity for a black brane solution with a cut off using "complexity=action" proposal. In order to have a late time behavior consistent with Lloyd's bound one is forced to have a cut off behind the horizon whose value is fixed by the boundary cut off. Using this result we compute holographic complexity for two dimensional AdS solutions where we get expected late times linear growth. It is in contrast with the naively computation which is done without assuming the cut off where the complexity approaches a constant at the late time.Comment: 14 pages, 2 figures, refs added, contribution of a counter term is added, minor correction, the final conclusion is not change