Bin Yu

TL;DR: This work uses the Hough transform to detect lane boundaries with a parabolic model under a variety of road pavement types, lane structures and weather conditions and shows that the proposed method is relatively less prone to the image noise and is computationally tractable.

TL;DR: Training and evaluating on a dataset with 2M domain names shows that there is surprisingly little difference between various convolutional neural network and recurrent neural network based architectures in terms of accuracy, prompting a preference for the simpler architectures, since they are faster to train and to score, and less prone to overfitting.

TL;DR: This work proposes a novel way to label a large volume of data collected from real traffic as DGA/non-DGA and by using deep learning techniques, which can be trained with large amounts of real traffic, rather than small synthetic data sets, and therefore have better performance.

TL;DR: This paper design and implement a method called WordGraph for extracting dictionaries used by the Domain Generation Algorithms (DGAs) solely DNS traffic, which gives an efficient mechanism for detecting Dictionary-AGDs, without any need for reverse engineering to extract dictionaries.