Abstract: In this paper, a deep neural network is used to model the signed distance function (SDF) of a rigid object for real-time tracking using a single depth camera. By leveraging the generalization capability of the neural network, we could better represent the model of the object implicitly. With the training stage done off-line, our proposed methods are capable of real-time performance and running as fast as 1.29 ms per frame on one CPU core, which is suitable for applications with limited hardware capabilities. Furthermore, the memory footprint of our trained SDF-Net for an object is less than 10 kilobytes. A quantitative comparison using public dataset is being carried out and our approach is comparable with the state-of-the-arts. The methods are also tested on actual depth records to evaluate their performance in real-life scenarios.

Abstract: Insider threat detection has attracted a considerable interest from the researchers and industries. Existing work mainly focused on applying machine-learning techniques to detecting insider threat. However, this work requires “feature engineering” which is difficult and time-consuming. As we know, the deep learning technique can automatically learn powerful features. In this paper, we present a novel insider-threat detection method with Deep Neural Network (DNN) based on user behavior. Specifically, we use the LSTM-CNN framework to recognize user’s anomalous behavior. First, similar to natural language modeling, we use the Long Short Term Memory (LSTM) to learn the language of user behavior through user actions and extract abstracted temporal features. Second, the extracted features are converted to the fixed-size feature matrices and the Convolutional Neural Network (CNN) use these fixed-size feature matrices to detect insider threat. We conduct experiments on a public dataset of insider threats. Experimental results show that our method is indeed successful at detecting insider threat and we obtained AUC = 0.9449 in best case.

Abstract: In Mobile Ad-Hoc Networks, cooperative intrusion detection is efficient and scalable to massively parallel attacks. However, due to concerns of privacy leakage and resource costs, if without enough incentives, most mobile nodes are often selfish and disinterested in helping others to detect an intrusion event, thus an efficient incentive mechanism is required. In this paper, we formulate the incentive mechanism for cooperative intrusion detection as an evolutionary game and achieve an optimal solution to help nodes decide whether to participate in detection or not. Our proposed mechanism can deal with the problems that cooperative nodes do not own complete knowledge about other nodes. We develop a game algorithm to maximize nodes’utility. Simulations demonstrate that our strategy can efficiently incentivize potential nodes to cooperate.