Recently, side-channel attacks based on deep learning (DLSCAs) have attracted much attention. Many works have improved the performance of DLSCAs by designing advanced neural network architectures and training strategies. There are few studies on leakage models for DLSCAs. Existing researches usually utilize the intermediate value Hamming weight (HW) and the intermediate value itself (ID) as leakage models. Training a classifier with good performance is challenging due to the many label classes in the ID leakage model. The HW leakage model can significantly reduce the number of labels, but it will cause samples imbalance. In this paper, we propose a new DLSCA leakage model, named Balanced Labels Compression (BLC). We consider dividing sensitive intermediate values with same lowest bits into same class to obtain balanced labels. Then, we train a classifier using the compressed BLC labels and profiling energy traces. At the attack phase, the probability distribution of BLC labels is extended to the probability distribution of sensitive intermediate values. We conduct extensive comparison experiments with HW, ID, and BLC leakage models under the two scenarios of sufficient and insufficient profiling energy traces. Further, we exploit VAE to improve attack performance when energy traces are insufficient. Experimental results show that VAE-based data augmentation can significantly reduce required energy traces.

We present LUCE, the first life-long predictive model for automated property valuation. LUCE addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on limited volume of recent house transaction. As a departure from prior work, LUCE organizes the house data in a HIN where graph nodes are house entities and attributes that are important for house price valuation. We employ GCN to extract the spatial information from the HIN, and then use LSTM network to model the temporal dependencies over time. Unlike prior work, LUCE makes effective use of the limited house transactions in the past few months to update valuation information for all house entities. By providing a complete and up-to-date house valuation dataset, LUCE thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of LUCE by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that LUCE not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth.

Graph Neural Networks (GNNs) have yielded fruitful results in learning multi-view graph data. However, it is challenging for existing GNNs to capture the potential correlation information (PCI) among the graph structure features of multiple views. It is also challenging to adaptively identify valuable neighbors for node feature fusion in different views. To this end, we propose a novelReinforcedTensorGraphNeuralNetwork (RTGNN) framework to more effectively perform multi-view graph representation learning through reinforcing inter- and intra-graph aggregation. Specifically, RTGNN first uses tensor decomposition to extract the graph structure features (GSFs) of each view in the common feature space. These GSFs contain the PCI of multiple views and alleviate fusion conflicts that may be caused by differences between view feature spaces in cross-view feature fusion. Since fusing the features of all neighbor nodes may harm the features of the center node, we filter the irrelevant neighbors to improve the performance of intra-graph aggregation in each view. Concretely, a reinforcement learning (RL)-guided scheme is developed to automatically calculate the optimal filtering threshold for each view, avoiding tedious manual updates and infeasible back propagation updates. Experimental results and analysis on five datasets show that RTGNN surpasses the best multi-view graph representation baselines and achieves the maximum 14.26% performance improvement in terms of F1. The code link ishttps://github.com/RingBDStack/RTGNN.

Device-level security has become a major concern in smart home systems. Detecting problems in smart home sytems strives to increase accuracy in near real time without hampering the regular tasks of the smart home. The current state of the art in detecting anomalies in smart home devices is mainly focused on the app level, which provides a basic level of security by assuming that the devices are functioning correctly. However, this approach is insufficient for ensuring the overall security of the system, as it overlooks the possibility of anomalies occurring at the lower layers such as the devices. In this article, we propose a novel notion, correlated graph , and with the aid of that, we develop our system to detect misbehaving devices without modifying the existing system. Our correlated graphs explicitly represent the contextual correlations among smart devices with little knowledge about the system. We further propose a linkage path model and a sensitivity ranking method to assist in detecting the abnormalities. We implement a semi-automatic prototype of our approach, evaluate it in real-world settings, and demonstrate its efficiency, which achieves an accuracy of around 90% in near real time.

The smart card is being used all over the world, because of its power to store data securely and execute calculations confidentially. This paper proposes an easy way to implement the strategy which can guarantee data integrity and privacy in the transmission path between the terminal application of the smart card and the smart card. This strategy consists of cryptographic functions of message and methods for transmitting APDU and managing secret keys. Some cryptographic functions of message are defined and developed for transmitting APDU confidentially. These cryptographic functions can keep the byte length of input and output messages identical. This strategy for transmitting APDU points out how to call cryptographic functions to encode or decode the data body of APDU, and how to construct a modified APDU message structure according to the original APDU message structure, which will be sent to its intended recipient instead of the original APDU message structure. With the proper secret keys, the authorized party can recover this modified APDU to its corresponding original APDU properly, without any information leakage. The transfer of secret keys to other parties is the most difficult aspect of secure APDU transmission, whereas data encryption/decryption is relatively straightforward. Two distinct strategies for managing secret keys, namely the static and the dynamic are introduced and compared. The dynamic strategy is preferred for a terminal application and smart card which do not need to authenticate each other. The implementation of this dynamic strategy has been detailed.