The need to account for native code in Android apps is becoming urgent as the usage of native code is growing with both benign and malicious apps. However, most current state-of-the-art analysis tools cannot effectively analyze the data-flow behavior of native code. On the one hand, existing native dynamic analysis tools are primarily based on test input generation tools to analyze Android apps and are therefore unable to locate native code quickly. On the other hand, existing native static analysis tools are based on symbolic execution to analyze native code and are therefore limited by the path and state explosion issues. In order to effectively analyze the behavior of sensitive data in the native world, we first proposed INFuzz, a fuzzing module for Android native libraries based on Client/Server architecture. Then, we propose INFuzz-Droid, a lightweight automated fuzzing and taint analysis framework for Android native code, based on this. INFuzz-Droid first locates the Android native code to which sensitive data is passed and then uses INFuzz to perform fuzzing the native code to improve code coverage while analyzing the data flow in native code with a dynamic binary tool. Experimental results on benchmarks and real-world apps show that IN Fuzz-Droid can effectively detect the leakage or transfer of sensitive data in app native code and outperforms the state-of-the-art native analysis tools.

In recent years, many researchers have started to construct Graph Neural Networks (GNNs) to deal with graph classification task. Those GNNs can fit into a framework named Message Passing Neural Networks (MPNNs), which consists of two phases: a Message Passing phase used for updating node embeddings and a Readout phase. In Readout phase, node embeddings are aggregated to extract graph feature used for classification. However, the above operation may obscure the effect of the node embedding of each node on graph classification. Therefore, a node voting based graph classification model is proposed in this paper, called Node Voting net (NVnet). Similar to the MPNNs, NVnet also contains the Message Passing phase. The main differences between NVnet and MPNNs are: 1, A decoder for graph reconstruction is added to NVnet to make node embeddings contain graph structure information as much as possible; 2, In NVnet, the Readout phase is replaced by a new phase called Node Voting phase. In this new phase, an attention layer based on the gate mechanism is constructed to help each node to observe the node embeddings of other nodes in the graph, and each node predicts the class of the graph from its own perspective. The above process is called node voting. After voting, the results of all nodes are aggregated to get the final graph classification result. In addition, considering that aggregation operation may also obscure the differences between node voting results, a regularization term is added to drive node voting results to reach group consensus. We evaluate the performance of NVnet on 4 benchmark datasets. The experimental results show that NVnet performs well on graph classification task.

Most Graph Convolutional Networks (GCNs) used for graph classification task can fit into the Message Passing Neural Networks (MPNNs) framework. However, traditional MPNNs don't consider global information in the message passing phase in which the node embeddings are updated. In this paper, we propose a new model called Cross Message Passing Graph Neural Network (CMPGNN). The new model consists of two message passing phases, of which one is the local message passing phase used for node embeddings updating and another is the global message passing phase used for graph feature updating. Several convolutional layers are stacked together in the local message passing phase, in which different convolutional layers are used to update the node embeddings at different time steps. Each convolutional layer updates node embeddings not only according to the outputs calculated at the previous convolutional layer but also to the graph feature calculated at the previous time step. A readout layer shared by all time steps is used in the global message passing phase. At each time step, after the node embeddings are updated, the readout layer aggregates the embeddings of all nodes by using a global gated network, and feeds the aggregation result into a Gated Recurrent Unit (GRU) to update the graph feature.The above two message passing phases are executed alternatively. After all time steps, the graph feature obtained by the readout layer is fed into a MultiLayer perception for graph classification. We evaluate the performance of CMPGNN on 6 graph classification datasets. Experimental results show that compared with other 10 baselines, CMPGNN achieves the highest accuracy on 4 of the 6 benchmark datasets.

Movie investors seek for high box-office revenue. Usually, it is not an easy task for investors to estimate the return on their invests for movies, due to the complicated factors that could impact the box-office revenue, such as movie stars' appeal, potential audience reactions, movie genre, and so on. In this paper, we design and implement SMAS, an investor-oriented Social Media Analysis System focusing on movie invests, which provides various modules for capturing public opinions, assessing the value of movie stars, analyzing the temporal changes of their box-office impact, and predicting box-office revenues.