Division of Computer Science,
Louisiana State University
3272K Patrick Taylor Hall
Tel: (225)-578-8353
Email: zhang at csc dot lsu dot edu
Some Recent Research Projects:
Deep neural network for detection of Android malware
Malware targeting mobile devices is a pervasive problem in modern
life. The detection of malware is essentially a software
classification problem In this project, we investigated the
effectiveness of Deep Neural Networks (DNNs) for classification of
Android applications. A random walk was conducted on the code to
generate sequences of event that are intrinsic to the program.
Leveraging the ability of DNNs to learn complex and flexible
features, we designed a Convolutional Neural Network (CNN) to detect
malware based on the event sequence. We tested and compared our CNN
to a recurrent neural network (LSTM) and other n-gram based methods.
Both CNN and LSTM significantly outperformed n-gram based methods.
Surprisingly, the performance of our CNN is also much better than
that of the LSTM, which is considered a natural choice for
sequential data.
Code graph on which we conduct random walk
Structure of the sequence CNN
Prediction of user attributes from tweets
User attributes such as age, and education are valuable
information for various online services such as personalized recommendation, marketing, public health and social study.
We consider the problem of inferring
these attributes from a collection of tweets generated by online users.
We designed a model that uses deep neural network to
discover text patterns of different sizes and complexities and
combines attention-aware DNN with max-margin classification.
Experiment results on real-world datasets show that the model outperforms
traditional text classification methods such as SVM and other
deep neural-network models such as regular LSTM and CNN.
