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Jiang, H., Turki, T., Wang, J. T. L..  2018.  DLGraph: Malware Detection Using Deep Learning and Graph Embedding. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). :1029-1033.

In this paper we present a new approach, named DLGraph, for malware detection using deep learning and graph embedding. DLGraph employs two stacked denoising autoencoders (SDAs) for representation learning, taking into consideration computer programs' function-call graphs and Windows application programming interface (API) calls. Given a program, we first use a graph embedding technique that maps the program's function-call graph to a vector in a low-dimensional feature space. One SDA in our deep learning model is used to learn a latent representation of the embedded vector of the function-call graph. The other SDA in our model is used to learn a latent representation of the given program's Windows API calls. The two learned latent representations are then merged to form a combined feature vector. Finally, we use softmax regression to classify the combined feature vector for predicting whether the given program is malware or not. Experimental results based on different datasets demonstrate the effectiveness of the proposed approach and its superiority over a related method.

Jiang, H., Xu, Q., Liu, C., Liu, Z..  2017.  An Efficient CPA-Secure Encryption Scheme with Equality Test. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 2:38–45.

In this paper, we propose a CPA-Secure encryption scheme with equality test. Unlike other public key solutions, in our scheme, only the data owner can encrypt the message and get the comparable ciphertext, and only the tester with token who can perform the equality test. Our encryption scheme is based on multiplicative homomorphism of ElGamal Encryption and Non Interactive Zero Knowledge proof of Discrete Log. We proof that the proposed scheme is OW-CPA security under the attack of the adversary who has equality test token, and IND-CPA security under the attack of adversary who can not test the equality. The proposed scheme only suppose to compare two ciphertexts encrypted by same user, though it is less of flexibility, it is efficient and more suitable for data outsourcing scenario.

Su, J. C., Wu, C., Jiang, H., Maji, S..  2017.  Reasoning About Fine-Grained Attribute Phrases Using Reference Games. 2017 IEEE International Conference on Computer Vision (ICCV). :418–427.

We present a framework for learning to describe finegrained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an object (e.g., “propeller on the nose” or “door near the wing” for airplanes) in a compositional manner. Instances within a category can be described by a set of these phrases and collectively they span the space of semantic attributes for a category. We collect a large dataset of such phrases by asking annotators to describe several visual differences between a pair of instances within a category. We then learn to describe and ground these phrases to images in the context of a reference game between a speaker and a listener. The goal of a speaker is to describe attributes of an image that allows the listener to correctly identify it within a pair. Data collected in a pairwise manner improves the ability of the speaker to generate, and the ability of the listener to interpret visual descriptions. Moreover, due to the compositionality of attribute phrases, the trained listeners can interpret descriptions not seen during training for image retrieval, and the speakers can generate attribute-based explanations for differences between previously unseen categories. We also show that embedding an image into the semantic space of attribute phrases derived from listeners offers 20% improvement in accuracy over existing attributebased representations on the FGVC-aircraft dataset.