Heterogeneous Graph Matching Networks

no code implementations • 17 Oct 2019 • Shen Wang, Zhengzhang Chen, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, Philip S. Yu

Information systems have widely been the target of malware attacks.

Graph Matching Malware Detection

Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification

no code implementations • 10 Dec 2018 • Shen Wang, Zhengzhang Chen, Ding Li, Lu-An Tang, Jingchao Ni, Zhichun Li, Junghwan Rhee, Haifeng Chen, Philip S. Yu

The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process.

Graph Classification Graph Embedding +2

Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer

no code implementations • 25 Aug 2017 • Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li

Given a well-trained dependency graph from a source domain and an immature dependency graph from a target domain, how can we extract the entity and dependency knowledge from the source to enhance the target?

Graph Learning Intrusion Detection +1

Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events

no code implementations • 26 Aug 2016 • Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Kai Zhang

Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process.

Anomaly Detection