activity
20172019
most citedAdversarial Defense Framework for Graph Neural Network

21 citations · 28 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CR20196 cited

Heterogeneous Graph Matching Networks

Shen Wang, Zhengzhang Chen, Xiao Yu +7

Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone t…

cs.LG201921 cited

Adversarial Defense Framework for Graph Neural Network

Shen Wang, Zhengzhang Chen, Jingchao Ni +4

Graph neural network (GNN), as a powerful representation learning model on graph data, attracts much attention across various disciplines. However, recent studies show that GNN is…

cs.CR2018

Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification

Shen Wang, Zhengzhang Chen, Ding Li +6

Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise i…

cs.CR2018

A Query System for Efficiently Investigating Complex Attack Behaviors for Enterprise Security

Peng Gao, Xusheng Xiao, Zhichun Li +4

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely at…

cs.CR2018

SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection

Peng Gao, Xusheng Xiao, Ding Li +6

Recently, advanced cyber attacks, which consist of a sequence of steps that involve many vulnerabilities and hosts, compromise the security of many well-protected businesses. This…

cs.CR2018

AIQL: Enabling Efficient Attack Investigation from System Monitoring Data

Peng Gao, Xusheng Xiao, Zhichun Li +4

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each host, and perform timely attack invest…