Dr. Yangqiu Song Wins Best Paper Award in ACM SIGKDD 2017 Applied Data Science Track

Dr. Yangqiu Song and his collaboration with members at West Virginia University and Comodo Security Solutions, Inc. received the Best Paper Award in applied data science track with a paper titled "HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network" at the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2017). The event took place on 13-17 August 2017 in Halifax, Nova Scotia, Canada.

The paper aims to analyse different relationships between Android malware and Application Programming Interface (API) and create higher-level semantics which require more efforts for attackers to evade the detection. Based on the extracted features, a novel Android malware detection framework, Hindroid, was presented. It introduces a structured heterogeneous information network (HIN), representing Android apps, and a meta-path based approach to link the apps.

Congratulations to Dr. Song and his collaborators!

For more information about the research, please refer to the introduction video, KDD best paper page and news report.

(from left to right) Roberto J. Bayardo (Google, KDD Applied Data Science Chair), Yanfang (Fanny) Ye (WVU), Yangqiu Song (HKUST), Charles Elkan (Amazon & UCSD, KDD Applied Data Science Chair)

(from left to right) Roberto J. Bayardo (Google, KDD Applied Data Science Chair), Yanfang (Fanny) Ye (WVU), Yangqiu Song (HKUST), Charles Elkan (Amazon & UCSD, KDD Applied Data Science Chair)

23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Best Paper Award 2017 plaque

23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Best Paper Award 2017 plaque