{"type":"ScholarlyArticle","title":"Heterogeneous Graph Matching Networks for Unknown Malware Detection","authors":[{"name":"Shen Wang","slug":"shen-wang","given":"Shen","family":"Wang","affiliation":"University of Illinois at Chicago , USA; NEC Laboratories America , USA"},{"name":"Zhengzhang Chen","slug":"zhengzhang-chen","given":"Zhengzhang","family":"Chen","affiliation":"NEC Laboratories America , USA"},{"name":"Xiao Yu","slug":"xiao-yu","given":"Xiao","family":"Yu","affiliation":"NEC Laboratories America , USA"},{"name":"Ding Li","slug":"ding-li","given":"Ding","family":"Li","affiliation":"NEC Laboratories America , USA"},{"name":"Jingchao Ni","slug":"jingchao-ni","given":"Jingchao","family":"Ni","affiliation":"NEC Laboratories America , USA"},{"name":"Lu-An Tang","slug":"lu-an-tang","given":"Lu-An","family":"Tang","affiliation":"NEC Laboratories America , USA"},{"name":"Jiaping Gui","slug":"jiaping-gui","given":"Jiaping","family":"Gui","affiliation":"NEC Laboratories America , USA"},{"name":"Zhichun Li","slug":"zhichun-li","given":"Zhichun","family":"Li","affiliation":"NEC Laboratories America , USA"},{"name":"Haifeng Chen","slug":"haifeng-chen","given":"Haifeng","family":"Chen","affiliation":"NEC Laboratories America , USA"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"University of Illinois at Chicago , USA; Tsinghua University , China"}],"abstract":"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 to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohibitively high training cost. To address the limitations of existing techniques, we propose MatchGNet, a heterogeneous Graph Matching Network model to learn the graph representation and similarity metric simultaneously based on the invariant graph modeling of the program's execution behaviors. We conduct a systematic evaluation of our model and show that it is accurate in detecting malicious program behavior and can help detect malware attacks with less false positives. MatchGNet outperforms the state-of-the-art algorithms in malware detection by generating 50% less false positives while keeping zero false negatives.","doi":"10.24963/ijcai.2019/522","url":"https://ijcai.org/proceedings/2019/522","pdf":"https://ijcai.org/proceedings/2019/0522.pdf","markdown":"https://ijcai.org/proceedings/2019/522.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/522","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":522,"pages":{"first":3762,"last":3770},"keywords":["Multidisciplinary Topics and Applications: Security and Privacy","Machine Learning Applications: Networks","Machine Learning: Deep Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Babić et al., 2011] Domagoj Babić, Daniel Reynaud, and Dawn Song. 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