{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:26:57Z","timestamp":1773512817595,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/522","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"3762-3770","source":"Crossref","is-referenced-by-count":46,"title":["Heterogeneous Graph Matching Networks for Unknown Malware Detection"],"prefix":"10.24963","author":[{"given":"Shen","family":"Wang","sequence":"first","affiliation":[{"name":"University of Illinois at Chicago, USA"}]},{"given":"Zhengzhang","family":"Chen","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Xiao","family":"Yu","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Ding","family":"Li","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Jingchao","family":"Ni","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Lu-An","family":"Tang","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Jiaping","family":"Gui","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Zhichun","family":"Li","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Haifeng","family":"Chen","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, USA"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, USA"},{"name":"Tsinghua University, China"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:49:53Z","timestamp":1564285793000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/522"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/522","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}