{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:37:02Z","timestamp":1773931022659,"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":[[2021,8]]},"abstract":"<jats:p>Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions.  In this paper, we propose  MG-DVD,  a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time.  Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects,  which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then,  MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants,  which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD  is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed  MG-DVD  outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/209","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:00:49Z","timestamp":1628665249000},"page":"1512-1519","source":"Crossref","is-referenced-by-count":10,"title":["MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning"],"prefix":"10.24963","author":[{"given":"Chen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"},{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"},{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"},{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"},{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu-Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"},{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:02:01Z","timestamp":1628665321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/209"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/209","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}