Deepdom: Malicious domain detection with scalable and heterogeneous graph convolutional networks

Computers & Security - Tập 99 - Trang 102057 - 2020
Xiaoqing Sun1,2, Zhiliang Wang1,2, Jiahai Yang1,2, Xinran Liu3
1Institute for Network Sciences and Cyberspace, Tsinghua University China
2National Research Center for Information Science and Technology, Beijing, China
3National Computer Network Emergency Response Technical Team/Coordination Center, Beijing, China

Tài liệu tham khảo

Alibaba, 2019. Euler. https://github.com/alibaba/euler. [Online]. Amazon Web Services, Inc., 2019. AWS | Alexa Top Sites - Up-to-date lists of the top sites on the web. https://aws.amazon.com/alexa-top-sites/. [Online]. Anderson, 2016, Deepdga: Adversarially-tuned domain generation and detection, 13 Antonakakis, 2010, Building a dynamic reputation system for dns., 273 Antonakakis, 2011, Detecting malware domains at the upper dns hierarchy., 11, 1 Antonakakis, 2012, From throw-away traffic to bots: Detecting the rise of dga-based malware., 12 Atwood, 2016, Diffusion-convolutional neural networks, 1993 Bilge, 2014, Exposure: a passive dns analysis service to detect and report malicious domains, ACM Transactions on Information and System Security (TISSEC), 16, 14, 10.1145/2584679 Bruna, J., Zaremba, W., Szlam, A., LeCun, Y., 2013. Spectral networks and locally connected networks on graphs. arXiv:1312.6203. Bushart, J., Rossow, C., 2019. Padding ain’t enough: Assessing the privacy guarantees of encrypted dns. arXiv:1907.01317. Chang, 2015, Heterogeneous network embedding via deep architectures, 119 Curtin, R.R., Gardner, A.B., Grzonkowski, S., Kleymenov, A., Mosquera, A., 2018. Detecting dga domains with recurrent neural networks and side information. arXiv:1810.02023. Dai, 2018, Learning steady-states of iterative algorithms over graphs, 1114 Defferrard, 2016, Convolutional neural networks on graphs with fast localized spectral filtering, 3844 Developers, G., 2019. Google Safe Browsing. https://developers.google.com/safe-browsing/. [Online]. Dnsdb.info, 2019. DNSDB. https://www.dnsdb.info. [Online]. Dong, 2017, metapath2vec: Scalable representation learning for heterogeneous networks, 135 Eksombatchai, C., Jindal, P., Liu, J.Z., Liu, Y., Sharma, R., Sugnet, C., Ulrich, M., Leskovec, J., 2017. Pixie: A system for recommending 3+ billion items to 200+ million users in real-time. Fan, 2018, Gotcha-sly malware!: Scorpion a metagraph2vec based malware detection system, 253 Fu, 2017, Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning, 1797 Gao, 2018, Large-scale learnable graph convolutional networks, 1416 Gilmer, 2017, Neural message passing for quantum chemistry, 1263 Hamilton, 2017, Inductive representation learning on large graphs, 1024 Hou, 2017, Hindroid: An intelligent android malware detection system based on structured heterogeneous information network, 1507 Huang, Z., Mamoulis, N., 2017. Heterogeneous information network embedding for meta path based proximity. arXiv:1701.05291. for Informatics, S.D.L.C., 2019. DNS-BH - Malware Domain Blocklist by RiskAnalytics. https://dblp.uni-trier.de/. [Online]. Khalil, 2016, Discovering malicious domains through passive dns data graph analysis, 663 Khalil, 2018, A domain is only as good as its buddies: Detecting stealthy malicious domains via graph inference, 330 Kipf, T.N., Welling, M., 2016. Semi-supervised classification with graph convolutional networks. arXiv:1701.05291. Li, 2018, Adaptive graph convolutional neural networks Malwaredomainlist.com, 2019. MDL. https://www.malwaredomainlist.com. [Online]. Malwaredomains.com, 2019. DNS-BH - Malware Domain Blocklist by RiskAnalytics. http://www.malwaredomains.com. [Online]. Manadhata, 2014, Detecting malicious domains via graph inference, 1 Passivedns.cn, 2019. Sign In-passiveDNS. https://passivedns.cn. [Online]. Patsakis, 2019, Encrypted and covert dns queries for botnets: challenges and countermeasures, Computers & Security, 101614 Perozzi, 2014, Deepwalk: Online learning of social representations, 701 Plohmann, 2016, A comprehensive measurement study of domain generating malware, 263 Rahbarinia, 2015, Segugio: Efficient behavior-based tracking of malware-control domains in large isp networks, 403 Schüppen, 2018, {FANCI}: Feature-based automated nxdomain classification and intelligence, 1165 Shang, J., Qu, M., Liu, J., Kaplan, L.M., Han, J., Peng, J., 2016. Meta-path guided embedding for similarity search in large-scale heterogeneous information networks. arXiv:1610.09769. Siby, S., Juarez, M., Diaz, C., Vallina-Rodriguez, N., Troncoso, C., 2019. Encrypted dns–> privacy? a traffic analysis perspective. arXiv:1906.09682. Sidi, L., Nadler, A., Shabtai, A., 2019. Maskdga: A black-box evasion technique against dga classifiers and adversarial defenses. arXiv:1902.08909. Song, 2018, Deepmem: Learning graph neural network models for fast and robust memory forensic analysis, 606 Sun, 2020, Hindom: A robust malicious domain detection system based on heterogeneous information network with transductive classification Sun, 2011, Pathsim: meta path-based top-k similarity search in heterogeneous information networks, Proceedings of the VLDB Endowment, 4, 992, 10.14778/3402707.3402736 Sun, 2009, Ranking-based clustering of heterogeneous information networks with star network schema, 797 Tang, 2015, Pte: Predictive text embedding through large-scale heterogeneous text networks, 1165 Virustotal.com, 2019. Virustotal. https://www.virustotal.com. [Online]. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P. S., 2019. A comprehensive survey on graph neural networks. arXiv:1901.00596. Ye, 2018, Icsd: An automatic system for insecure code snippet detection in stack overflow over heterogeneous information network, 542 Ying, 2018, Graph convolutional neural networks for web-scale recommender systems, 974 Zhang, 2018, Metagraph2vec: Complex semantic path augmented heterogeneous network embedding, 196 Zhang, 2017, Bl-mne: emerging heterogeneous social network embedding through broad learning with aligned autoencoder, 605 Zhauniarovich, 2018, A survey on malicious domains detection through dns data analysis, ACM Computing Surveys (CSUR), 51, 67, 10.1145/3191329 Zou, 2015, Detecting malware based on dns graph mining, Int. J. Distrib. Sens. Netw., 11, 102687