Botnet DGA Domain Name Classification Using Transformer Network with Hybrid Embedding

Abu Rajab, 2006, A multifaceted approach to understanding the botnet phenomenon, 41 Jianwei, 2008, Research and development of botnets, J. Softw., 19, 702, 10.3724/SP.J.1001.2008.00702 G. Gu, R. Perdisci, J. Zhang, et al., Botminer: Clustering analysis of network traffic for protocol-and structure-independent botnet detection, 2008. Baruah, 2019, Botnet detection: analysis of various techniques, Int. J. Comput. Intel. & IoT, 2 Albanese, 2019, Adaptive cyber defenses for botnet detection and mitigation, 156 Stone-Gross, 2009, Your botnet is my botnet: analysis of a botnet takeover, 635 Herwig, 2019, Measurement and analysis of hajime, a peer-to-peer IoT botnet Singh, 2019, Issues and challenges in DNS based botnet detection: a survey, Comput. Secur., 10.1016/j.cose.2019.05.019 Perdisci, 2009, Detecting malicious flux service networks through passive analysis of recursive DNS traces, 311 Hu, 2011, Measurement and analysis of global IP-usage patterns of fast-flux botnets, 2633 Stone-Gross, 2011, Analysis of a botnet takeover, IEEE Secur. Priv., 9, 64, 10.1109/MSP.2010.144 Leder, 2009, Proactive botnet countermeasures: an offensive approach, vol. 3, 211 Zhou, 2013, DGA-based botnet detection using DNS traffic, J. Internet Serv. Inf. Secur., 3, 116 Antonakakis, 2011, Detecting malware domains at the upper DNS hierarchy, vol. 11, 1 Crawford, 2008, Automatic domain name generation, Softw. Pract. Exp., 38, 1561, 10.1002/spe.885 Schiavoni, 2014, Phoenix: DGA-based botnet tracking and intelligence, 192 Zhang, 2018, An overview on restricted Boltzmann machines, Neurocomputing, 275, 1186, 10.1016/j.neucom.2017.09.065 Zeng, 2019, Single image super-resolution using a polymorphic parallel CNN, Appl. Intell., 49, 292, 10.1007/s10489-018-1270-7 Zhang, 2019, A note on restricted Boltzmann machines and variational autoencoders, Int. J. Collab. Intell., 2, 66 Zhao, 2019, Applications of asynchronous deep reinforcement learning based on dynamic updating weights, Appl. Intell., 49, 581, 10.1007/s10489-018-1296-x Antonakakis, 2010, Building a dynamic reputation system for dns, 273 Bilge, 2011, EXPOSURE: finding malicious domains using passive DNS analysis, 1 Yadav, 2010, Detecting algorithmically generated malicious domain names, 48 Yadav, 2012, Detecting algorithmically generated domain-flux attacks with DNS traffic analysis, IEEE/ACM Trans. Netw., 20, 1663, 10.1109/TNET.2012.2184552 Antonakakis, 2012, From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware, vol. 12, 491 Woodbridge Yu, 2017, Inline DGA detection with deep networks, 683 Yu, 2018, Character level based detection of DGA domain names, 1 Vinayakumar, 2018, Evaluating deep learning approaches to characterize and classify the DGAs at scale, J. Intell. Fuzzy Syst., 34, 1265, 10.3233/JIFS-169423 Mikolov, 2013, Distributed representations of words and phrases and their compositionality, 3111 Zhou Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 360 Netlab OpenData project DGA feeds Vaswani, 2017, Attention is all you need, Adv. Neural Inf. Process. Syst., 5998 Devlin Radford Yang Tong, 2019, D3N: DGA Detection with Deep-Learning Through NXDomain, 10.1007/978-3-030-29551-6_41 Zhou, 2019, CNN-Based DGA Detection with High Coverage, 10.1109/ISI.2019.8823200