@inproceedings{f9be55db9b6a4a93afccbd67064b7bde,
title = "Time series deinterleaving of DNS traffic",
abstract = "Stream deinterleaving is an important problem with various applications in the cybersecurity domain. In this paper, we consider the specific problem of deinterleaving DNS data streams using machine-learning techniques, with the objective of automating the extraction of malware domain sequences. We first develop a generative model for user request generation and DNS stream interleaving. Based on these we evaluate various inference strategies for deinterleaving including augmented HMMs and LSTMs on synthetic datasets. Our results demonstrate that state-of-the-art LSTMs outperform more traditional augmented HMMs in this application domain.",
keywords = "DNS, Deinterleaving, LSTM, Malicious domain detection",
author = "{Asiaee T}, Amir and Hardik Goel and Shalini Ghosh and Vinod Yegneswaran and Arindam Banerjee",
note = "Funding Information: The work was supported in part by NSF grants CNS- 1314560, IIS-1447566, IIS-1447574, IIS-1422557, CCF- 1451986, and IIS-1563950. SG and VY acknowledge partial support from NSF Grant CNS-1314956 and CNS-1514503. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018 ; Conference date: 24-05-2018",
year = "2018",
month = aug,
day = "2",
doi = "10.1109/SPW.2018.00024",
language = "English (US)",
isbn = "9780769563497",
series = "Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "103--108",
booktitle = "Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018",
address = "United States",
}