Title
Algorithmically Generated Domain Detection and Malware Family Classification
Publication Date
2019
Document Type
Article
Abstract
In this paper, we compare the performance of several machine learning based approaches for the tasks of detecting algorithmically generated malicious domains and the categorization of domains according to their malware family. The datasets used for model comparison were provided by the shared task on Detecting Malicious Domain names (DMD 2018). Our models ranked first for two out of the four test datasets provided in the competition. © Springer Nature Singapore Pte Ltd. 2019.
Publication Title
Communications in Computer and Information Science
Volume
969
First Page
640
Last Page
655
DOI
10.1007/978-981-13-5826-5_50
Publisher Policy
pre print, post print
Recommended Citation
Choudhary, C.; Sivaguru, R.; Pereira, M.; Yu, B.; Nascimento, A.C.; and De Cock, M., "Algorithmically Generated Domain Detection and Malware Family Classification" (2019). School of Engineering and Technology Publications. 334.
https://digitalcommons.tacoma.uw.edu/tech_pub/334
