Deep Convolutional Neural Networks for DGA Detection

Autor:	Carlos Catania, Pablo Torres, Sebastián García
Rok vydání:	2019
Předmět:	Computer science business.industry Network security Pattern recognition Communications server Architecture complexity computer.software_genre Convolutional neural network Image (mathematics) Domain (software engineering) Malware False positive rate Artificial intelligence business computer
Zdroj:	Communications in Computer and Information Science ISBN: 9783030207861 CACIC
Popis:	A Domain Generation Algorithm (DGA) is an algorithm to generate domain names in a deterministic but seemly random way. Malware use DGAs to generate the next domain to access the Command & Control (C&C) communication server. Given the simplicity of the generation process and speed at which the domains are generated, a fast and accurate detection method is required. Convolutional neural network (CNN) are well known for performing real-time detection in fields like image and video recognition. Therefore, they seemed suitable for DGA detection. The present work provides an analysis and comparison of the detection performance of a CNN for DGA detection. A CNN with a minimal architecture complexity was evaluated on a dataset with 51 DGA malware families and normal domains. Despite its simple architecture, the resulting CNN model correctly detected more than 97% of total DGA domains with a false positive rate close to 0.7%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fb7589fdd141c5aa4895f7ea3a255905 https://doi.org/10.1007/978-3-030-20787-8_23 Zobrazit plný text záznamu