Word encoding for word-looking DGA-based Botnet classification

Sea Ran Cleon Liew, Ngai Fong Law

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

There are two main types of domain name-generating algorithms (DGAs) - random-looking and word-looking. While existing methods can effectively distinguish between the two types of DGAs with high accuracy, classifying different types of word-looking DGAs has proven to be challenging, as they are often mistaken for legitimate domains. To address this issue, previous methods used character encoding with long short-term memory networks (LSTM) or convolutional neural networks (CNN) to model the character distribution of different word-looking DGAs. Since most word-looking DGAs are constructed using various dictionaries, we propose using word encoding instead of character encoding. Word encoding can provide a better characterization as it is based on the usage of different words in the dictionaries and their associations. Experimental results show that the classification accuracy for word-based DGAs increases by more than 7% (from 87% to 94%) using word encoding as compared to character encoding.

Original languageEnglish
Title of host publication2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1816-1821
Number of pages6
ISBN (Electronic)9798350300673
DOIs
Publication statusPublished - Nov 2023
Event2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan
Duration: 31 Oct 20233 Nov 2023

Publication series

Name2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Country/TerritoryTaiwan
CityTaipei
Period31/10/233/11/23

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Artificial Intelligence
  • Computer Science Applications

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