DNA Steganalysis Using Deep Recurrent Neural Networks release_o6vvte3turd2rd7643xtkceglu

by Ho Bae, Byunghan Lee, Sunyoung Kwon, Sungroh Yoon

Released as a article .

2018  

Abstract

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods depend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution variations by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools.
In text/plain format

Archived Files and Locations

application/pdf  3.1 MB
file_szt65xjdovgf3pxpzmmxsmhkh4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2018-10-05
Version   v3
Language   en ?
arXiv  1704.08443v3
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: c841ed6e-d181-4901-8e87-20ca7ae0bf27
API URL: JSON