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Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks
This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the pe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9186480/ https://www.ncbi.nlm.nih.gov/pubmed/35688914 http://dx.doi.org/10.1038/s41598-022-13700-5 |
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author | Islam, M. S. Ivanov, S. Awan, H. Drohan, J. Balasubramaniam, S. Coffey, L. Kidambi, S. Sri-saan, W. |
author_facet | Islam, M. S. Ivanov, S. Awan, H. Drohan, J. Balasubramaniam, S. Coffey, L. Kidambi, S. Sri-saan, W. |
author_sort | Islam, M. S. |
collection | PubMed |
description | This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator’s machine in the DNA. Genetic analysis of the sample’s DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from Trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment. |
format | Online Article Text |
id | pubmed-9186480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91864802022-06-10 Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks Islam, M. S. Ivanov, S. Awan, H. Drohan, J. Balasubramaniam, S. Coffey, L. Kidambi, S. Sri-saan, W. Sci Rep Article This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator’s machine in the DNA. Genetic analysis of the sample’s DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from Trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment. Nature Publishing Group UK 2022-06-10 /pmc/articles/PMC9186480/ /pubmed/35688914 http://dx.doi.org/10.1038/s41598-022-13700-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Islam, M. S. Ivanov, S. Awan, H. Drohan, J. Balasubramaniam, S. Coffey, L. Kidambi, S. Sri-saan, W. Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks |
title | Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks |
title_full | Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks |
title_fullStr | Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks |
title_full_unstemmed | Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks |
title_short | Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks |
title_sort | using deep learning to detect digitally encoded dna trigger for trojan malware in bio-cyber attacks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9186480/ https://www.ncbi.nlm.nih.gov/pubmed/35688914 http://dx.doi.org/10.1038/s41598-022-13700-5 |
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