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Trojan Bio-Hacking of DNA-Sequencing Pipeline

Published: 25 September 2019 Publication History

Abstract

The article focuses on the information security risks that arise from the use of dubious software as part of a DNA-sequencing pipeline. We show how the perpetrator can use a biologically engineered sample that contains the remote machine's IP address and port number to trigger Trojan spyware previously dormant, and create a connection to the remote machine. The spyware is then used to either steal sensitive data processed by the pipeline (e.g. DNA-sample of crime suspect) or manipulate its control-flow (e.g. via opening a backdoor). To avoid detection the spyware can accept and expect required payload in fragments, which are also hidden inside the sample in a distributed manner. We show how the adversary can use cryptographic tools such as encryption and steganography to make such detection even harder while limiting the footprint that either identifies the attacker or makes the trigger-sample substantially different from its biological species. Therefore, we prove the viability of the attack and further stress the need to account for attacks being launched from the physical, rather than cyber-world. Furthermore, DNA sequencing error can hinder the successful delivery of a payload, hence the success of such attacks. We estimate the success rates for different sequencing error rates, where the calculated results are also verified with corresponding results from simulations.

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Cited By

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  • (2022)Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacksScientific Reports10.1038/s41598-022-13700-512:1Online publication date: 10-Jun-2022

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Published In

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NANOCOM '19: Proceedings of the Sixth Annual ACM International Conference on Nanoscale Computing and Communication
September 2019
225 pages
ISBN:9781450368971
DOI:10.1145/3345312
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2019

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Author Tags

  1. Bio-Hacking
  2. DNA-Sequencing Pipeline
  3. Encryption
  4. Steganography

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Science Foundation Ireland through the SFI VistaMilk
  • Science Foundation Ireland through the CONNECT research centres

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NANOCOM '19

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NANOCOM '19 Paper Acceptance Rate 35 of 52 submissions, 67%;
Overall Acceptance Rate 97 of 135 submissions, 72%

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Cited By

View all
  • (2022)Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacksScientific Reports10.1038/s41598-022-13700-512:1Online publication date: 10-Jun-2022

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