Adversarial Vulnerability of Deep Learning Models in Analyzing Next Generation Sequencing Data

Amiel Meiseles, Ishai Rosenberg, Yair Motro, Lior Rokach, Jacob Moran-Gilad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Deep Neural Networks (DNN) can be effectively used to accurately identify infectious pathogens. Unfortunately, DNNs can be exploited by bioterrorists, using adversarial attacks, to stage a fake super-bug outbreak or to hide the extent of a super-bug outbreak. In this work, we show how a DNN that performs superb classification o f c gMLST p rofiles ca n be exploited using adversarial attacks. To this end, we train a novel DNN model, Methicillin Resistance Classification Network (MRCN), which identifies s trains o f t he S taph b acteria t hat are resistant to an antibiotic named methicillin with 93.8 percent accuracy, using Core Genome Multi-Locus Sequence Typing (cgMLST) profiles. To defend a gainst this kind of exploitation, we train a second DNN model, Synthetic Profile Classifier (SPC), which can differentiate between natural Staph bacteria and generic synthetic Staph bacteria with 92.3 percent accuracy. Our experiments show that the MRCN model is highly susceptible to multiple adversarial attacks and that the defenses we propose are not able to provide effective protection against them. As a result, a bioterrorist would be able to utilize the compromised DNN model to inflict immense damage by s taging a fake epidemic or delaying the detection of an epidemic, allowing it to proliferate undeterred.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
PublisherInstitute of Electrical and Electronics Engineers
Pages464-468
Number of pages5
ISBN (Electronic)9781728162157
DOIs
StatePublished - 16 Dec 2020
Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
Duration: 16 Dec 202019 Dec 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/TerritoryKorea, Republic of
CityVirtual, Seoul
Period16/12/2019/12/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Medicine (miscellaneous)
  • Health Informatics

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