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@inproceedings{Zizzo:2019:10.1145/3316781.3323470,
author = {Zizzo, G and Hankin, C and Maffeis, S and Jones, K},
doi = {10.1145/3316781.3323470},
publisher = {ACM Press},
title = {Adversarial machine learning beyond the image domain},
url = {http://dx.doi.org/10.1145/3316781.3323470},
year = {2019}
}

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TY  - CPAPER
AB  - Machine learning systems have had enormous success in a wide range of fields from computer vision, natural language processing, and anomaly detection. However, such systems are vulnerable to attackers who can cause deliberate misclassification by introducing small perturbations. With machine learning systems being proposed for cyber attack detection such attackers are cause for serious concern. Despite this the vast majority of adversarial machine learning security research is focused on the image domain. This work gives a brief overview of adversarial machine learning and machine learning used in cyber attack detection and suggests key differences between the traditional image domain of adversarial machine learning and the cyber domain. Finally we show an adversarial machine learning attack on an industrial control system.
AU  - Zizzo,G
AU  - Hankin,C
AU  - Maffeis,S
AU  - Jones,K
DO  - 10.1145/3316781.3323470
PB  - ACM Press
PY  - 2019///
TI  - Adversarial machine learning beyond the image domain
UR  - http://dx.doi.org/10.1145/3316781.3323470
UR  - http://hdl.handle.net/10044/1/72468
ER  -

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