| Title | Adversarial Machine Learning Beyond the Image Domain |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Zizzo, Giulio, Hankin, Chris, Maffeis, Sergio, Jones, Kevin |
| Conference Name | Proceedings of the 56th Annual Design Automation Conference 2019 |
| Date Published | jun |
| Publisher | Association for Computing Machinery |
| Conference Location | Las Vegas, NV, USA |
| ISBN Number | 978-1-4503-6725-7 |
| Keywords | Adversarial Machine Learning, composability, Intrusion detection, machine learning, Neural networks, privacy, pubcrawl, resilience, Resiliency |
| Abstract | 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. |
| DOI | 10.1145/3316781.3323470 |
| Citation Key | zizzo_adversarial_2019 |
Adversarial Machine Learning Beyond the Image Domain