The AV says: Your Hardware Definitions Were Updated!

Botacin, Marcus; Galante, Lucas; Ceschin, Fabrício; Santos, Paulo C.; Carro, Luigi; Geus, Paulo Lício de; Grégio, André; Alves, Marco Antonio Zanata

doi:10.1109/recosoc48741.2019.9034972

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…To overcome the performance barriers of software implementations, many security solutions opt to outsource the processing of ML algorithms to third part components. A frequent approach in the security context is to propose hardware devices to perform critical tasks, among which the processing of ML algorithms [18]. Alternatively, security solutions might also outsource scanning procedures to the cloud.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning (In) Security: A Stream of Problems

Ceschin,

Gomes,

Botacin

et al. 2020

Preprint

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Machine Learning (ML) has been widely applied to cybersecurity, and is currently considered state-of-the-art for solving many of the field´s open issues. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas (at least not in the same way). One of these challenges is the concept drift, that actually creates an arms race between attackers and defenders, given that any attacker may create novel, different threats as time goes by (to overcome defense solutions) and this "evolution´´is not always considered in many works. Due to this type of issue, it is fundamental to know how to correctly build and evaluate a ML-based security solution. In this work, we list, detail, and discuss some of the challenges of applying ML to cybersecurity, including concept drift, concept evolution, delayed labels, and adversarial machine learning. We also show how existing solutions fail and, in some cases, we propose possible solutions to fix them.

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Section: Methodsmentioning

confidence: 99%