Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead

Muhammad Shafique; Mahum Naseer; Theocharis Theocharides; Christos Kyrkou; Onur Mutlu; Lois Orosa; Jungwook Choi

doi:10.1109/MDAT.2020.2971217

Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead

Muhammad Shafique, Mahum Naseer, Theocharis Theocharides, Christos Kyrkou, Onur Mutlu, Lois Orosa, Jungwook Choi

Electrical Engineering

Research output: Contribution to journal › Review article › peer-review

Abstract

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical systems (CPS) and Internet-of-Things (IoT). This article discusses various challenges and probable solutions for security attacks on these ML-inspired hardware and software techniques. - Partha Pratim Pande, Washington State University.

Original language	English (US)
Article number	8979377
Pages (from-to)	30-57
Number of pages	28
Journal	IEEE Design and Test
Volume	37
Issue number	2
DOIs	https://doi.org/10.1109/MDAT.2020.2971217
State	Published - Apr 2020

ASJC Scopus subject areas

Software
Hardware and Architecture
Electrical and Electronic Engineering

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10.1109/MDAT.2020.2971217

Cite this

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abstract = "Currently, machine learning (ML) techniques are at the heart of smart cyber-physical systems (CPS) and Internet-of-Things (IoT). This article discusses various challenges and probable solutions for security attacks on these ML-inspired hardware and software techniques. - Partha Pratim Pande, Washington State University.",

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Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead

Abstract

ASJC Scopus subject areas

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