Imperial College London

Professor Emil Lupu

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



e.c.lupu Website




564Huxley BuildingSouth Kensington Campus






BibTex format

author = {Muñoz-González, L and Lupu, EC},
booktitle = {Intelligent Systems Reference Library},
doi = {10.1007/978-3-319-98842-9_3},
pages = {47--79},
title = {The security of machine learning systems},
url = {},
year = {2019}

RIS format (EndNote, RefMan)

AB - © Springer Nature Switzerland AG 2019. Machine learning lies at the core of many modern applications, extracting valuable information from data acquired from numerous sources. It has produced a disruptive change in society, providing new functionality, improved quality of life for users, e.g., through personalization, optimized use of resources, and the automation of many processes. However, machine learning systems can themselves be the targets of attackers, who might gain a significant advantage by exploiting the vulnerabilities of learning algorithms. Such attacks have already been reported in the wild in different application domains. This chapter describes the mechanisms that allow attackers to compromise machine learning systems by injecting malicious data or exploiting the algorithms’ weaknesses and blind spots. Furthermore, mechanisms that can help mitigate the effect of such attacks are also explained, along with the challenges of designing more secure machine learning systems.
AU - Muñoz-González,L
AU - Lupu,EC
DO - 10.1007/978-3-319-98842-9_3
EP - 79
PY - 2019///
SP - 47
TI - The security of machine learning systems
T1 - Intelligent Systems Reference Library
UR -
ER -