Deakin University
Browse
yu-surveyonsecurity-2018.pdf (6.34 MB)

A survey on security threats and defensive techniques of machine learning: a data driven view

Download (6.34 MB)
Version 2 2024-06-06, 02:59
Version 1 2018-03-22, 15:19
journal contribution
posted on 2024-06-06, 02:59 authored by Q Liu, P Li, W Zhao, W Cai, S Yu, VCM Leung
OAPA Machine learning is one of the most prevailing techniques in Computer Science, and it has been widely applied in image processing, natural language processing, pattern recognition, cybersecurity and other fields. Regardless of successful applications of machine learning algorithms in many scenarios, e.g., facial recognition, malware detection, automatic driving and intrusion detection, these algorithms and corresponding training data are vulnerable to a variety of security threats, inducing a significant performance decrease. Hence, it is vital to call for further attention regarding security threats and corresponding defensive techniques of machine learning, which motivates a comprehensive survey in this paper. Until now, researchers from academia and industry have found out many security threats against a variety of learning algorithms including Naive Bayes, Logistic Regression, Decision Tree, support vector machine (SVM), principle component analysis, clustering and prevailing deep neural networks. Thus, we revisit existing security threats and give a systematic survey on them from two aspects, the training phase and the testing/inferring phase. After that, we categorize current defensive techniques of machine learning into four groups: security assessment mechanisms, countermeasures in the training phase, those in the testing or inferring phase, data security and privacy. Finally, we provide five notable trends in the research on security threats and defensive techniques of machine learning, which are worth doing in-depth studies in future.

History

Journal

IEEE access

Volume

6

Pagination

12103-12117

Location

Piscataway, N.J.

Open access

  • Yes

eISSN

2169-3536

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2018, IEEE

Publisher

Institute of Electrical and Electronics Engineers