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Machine Learning and Security by David Freeman, Clarence Chio

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Chapter 8. Adversarial Machine Learning

As machine learning begins to be ubiquitously deployed in critical systems, its reliability naturally comes under scrutiny. Although it is important not to be alarmist, the threat that adversarial agents pose to machine learning systems is real. Much like how a hacker might take advantage of a firewall vulnerability to gain access to a web server, a machine learning system can itself be targeted to serve the goals of an attacker. Hence, before putting such solutions in the line of fire, it is crucial to consider their weaknesses and understand how malleable they are under stress.

Adversarial machine learning is the study of machine learning vulnerabilities in adversarial environments. Security and machine learning researchers have published research on practical attacks against machine learning antivirus engines,1 spam filters,2 network intrusion detectors, image classifiers,3 sentiment analyzers,4,5 and more. This has been an increasingly active area of research in recent times, even though such attacks have rarely been observed in the wild. When information security, national sovereignties, and human lives are at stake, machine learning system designers have a responsibility to preempt attacks and build safeguards into these systems.

Vulnerabilities in machine learning systems can arise from flawed system design, fundamental algorithmic limitations, or a combination of both. In this chapter, we examine some vulnerabilities in and attacks ...

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