Using honeypots in a decentralized framework to defend against adversarial machine-learning attacks

The market demand for online machine-learning services is increasing, and so to have are the threats to them. Adversarial inputs represent a new threat to Machine-Learningas- a-Services (MLaaSs). Meticulously crafted malicious inputs can be used to mislead and confuse the learning model, even in cases where the adversary only has access to input and output labels. As a result, there has been increased interest in defence techniques to combat these types of attacks. In this thesis, we propose a network of high-interaction honeypots as a decentralized defence framework that prevents an adversary from corrupting the learning model, primarily through the use of deception. We accomplish our aim by 1) preventing the attacker from correctly learning the labels and approximating the architecture of the black-box system; 2) luring the attacker away, towards a decoy model, using HoneyTokens; and 3) creating infeasible computational work for the adversary.