With the continuous promotion and deepened application of Machine Learning-as-a-Service
(MLaaS) across various societal domains, its privacy problems occur frequently and receive …

We show how perturbing inputs to machine learning services (ML-service) deployed in the
cloud can protect against model stealing attacks. In our formulation, there is an ML-service …

The risk of cyber attacks against Industrial Control Systems (ICS) has marked a significant
growth over the past few years. Given ICS has large-scale applications in critical …

Many organizations turn to the promise of artificial intelligence and machine learning (ML)
as its use gains traction in many disciplines. However, developing high-performing ML …

The mere development of a software to predict University dropout is not sufficient for its
effective implementation in the academic context. In order to exploit it as a tool supporting …

Counterfactual explanation aims to identify minimal and meaningful changes required in an
input instance to produce a different prediction from a given model. Counterfactual …

As a result of our heavy reliance on internet usage and running online transactions,
authentication has become a routine part of our daily lives. So, what happens when we lose …

A well-trained deep neural network (DNNs) requires massive computing resources and
data, therefore it belongs to the model owners' Intellectual Property (IP). Recent works have …

Public intelligent services enabled by machine learning algorithms are vulnerable to model
extraction attacks that can steal confidential information of the learning models through …

Deficiency of correctly implemented and robust defence leaves Internet of Things devices
vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial …