This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …

Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …

A membership inference attack allows an adversary to query a trained machine learning
model to predict whether or not a particular example was contained in the model's training …

Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern
exploiting training (PET), have achieved impressive results in label-scarce settings …

Recently, NLP has seen a surge in the usage of large pre-trained models. Users download
weights of models pre-trained on large datasets, then fine-tune the weights on a task of their …

As data becomes the fuel driving technological and economic growth, a fundamental
challenge is how to quantify the value of data in algorithmic predictions and decisions. For …

Removing information from a machine learning model is a non-trivial task that requires to
partially revert the training process. This task is unavoidable when sensitive data, such as …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

Although various techniques have been proposed to generate adversarial samples for white-
box attacks on text, little attention has been paid to a black-box attack, which is a more …

How can we explain the predictions of a black-box model? In this paper, we use influence
functions—a classic technique from robust statistics—to trace a model's prediction through …