We present a new algorithm to train a robust malware detector. Malware is a prolific problem
and malware detectors are a front-line defense. Modern detectors rely on machine learning …

Research in adversarial learning has primarily focused on homogeneous unstructured
datasets, which often map into the problem space naturally. Inverting a feature space attack …

The generation of feasible adversarial examples is necessary for properly assessing models
that work in constrained feature space. However, it remains a challenging task to enforce …

Randomized smoothing is the current state-of-the-art method for producing provably robust
classifiers. While randomized smoothing typically yields robust $\ell_2 $-ball certificates …

Deep neural network (DNN)-based applications are extensively being researched and
applied in the Internet of Things (IoT) devices in daily lives due to impressive performance …

The vulnerability of machine learning-based malware detectors to adversarial attacks has
prompted the need for robust solutions. Adversarial training is an effective method but is …

This work delves into the complexities of machine unlearning in the face of distributional
shifts, particularly focusing on the challenges posed by non-uniform feature and label …

In this discussion paper, we survey recent research surrounding robustness of machine
learning models. As learning algorithms become increasingly more popular in data-driven …

In the literature, it is well explored that machine learning algorithms trained on image
classes are highly vulnerable against adversarial examples. However, very limited attention …

Neural networks are successful in various applications but are also susceptible to
adversarial attacks. To show the safety of network classifiers, many verifiers have been …