In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

The language models, especially the basic text classification models, have been shown to
be susceptible to textual adversarial attacks such as synonym substitution and word …

We present Pasado, a technique for synthesizing precise static analyzers for Automatic
Differentiation. Our technique allows one to automatically construct a static analyzer …

The widespread adoption of the Android operating system has made malicious Android
applications an appealing target for attackers. Machine learning-based (ML-based) Android …

Deep learning (DL) performs well in many traffic analysis tasks. Nevertheless, the
vulnerability of deep learning weakens the real-world performance of these traffic analyzers …