Public and academic discourse on the safety of conversational agents using generative AI, particularly chatbots, often centers on fairness, trust, and risk. However, there is limited insight into how users differentiate these perceptions and what …
Machine-learning (ML) classifiers are increasingly used to distinguish malware from benign binaries. Recent work has shown that ML-based detectors can be evaded by adversarial examples, but also that one may defend against such attacks via …
Psychometric security scales can enable various crucial tasks (e.g., measuring changes in user behavior over time), but, unfortunately, they often fail to accurately predict actual user behavior. We hypothesize that one can enhance prediction …
This work presents CaFA, a system for Cost-aware Feasible Attacks for assessing the robustness of neural tabular classifiers against adversarial examples realizable in the problem space, while minimizing adversaries’ effort. To this end, CaFA …
The increasing complexity of attacks has given rise to varied security applications tackling profound tasks, ranging from alert triage to attack reconstruction. Yet, security products, such as Endpoint Detection and Response, bring together …
Machine-learning models are known to be vulnerable to evasion attacks that perturb model inputs to induce misclassifications. In this work, we identify real-world scenarios where the true threat cannot be assessed accurately by existing attacks. …
Prior work showed that face-recognition systems ingesting RGB images captured via visible-light (VIS) cameras are susceptible to real-world evasion attacks. Face-recognition systems in near-infrared (NIR) are widely deployed for critical tasks (e.g., …
Machine learning (ML) models have shown promise in classifying raw executable files (binaries) as malicious or benign with high accuracy. This has led to the increasing influence of ML-based classification methods in academic and real-world malware …
Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics. Despite their impressive performance, concerns remain over whether these GNN-based job schedulers …
Social bots—software agents controlling accounts on online social networks (OSNs)—have been employed for various malicious purposes, including spreading disinformation and scams. Understanding user perceptions of bots and ability to distinguish them …