Recent advances in machine learning have created an opportunity to embed artificial
intelligence in software-intensive systems. These artificial intelligence systems, however …

In light of the wide application of Graph Neural Networks (GNNs), Membership Inference
Attack (MIA) against GNNs raises severe privacy concerns, where training data can be …

The query-based black-box attacks have raised serious threats to machine learning models
in many real applications. In this work, we study a lightweight defense method, dubbed …

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks
that break every ML model, or defenses that withstand most attacks. Unfortunately, little …

Deep learning models are widely used in daily life, which bring great convenience to our
lives, but they are vulnerable to attacks. How to build an attack system with strong …

This work presents a comprehensive exploration of Reverse Engineering of Deceptions
(RED) in the field of adversarial machine learning. It delves into the intricacies of machine …

Network traffic classification is important to intrusion detection and network management.
Most of existing methods are based on machine learning techniques and rely on the …

Recent work has proposed stateful defense models (SDMs) as a compelling strategy to
defend against a black-box attacker who only has query access to the model, as is common …

It has been well recognized that neural network based image classifiers are easily fooled by
images with tiny perturbations crafted by an adversary. There has been a vast volume of …

One intriguing property of adversarial attacks is their “transferability”–an adversarial
example crafted with respect to one deep neural network (DNN) model is often found …