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Found 16 papers, 4 papers with code
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
In this work, we call the attack that manipulates several nodes in the DMG concurrently a multi-instance evasion attack.
FairDP: Certified Fairness with Differential Privacy
This paper introduces FairDP, a novel mechanism designed to achieve certified fairness with differential privacy (DP).
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data.
Ten Years after ImageNet: A 360° Perspective on AI
The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.
An Adaptive Black-box Defense against Trojan Attacks (TrojDef)
Through mathematical analysis, we show that if the attacker is perfect in injecting the backdoor, the Trojan infected model will be trained to learn the appropriate prediction confidence bound, which is used to distinguish Trojan and benign inputs under arbitrary perturbations.
How to Backdoor HyperNetwork in Personalized Federated Learning?
This paper explores previously unknown backdoor risks in HyperNet-based personalized federated learning (HyperNetFL) through poisoning attacks.
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples
In this work, we show how to jointly exploit adversarial perturbation and model poisoning vulnerabilities to practically launch a new stealthy attack, dubbed AdvTrojan.
Trojans and Adversarial Examples: A Lethal Combination
In this work, we naturally unify adversarial examples and Trojan backdoors into a new stealthy attack, that is activated only when 1) adversarial perturbation is injected into the input examples and 2) a Trojan backdoor is used to poison the training process simultaneously.
Time-Window Group-Correlation Support vs. Individual Features: A Detection of Abnormal Users
Autoencoder-based anomaly detection methods have been used in identifying anomalous users from large-scale enterprise logs with the assumption that adversarial activities do not follow past habitual patterns.
Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems
We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making.
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses.
BASCPS: How does behavioral decision making impact the security of cyber-physical systems?
We model the security investment decisions made by the defenders as a security game.
Cryptography and Security Computer Science and Game Theory
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples
Single-Step adversarial training methods have been proposed as computationally viable solutions, however they still fail to defend against iterative adversarial examples.
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense
Due to the surprisingly good representation power of complex distributions, neural network (NN) classifiers are widely used in many tasks which include natural language processing, computer vision and cyber security.
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks
Neural Network classifiers have been used successfully in a wide range of applications.
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
Machine learning models, especially neural network (NN) classifiers, are widely used in many applications including natural language processing, computer vision and cybersecurity.
