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Found 11 papers, 5 papers with code
APBench: A Unified Benchmark for Availability Poisoning Attacks and Defenses
To further evaluate the attack and defense capabilities of these poisoning methods, we have developed a benchmark -- APBench for assessing the efficacy of adversarial poisoning.
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
In this paper, we introduce the UEraser method, which outperforms current defenses against different types of state-of-the-art unlearnable example attacks through a combination of effective data augmentation policies and loss-maximizing adversarial augmentations.
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach
In this paper, a Geometric Graph Alignment (GGA) approach is leveraged to mask the geometric heterogeneities between domains for better intrusion knowledge transfer.
AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models
It can generate UAEs from scratch or conditionally based on reference images.
Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation
Open software supply chain attacks, once successful, can exact heavy costs in mission-critical applications.
Joint Semantic Transfer Network for IoT Intrusion Detection
The scenario semantic endows source NI and II domain with characteristics from each other to ease the knowledge transfer process via a confused domain discriminator and categorical distribution knowledge preservation.
Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection
Integrated sensing and communication (ISAC) represents a paradigm shift, where previously competing wireless transmissions are jointly designed to operate in harmony via the shared use of the hardware platform for improving the spectral and energy efficiencies.
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning
Federated learning (FL) enables edge clients to train collaboratively while preserving individual's data privacy.
Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting
Our main novelties are three aspects: (1) We enrich the spatiotemporal information of model inputs by fusing multi-view features (time, location and traffic states) (2) We build multiple kinds of spatial correlations based on both prior knowledge and data-driven knowledge to improve model performance especially in insufficient or noisy data cases.
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs).
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways.
