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Found 10 papers, 0 papers with code
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface
Whilst a legitimate RIS can yield beneficial impacts including increased channel randomness to enhance physical layer secret key generation (PL-SKG), malicious RIS can poison legitimate channels and crack most of existing PL-SKGs.
Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning
This paper presents a Federated multi-agent Deep reinforcement learning-assisted Distributed Key generation scheme (FD2K), which fully exploits the common features of physical dynamics to establish secret key between legitimate users.
Adversarial Reconfigurable Intelligent Surface Against Physical Layer Key Generation
The development of reconfigurable intelligent surfaces (RIS) has recently advanced the research of physical layer security (PLS).
Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process
The causal mechanism between climate and political violence is fraught with complex mechanisms.
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space
We believe that this approach of exploiting general data distribution knowledge form neural networks can be applied to a wide range of scarce data open challenges.
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions
In this paper, we achieve partially explainable learning model by symbolically explaining the role of activation functions (AF) under a scalable topology.
Graph Layer Security: Encrypting Information via Common Networked Physics
Leveraging this, we propose for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption.
Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics
Infrastructure monitoring is critical for safe operations and sustainability.
High-dimensional Metric Combining for Non-coherent Molecular Signal Detection
If the channel is unknown, we cannot easily achieve traditional coherent channel estimation and cancellation, and the impact of ISI will be more severe.
Sequential Bayesian Detection of Spike Activities from Fluorescence Observations
Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work.
