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Found 24 papers, 6 papers with code
A Game-Theoretic Approach for PMU Deployment Against False Data Injection Attacks
Phasor Measurement Units (PMUs) are used in the measurement, control and protection of power grids.
Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs
Face image synthesis detection is considerably gaining attention because of the potential negative impact on society that this type of synthetic data brings.
Detecting Face Synthesis Using a Concealed Fusion Model
In this paper, we propose a fusion-based strategy to detect face image synthesis while providing resiliency to several attacks.
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
In this work, we initiate the study of federated synthetic tabular data generation.
The AI Revolution: Opportunities and Challenges for the Finance Sector
A further issue identified in this report is the systemic risk that AI can introduce to the financial sector.
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
SaGess then constructs a synthetic graph using the subgraphs that have been generated by DiGress.
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
We also develop a novel aggregation scheme within the encrypted domain, utilizing users' non-poisoning rates, to effectively address data poisoning attacks while ensuring privacy is preserved by the proposed encryption scheme.
Leveraging Semantic Relationships to Prioritise Indicators of Compromise in Additive Manufacturing Systems
A threat assessment uses similarity measures based on meta-paths and meta-graphs to assess semantic relations among IOCs.
Science and Technology Ontology: A Taxonomy of Emerging Topics
Thus, there needs to be an ontology covering Science and Technology and facilitate multidisciplinary research by connecting topics from different fields and domains that may be related or have commonalities.
Super forecasting the technological singularity risks from artificial intelligence
The article forecasts emerging cyber-risks from the integration of AI in cybersecurity.
Federated Boosted Decision Trees with Differential Privacy
There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data.
Synthetic Data -- what, why and how?
This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy.
Data-Driven Detection and Identification of IoT-Enabled Load-Altering Attacks in Power Grids
Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations.
A Comparison of Data-Driven Techniques for Power Grid Parameter Estimation
Our results show that the SINDy algorithm outperforms the PINN and UKF algorithms in being able to accurately estimate the power grid parameters over a wide range of system parameters (including high and low inertia systems).
On the detection-to-track association for online multi-object tracking
Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT).
FedProf: Selective Federated Learning with Representation Profiling
The results show that the selective behaviour of our algorithm leads to a significant reduction in the number of communication rounds and the amount of time (up to 2. 4x speedup) for the global model to converge and also provides accuracy gain.
Dynamic cyber risk estimation with Competitive Quantile Autoregression
We propose two methods for modelling Value-at-Risk (VaR) which can be used for any time-series data.
A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations
To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach.
Anomaly detection with superexperts under delayed feedback
The increasing connectivity of data and cyber-physical systems has resulted in a growing number of cyber-attacks.
Beyond COVID-19: Network science and sustainable exit strategies
On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc.
Physics and Society
Differentially Private Health Tokens for Estimating COVID-19 Risk
Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0. 03 on average and thus the aggregated results can be useful in a number of identity-free contexts.
Cryptography and Security Computers and Society
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars
This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real time intelligence in edge computing.
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data.
