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Found 10 papers, 0 papers with code
A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid
Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors.
Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid
The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller.
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
The existing single prototype-based strategy represents a class by using the mean of the feature space.
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery
First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging
To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO.
Drive Safe: Cognitive-Behavioral Mining for Intelligent Transportation Cyber-Physical System
Further, we develop a prototype of the proposed drive safe platform to establish proof-of-concept (PoC) for the road safety in IT-CPS.
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach
First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy.
Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach
First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem.
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems
In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent.
