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Found 8 papers, 4 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.
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach.
Performance Analysis of Fixed Broadband Wireless Access in mmWave Band in 5G
This work investigates transmission data from fixed broadband wireless access in the mmWave band in 5G.
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis
To address this, an unsupervised auto-encoder-based anomaly detection is presented with an AUC of 0. 987.
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.
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)
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Exploring the Attack Surface of Blockchain: A Systematic Overview
In this paper, we systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains.
Cryptography and Security
Feature-Based Transfer Learning for Network Security
We evaluated the technique on publicly available datasets, and the results demonstrate the effectiveness of transfer learning to detect new network attacks.
