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Found 16 papers, 3 papers with code
Transfer Learning for Security: Challenges and Future Directions
The latter data may follow a distinct distribution.
Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs
In this paper, we propose Graphene, an advanced system designed to provide a detailed analysis of the security posture of computing infrastructures.
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge
Despite the initial success of current audio backdoor attacks, they suffer from the following limitations: (i) Most of them require sufficient knowledge, which limits their widespread adoption.
Educating for AI Cybersecurity Work and Research: Ethics, Systems Thinking, and Communication Requirements
The present study explored managerial and instructor perceptions of their freshly employed cybersecurity workers' or students' preparedness to work effectively in a changing cybersecurity environment that includes AI tools.
Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report
The emergence of AI tools in cybersecurity creates many opportunities and uncertainties.
TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support
Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns.
Maximal Domain Independent Representations Improve Transfer Learning
We show that information useful only in the source can be present in the DIRep, weakening the quality of the domain adaptation.
Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces.
SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis
5G is the 5th generation cellular network protocol.
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models
Increasing use of machine learning (ML) technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if these ML technologies are introducing leakage of sensitive and proprietary training data.
Simeon -- Secure Federated Machine Learning Through Iterative Filtering
Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of their training data and their hardware.
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols
Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19.
Computers and Society C.3; H.4; J.3; J.7; K.4; K.6.5
Artificial Intelligence at the Edge
The Internet of Things (IoT) and edge computing applications aim to support a variety of societal needs, including the global pandemic situation that the entire world is currently experiencing and responses to natural disasters.
Artificial Intelligence & Cooperation
There is a vital need for research in "AI and Cooperation" that seeks to understand the ways in which systems of AIs and systems of AIs with people can engender cooperative behavior.
Black-box Model Inversion Attribute Inference Attacks on Classification Models
In this paper, we focus on one kind of model inversion attacks, where the adversary knows non-sensitive attributes about instances in the training data and aims to infer the value of a sensitive attribute unknown to the adversary, using oracle access to the target classification model.
FLAP -- A Federated Learning Framework for Attribute-based Access Control Policies
The design of a policy transfer framework has challenges, including policy conflicts and privacy issues.
Cryptography and Security
