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Found 10 papers, 5 papers with code
Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing
In this context, federated learning (FL)-enabled spectrum sensing technology has garnered wide attention, allowing for the construction of an aggregated ML model without disclosing the private spectrum sensing information of wireless user devices.
Contextual Chart Generation for Cyber Deception
Honeyfiles are a type of honeypot that mimic real, sensitive documents, creating the illusion of the presence of valuable data.
Interpretability and Transparency-Driven Detection and Transformation of Textual Adversarial Examples (IT-DT)
It focuses on interpretability and transparency in detecting and transforming textual adversarial examples.
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models
Existing integrity verification approaches for deep models are designed for private verification (i. e., assuming the service provider is honest, with white-box access to model parameters).
Characterizing Malicious URL Campaigns
Seemingly dissimilar URLs are being used in an organized way to perform phishing attacks and distribute malware.
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things
Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clients or end devices.
DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation
To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6, 000 spam and 2, 313 non-spam image samples.
Can the Multi-Incoming Smart Meter Compressed Streams be Re-Compressed?
Smart meters have currently attracted attention because of their high efficiency and throughput performance.
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
For learning performance, which is specified by the model accuracy and convergence speed metrics, we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data.
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
We observed that the 1D CNN model under split learning can achieve the same accuracy of 98. 9\% like the original (non-split) model.
