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Found 3 papers, 0 papers with code
Reinforcement learning guided fuzz testing for a browser's HTML rendering engine
Generation-based fuzz testing can uncover various bugs and security vulnerabilities.
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks
This paper reports the results of devising a tiny DNN model, robust to adversarial black and white box attacks, trained with an automatic quantizationaware training framework, i. e. QKeras, with deep quantization loss accounted in the learning loop, thereby making the designed DNNs more accurate for deployment on tiny devices.
mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0
In this paper, we aim to improve ELSA by minimising the lookup table size and summarising the data records by integrating Machine Learning (ML) methods suitable for execution at the edge.
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