2 results sorted by ID
Deep Selfish Proposing in Longest-Chain Proof-of-Stake Protocols
Roozbeh Sarenche, Svetla Nikova, Bart Preneel
Attacks and cryptanalysis
It has been shown that the selfish mining attack enables a miner to achieve an unfair relative revenue, posing a threat to the progress of longest-chain blockchains. Although selfish mining is a well-studied attack in the context of Proof-of-Work blockchains, its impact on the longest-chain Proof-of-Stake (LC-PoS) protocols needs yet to be addressed. This paper involves both theoretical and implementation-based approaches to analyze the selfish proposing attack in the LC-PoS protocols. We...
Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis
Jorai Rijsdijk, Lichao Wu, Guilherme Perin, Stjepan Picek
Implementation
Deep learning represents a powerful set of techniques for profiling side-channel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is possible to break targets protected with various countermeasures.
Considering that deep learning techniques commonly have a plethora of hyperparameters to tune, it is clear that such top attack results can come with a high price...
It has been shown that the selfish mining attack enables a miner to achieve an unfair relative revenue, posing a threat to the progress of longest-chain blockchains. Although selfish mining is a well-studied attack in the context of Proof-of-Work blockchains, its impact on the longest-chain Proof-of-Stake (LC-PoS) protocols needs yet to be addressed. This paper involves both theoretical and implementation-based approaches to analyze the selfish proposing attack in the LC-PoS protocols. We...
Deep learning represents a powerful set of techniques for profiling side-channel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is possible to break targets protected with various countermeasures. Considering that deep learning techniques commonly have a plethora of hyperparameters to tune, it is clear that such top attack results can come with a high price...
