Core Concepts
The authors propose new defense systems utilizing learning automata to mitigate risks associated with double-spending and selfish mining attacks in blockchain networks.
Abstract
The content discusses the challenges of double-spending and selfish mining attacks in blockchain-based digital currencies. It introduces a combined attack model and two defense models, SDTLA and WVBM, to enhance security. Experimental results show significant reductions in risks associated with these attacks.
In the introduction, the concept of blockchain, Bitcoin mining, and incentive mechanisms are explained. The paper highlights the potential threats posed by selfish mining and double-spending attacks.
Selfish mining is described as an intentional alteration of the blockchain to increase rewards for miners. Double-spending attacks exploit transaction confirmation delays to spend the same coins multiple times.
Existing defenses against combined attacks are discussed, including uniform tie-breaking methods and timestamp-based approaches. The proposed models aim to address these vulnerabilities effectively.
The SDTLA method increases profitability thresholds for selfish mining, while the WVBM method validates chains based on weight thresholds. Both models demonstrate improved security against double-spending attacks.
Learning automata play a crucial role in updating safe parameters for defense strategies. The evaluation metrics include relative revenue of selfish miners, occurrences of double-spending attacks, profitable thresholds, and upper bounds of revenue for attackers.
Experiments validate the effectiveness of the proposed defense systems in mitigating risks associated with double-spending and selfish mining attacks in blockchain networks.
Stats
Our experimental results show that the SDTLA method increases the profitability threshold of selfish mining up to 47%.
The WVBM method performs even better than SDTLA.
The Z Parameter is tuned to reduce risks of double-spending.
Uniform tie-breaking mechanism increases profit threshold for unfair block rewards within selfish mining strategy to 25%.
Quotes
"The proposed models aim to address vulnerabilities effectively."
"Learning automata play a crucial role in updating safe parameters for defense strategies."