# New intelligent defense systems to reduce the risks of Selfish Mining and Double-Spending attacks using Learning Automata

arXiv: https://arxiv.org/abs/2307.00529 · HTML: https://mlsanity.com/paper/2307.00529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Optimal Selfish Mining Strategies in Bitcoin](https://mlsanity.com/paper/1507.06183/) (2015)
- [Countering Selfish Mining in Blockchains](https://mlsanity.com/paper/1811.09943/) (2018)
- [Nik Defense: An Artificial Intelligence Based Defense Mechanism against Selfish Mining in Bitcoin](https://mlsanity.com/paper/2301.11463/) (2023)
- [VDHLA: Variable Depth Hybrid Learning Automaton and Its Application to Defense Against the Selfish Mining Attack in Bitcoin](https://mlsanity.com/paper/2302.12096/) (2023)

## Abstract (arXiv)

In this paper, we address the critical challenges of double-spending and selfish mining attacks in blockchain-based digital currencies. Double-spending is a problem where the same tender is spent multiple times during a digital currency transaction, while selfish mining is an intentional alteration of a blockchain to increase rewards to one miner or a group of miners. We introduce a new attack that combines both these attacks and propose a machine learning-based solution to mitigate the risks associated with them. Specifically, we use the learning automaton, a powerful online learning method, to develop two models, namely the SDTLA and WVBM, which can effectively defend against selfish mining attacks. Our experimental results show that the SDTLA method increases the profitability threshold of selfish mining up to 47%, while the WVBM method performs even better and is very close to the ideal situation where each miner's revenue is proportional to their shared hash processing power. Additionally, we demonstrate that both methods can effectively reduce the risks of double-spending by tuning the Z Parameter. Our findings highlight the potential of SDTLA and WVBM as promising solutions for enhancing the security and efficiency of blockchain networks.