Mitigating 51% Attacks in Blockchain Systems Through Early Detection and Checkpoint-Based Defense
cs.CR
Submitted: 2026-09-13
Updated: 2026-09-13
License: http://creativecommons.org/licenses/by/4.0/
The gist: The 51% attack remains one of the significant security concern in Proof-of-Work (PoW) blockchains, where increasing hash-power concentration can create a malicious majority-control risk and enable
Terminology
Abstract
The 51% attack remains one of the significant security concern in Proof-of-Work (PoW) blockchains, where increasing hash-power concentration can create a malicious majority-control risk and enable adversarial chain reorganization. This paper proposes a two-layer defense that combines early hash-power monitoring with checkpoint-based mitigation. The first layer monitors mining-power concentration and provides an early warning before the critical majority-control threshold is reached. The second layer uses checkpointing to restrict the depth of accepted chain reorganizations. Monte Carlo simulations are used to evaluate both mechanisms under different attack scenarios. Across 1,000 simulation runs, a 45% warning threshold provided a mean warning-to-critical lead time of 10.531 minutes before the modeled 50% critical threshold was reached. At the selected checkpoint depth of N = 6, approximately 63.68% of simulated reorganization attempts were rejected, while the mean reorganization-depth outcome decreased from 7.948 to 1.634 blocks, representing an approximately 79.4% reduction. The results demonstrate that early detection and checkpoint-based mitigation provide complementary mechanisms for reducing the potential impact of 51% attacks under the evaluated conditions.
Sources
Related papers
- SoK: AI-Augmented Binary Reversing
- Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
- Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
- Efficient Fuzzy PSI under One-Sided Assumptions
- Sealing the Audit-Runtime Gap for LLM Skills
- Token Composition: A Graph Based on EVM Logs