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A Multi-Layered Security Analysis of Blockchain Systems: From Attack Vectors to Defense and System Hardening

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arxiv 2504.09181 v1 pith:K7MGOFUD submitted 2025-04-12 cs.CR

classification cs.CR
keywords blockchainsecuritybitcoinsystemtechnologyapplicationanalyzescurrency
verification ladder T0 review T1 audit T2 compute T3 formal
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The application of Bitcoin enables people to understand blockchain technology gradually. Bitcoin is a decentralized currency that does not rely on third-party credit institutions, and the core of Bitcoin's underlying technology is blockchain. With the increasing value of Bitcoin and the vigorous development of decentralization, people's research on blockchain is also increasing day by day. Today's blockchain technology has not only made great achievements in the application of Bitcoin, but has also been preliminarily applied in other fields, such as finance, medical treatment, the Internet of Things, and so on. However, with the initial application of blockchain technology on the Internet, the security of blockchain technology has also been widely concerned by people in the industry. For example, whether currency trading platforms, smart contracts, blockchain consensus mechanisms, and other technologies are vulnerable to attacks, and how we can defend against these attacks digitally and optimize the blockchain system is exactly the subject we want to study. For the security of appeal blockchain, this paper first analyzes the security threats faced by the application digital currency trading platform of the blockchain system, then analyzes the security problems of smart contract closely related to blockchain 2.0, and then analyzes and studies the security threats of blockchain public chain, consensus mechanism, and P2P. Finally, combined with the security problems at all levels of the blockchain system we analyze and study how to optimize the security of the blockchain system.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Viral to Void: Multi-Dimensional Behavioral and Contractual Analysis for Rug Pull Identification

    cs.CR 2026-08 reject novelty 4.0 of 10

    An MLP with SMOTE and Focal Loss is claimed to detect rug pull tokens with 0.927 accuracy, but the ground truth labels are partly randomly generated.

  2. Ethereum NFT Smart Contracts: Knowledge-Guided Vulnerability Detection with LLM and Code Slicing

    cs.CR 2026-07 conditional novelty 4.0 of 10

    Code slicing plus a knowledge base raises the LLM's positive-label rate from 73.8% to 97.1% on 450 NFT contracts, an effect not validated against ground truth.

  3. Securing High-Concurrency Ticket Sales: A Framework Based on Microservice

    cs.SE 2025-12 reject novelty 2.0 of 10

    A railway ticketing system built from standard Spring Cloud components is reported to reach 817 req/s on a train-query interface, but only under a 100-thread local VM test with inconsistent purchase-interface data.

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