Systematic analysis of peer to peer network attacks in blockchain technology examining strategies real world cases and cross layer vulnerabilities
Abstract The peer-to-peer (P2P) network layer, the foundation of the system, tends to be underplayed in blockchain security conversations about headline-grabbing applications and consensus rules. This work digs deep into the P2P layer and discovers it to be a critical front for the integrity of the entire system, not passive plumbing for transactions. The complex realm of P2P attacks can be grouped into two main categories: those designed to destabilize network connections (such as Sybil, Eclipse, and BGP hijacking) and those aimed at destabilizing consensus (such as the infamous 51% attack and selfish mining). The mechanism, resource needs, and potential for pandemonium are utilized to classify each type of attack. To demonstrate that these are not merely hypothetical dangers, real-world catastrophes are analyzed, from the 2014 BGP hijacking of mining pools and the 2015 Bitcoin Eclipse attack to multiple attacks on Ethereum Classic. To better set up the ground to compare, such attacks are cataloged side by side in an easy-to-read comparative matrix. Forward-looking, the topic takes into account future threats, i.e., the growing threat of quantum attacks and the near-term threat of AI-based attacks. It also takes into account the sophisticated defenses needed to repel them, including zero-knowledge proofs and other privacy-enhancing measures. The aim is to put in the spotlight how securing the P2P layer and the expertise to build more secure blockchain systems is critical.
Authors
- Neha Dutta (ORCID: https://orcid.org/0000-0002-3573-9628)
- Kuldeep Kumar
- Vipin Kumar
Institutions
- Chandigarh University (IN)
- Manipal University Jaipur
Publication Details
- Journal
- Discover Computing
- Published
- 2026-10-07
- DOI
- https://doi.org/10.1007/s10791-026-10503-4
- Primary Topic
- Blockchain Technology Applications and Security
- Type
- article
- Field-Weighted Citation Impact
- 0.00