PREVENTING SYBIL ATTACKS IN BLOCKCHAIN USING DISTRIBUTED BEHAVIOR MONITORING AND INTEGRITY VALIDATION OF MINERS

The swift expansion of blockchain technology has underscored the critical necessity of safeguarding decentralized networks against Sybil attacks, wherein malicious actors compromise network integrity by establishing a multitude of fraudulent nodes. This study introduces an innovative methodology for alleviating Sybil attacks within blockchain networks via a distributed behavior monitoring framework tailored for miners, in conjunction with augmented data integrity validation mechanisms. Within this system, the conduct of each miner is perpetually observed and documented, with suspicious behaviors discerned based on deviations from normative patterns in mining activities. A simulation of a Sybil attack further illustrates the system's proficiency in recognizing irregularities in miner conduct. To guarantee the dependability and veracity of mined blocks, we incorporate an integrity verification layer that utilizes digital signatures alongside Merkle tree configurations. This enhancement not only amplifies the accountability of miners but also fortifies the blockchain's defenses against tampering and nefarious alterations. Our research indicates that the amalgamation of behavioral surveillance with integrity verification markedly enhances the precision of identifying Sybil-like behaviors, thereby offering a formidable strategy for safeguarding decentralized systems from internal threats.