Title:

MACHINE LEARNING APPROACHES TO DETECT SYBIL ATTACKS AND NETWORK MANIPULATION IN BLOCKCHAIN

Author:

Priyesha Dnyaneshwar Gharat and Dr. Lalit Kumar Khatri

Abstract:

Blockchain networks are susceptible to Sybil attacks and other network manipulations, which threaten consensus and integrity. Traditional security mechanisms alone are insufficient for detecting such attacks. This review examines machine learning (ML) techniques for identifying malicious nodes and anomalous behaviors in blockchain networks. Both supervised and unsupervised approaches, including hybrid and graph-based methods, are discussed. A comparative summary highlights datasets, methodologies, and detection performance, providing insights into research gaps and future directions.

Keyword:

Blockchain security, Sybil attack detection, Network manipulation.