An Efficient Approach Based on RAE-GAMI-NET for Long Range Attack Detection on Blockchain

Abstract

Blockchain is a prominent and leading decentralized ledger technology that has gained global attention and adoption across various industries. Long-range attacks (LRAs) are when an adversary attempts to rewrite the blockchain’s history from a point far back in time. Since PoS Blockchain relies on validators’ stakes as a form of security, LRAs can potentially undermine the network’s security if not detected and prevented. In order to protect against long-range attacks, this research suggests a high-performance explainable neural network model that can accurately categorize nodes as malicious or non-malicious while maintaining interpretability. The proposed explainable neural network model includes Residual Auto Encoder (RAE) guided generalized additive models with incorporating structured interactions (RAE-GAMI-Net) for LRA detection in PoS Blockchain In this work, a wrapper-based Binary Orchard Algorithm (W-BOA) is used to find the best features to lessen the dimensionality of extracted Characteristics, and a global feature extraction has been implemented based on multi-scale Densenet (MDensenet) that assures early convergence and optimal performance by providing global optimal solution. Then, the transformed features are used to train the RAE-GAMI-Net-based model to detect the LR attack. The included RAE learns a compressed representation (latent) of the input features. Then, the latent features are classified with GAMI-Net, balancing the model interpretability and accuracy. The effectiveness of our proposed method is assessed using the Proof of Stake blockchain dataset and benchmarked against other deep learning techniques. Our approach yields significant enhancements in accuracy (0.962), precision (0.9614), and recall 0.9604, accompanied by a notably low Brier score of 0.038.