Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques

Abstract

In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

“…Meanwhile, the existing literature is also relatively scarce in terms of comparative studies of different feature dimension reduction and data augmentation methods and their technical effectiveness in anomaly detection based on high-dimensional and sparse graph features in the blockchain. ✓ ✓ ✓ Liang et al [15] ✓ ✓ Ashfaq et al [16] ✓ ✓ ✓ Sanjalawe et al [17] ✓ ✓ ✓ Muhammad et al [18] ✓ ✓ Alarab et al [19] ✓ ✓ ✓ Sharma et al [20] ✓ ✓ ✓ Chen et al [21] ✓ ✓ ✓ Pourhabibi et al [22] ✓ Xiao et al [23] ✓ ✓ ✓ Liu et al [24] ✓ ✓ ✓ Alarab et al [25] ✓ ✓ Elbaghdadi et al [26] ✓ ✓ Nerurkar et al [27] ✓ ✓ Mohammed et al [28] ✓ ✓ Voronov et al [29] ✓ ✓ Venkatesan et al [30] ✓ ✓…”

“…Data structures known as sketches, specifically bloom filters and hyperLogLog, have been utilized to identify suspicious accounts without requiring the examination of the entire blockchain data, and methods have been developed to identify accounts with high transaction volume, frequency, and node degree (Voronov et al [29]). Another study highlights how an interplay between blockchain and ML would allow both technologies to assist 130 cybersecurity-related use cases (Venkatesan et al [30]).…”

Harnessing the power of blockchain to strengthen cybersecurity measures: a review

“…The proposed hybrid method power and perfect the proposed consensus protocols' security, trust, and strength. However, they also explored the various ML techniques with hybrid consensus algorithms, such as delegated proof of stake work (DPoSW), proof of stake and work (PoSW), proof of CASBFT (PoCASBFT), delegated byzantine proof of stake (DBPoS) to enhance security [17].…”

“…The selection algorithm uses a combination of a candidate's stake (amount of cryptocurrency held) and other factors such as the age of the coin and randomization to ensure fairness among all nodes in the network. [17] A hybrid consensus algorithm that combines ML techniques to address the challenges and exposures in blockchain networks [18] Many applications of BC Technology for security have been explored by researchers. Such BC Technology serves as the safest platform to avoid because it can improve cybersecurity.…”