ENHANCING BLOCKCHAIN SECURITY WITH MACHINE LEARNING: A COMPREHENSIVE STUDY OF ALGORITHMS AND APPLICATIONS

Аннотация

INTRODUCTION explores the strengths and limitations of these Blockchain technology has emerged as a models, providing insights into their applicability revolutionary paradigm in ensuring transparency, for real-world blockchain environments. security, and decentralization across various domains, including finance, healthcare, supply The integration of machine learning into chain, and governance. Its distributed ledger blockchain security has gained significant system, coupled with cryptographic protocols, attention in recent years. Blockchain's inherent offers an immutable and verifiable framework for security features, such as cryptographic hashing recording transactions without the need for and consensus mechanisms, are effective against centralized authorities. Despite its robust traditional cyber threats but are insufficient architecture, blockchain networks are increasingly against evolving, targeted attacks. According to targeted by sophisticated cyberattacks, including Zhuang et al. (2021), machine learning offers a double-spending, Sybil attacks, Distributed Denial promising solution to this challenge by enabling of Service (DDoS), and smart contract systems to identify anomalous patterns and vulnerabilities. These security threats compromise potential threats through data-driven insights. the integrity and trust of blockchain systems, Their study highlighted the role of supervised necessitating advanced mechanisms to safeguard learning algorithms like Random Forest and their operations. Support Vector Machines (SVM) in achieving high accuracy in attack detection. Machine learning, as a subset of artificial intelligence, has demonstrated significant Unsupervised learning algorithms, such as K- potential in cybersecurity, offering dynamic Means and DBSCAN, have also been explored for solutions to identify and mitigate complex attack anomaly detection in blockchain networks. These patterns. By leveraging algorithms capable of models excel in identifying deviations from normal analyzing vast datasets and learning from evolving transaction behaviors, as demonstrated by Zheng threats, machine learning models provide an et al. (2020). However, their high false-positive adaptive and proactive approach to blockchain rates pose a challenge, necessitating further security. This study aims to investigate the efficacy refinement to balance sensitivity and specificity. of various machine learning algorithms, such as Reinforcement learning (RL) has emerged as a supervised learning, unsupervised learning, and game-changing approach in adaptive security reinforcement learning, in addressing the pressing management for blockchain systems. Deep Q- security challenges faced by blockchain systems. Networks (DQN), a subset of RL, have been Through a comparative analysis, the research https://www.theamericanjournals.com/index.php/tajet 151 THE USA JOURNALS THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN – 2689-0984) VOLUME 06 ISSUE12 particularly effective in dynamic threat mitigation. encompassing data collection, preprocessing, Studies by Wang et al. (2022) illustrate the model design, training, implementation, superiority of DQN in handling complex security evaluation, and deployment. Each subsection is scenarios, such as consensus manipulation and elaborated upon to provide a comprehensive smart contract vulnerabilities, by learning optimal framework for addressing the dynamic and responses through iterative interactions with the multifaceted security challenges within blockchain environment. ecosystems. Despite these advancements, the computational DATA COLLECTION AND SOURCES demands of machine learning models remain a The foundation of this study lies in the acquisition significant barrier to their widespread adoption in of high-quality data from diverse and relevant blockchain systems. Research by Kumar et al. sources. Publicly available blockchain transaction (2023) emphasizes the need for lightweight data from platforms such as Ethereum, Bitcoin, and algorithms that maintain high performance while Hyperledger form the primary data corpus. These reducing resource consumption, particularly for platforms provide extensive logs of historical energy-constrained blockchain networks like IoT- transactions, encompassing both legitimate based blockchains. activities and malicious behavior. Transactional Furthermore, integrating explainable AI (XAI) into data includes parameters such as timestamps, blockchain security is becoming a critical area of wallet addresses, hash rates, gas fees, and focus. According to Miller and Johnson (2021), the transaction values, which serve as critical lack of interpretability in machine learning indicators for anomaly detection. decisions hinders stakeholder trust and adoption. Additionally, synthetic datasets are generated to Their work advocates for models that not only simulate a range of attack scenarios, such as deliver high accuracy but also provide transparent double-spendi