Optimization Algorithms for Blockchain Network Security Enhancement Using Machine Learning

Thesis Abstract

The increasing adoption of blockchain technology in diverse sectors underscores the necessity for robust security mechanisms to safeguard distributed networks against evolving threats. Despite blockchain’s inherent decentralization and cryptographic features, vulnerabilities such as 51% attacks, Sybil attacks, and smart contract exploits persist, necessitating advanced security solutions that leverage contemporary computational techniques. This study aims to develop and evaluate novel optimization algorithms integrated with machine learning models to enhance blockchain network security, focusing on improving intrusion detection, threat prediction, and network resilience. The specific objectives include designing a hybrid optimization framework that combines genetic algorithms and particle swarm optimization to optimize detection parameters; implementing supervised machine learning classifiers—specifically support vector machines (SVM) and random forests—to identify malicious activities effectively; and assessing the performance of these integrated models through empirical analysis. The research adopts a quantitative, mixed-methods approach centered on experimental simulation and data analysis. The main population comprises blockchain networks and transaction datasets from publicly available platforms such as Bitcoin and Ethereum, totaling approximately 1 million transactions. A stratified sampling technique is employed to select a representative sample of 100,000 transactions, ensuring diverse types of activities, both legitimate and malicious. Data collection involves extracting transaction metadata, network traffic logs, and smart contract interaction patterns, which serve as input for machine learning classifiers. The study utilizes feature engineering techniques to transform raw data into meaningful attributes, followed by model training and validation through K-fold cross-validation to ensure robustness. Optimization algorithms are applied during the feature selection process and parameter tuning phases, with hyperparameter calibration conducted via grid search. The primary analytical techniques include statistical measures such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves to evaluate the classifiers. Additionally, comparative analysis employing analysis of variance (ANOVA) is used to evaluate the performance differences between models. The integration of the optimization algorithms with machine learning models is formalized through a conceptual framework grounded in the Theory of Computational Optimization and the Adaptive Systems Theory. Expected findings predict that the hybrid optimization algorithms will significantly enhance the accuracy and speed of machine learning classifiers in detecting malicious activities within blockchain networks. Specifically, it is anticipated that the optimized SVM and random forest models will outperform baseline models in terms of detection precision by at least 15% and reduce false positive rates by approximately 20%. Results are expected to demonstrate the efficacy of combined genetic and particle swarm optimization techniques in tuning model parameters more efficiently than traditional grid search methods. Furthermore, the study anticipates revealing critical features that contribute to system vulnerabilities, thereby offering insights into network security dynamics. These findings will contribute to the theoretical understanding of integrating evolutionary algorithms with machine learning for cybersecurity applications in decentralized environments. The study’s contribution to knowledge resides in providing a comprehensive framework for implementing and evaluating optimization-driven machine learning models tailored for blockchain security. It bridges gaps identified in prior research regarding the limited application of hybrid optimization techniques in blockchain threat mitigation and offers an empirically validated approach adaptable to various blockchain architectures. The main conclusion underscores the potential of adaptive optimization algorithms to substantially improve blockchain network security, emphasizing their role in proactive threat detection and resilience enhancement. The study recommends further exploration into real-time deployment of these models within blockchain infrastructure, the incorporation of additional machine learning techniques such as deep learning, and investigation into scalability issues associated with larger networks. Future research directions include longitudinal studies to examine the models' long-term effectiveness and the integration of anomaly detection with automated response mechanisms. Overall, this work advances the intersection of optimization algorithms, machine learning, and blockchain security, paving the way for more resilient decentralized systems.