An Optimal Cryptography based Block Chain Security Technique to Avoid Intrusions Block chain Transactions

Abstract

The widespread growth of Ethereum's ecosystem and decentralized finance, especially the burgeoning smart contracts market, has presented an unprecedented opportunity for hackers to exploit malicious and exit scams within the cryptocurrency space as well as various Ponzi schemes through wallet-using phishing attacks. Protocol-level cryptographic mechanisms like Keccak-256 hashing and the Elliptic Curve Digital Signature Algorithm assure transaction validity and structural state integrity, but they are not able to measure semantic intentions and behavioral legitimacy of on-chain activities. This operational shortcoming is addressed in this Article with the introduction of an end to end, computationally efficient machine learning system specifically designed for Ethereum address fraud detection in real-time. The methodology proposed builds a very high-dimensional feature space with 51 engineered attributes reflecting the past dynamics of Ether timing and also technical ratios relative to token interactions, such as the ratio of transactions that balance to zero Ether, or contracts that interact with tokens. An Extremely Randomized Trees Extra Trees ensemble classifier is built from the real-world blockchain data by taking advantage of cost sensitive inverse class frequency weighting without introducing synthetic noise caused by oversampling. Standard baseline classifiers give overall classification Accuracy of 89.76%, Precision of 88.89%, Recall of 88.46%, F1-Score of 88.54% and an ROC-AUC of 0.981, while the results of the extra trees model is 98.42%, 97.85%, 98.10%, 97.97% and 0.988 respectively, which is the best among all of them. After integrating the ERC20 token dynamics into the system, feature ablation analysis demonstrates that the improvement made in terms of F1-Score is 6.77% as compared to traditional ETH-only approaches. Moreover, it only needs 0.85 milliseconds per address, showing its good applicability for such tasks as transaction monitoring in live applications, screening memory pools, and pre-execution wallet security alerts.