How AI improves the security of smart contracts
The increasing adoption of smart contracts has changed the way companies and individuals carry out financial transactions. However, one of the important challenges arising from the implementation of smart contracts is their safety. As blockchain technology grows, traditional safety measures, such as cryptography, have been replaced by more advanced methods such as machine training (ML) and artificial intelligence (AI). In this article, we will study how AI improves the security of smart contracts.
What are smart contracts?
Smart contracts are self -executing contracts with the terms of the contract written directly in the ranks of the code. They automate a variety of financial transactions, such as payment processing, ownership transfer and dispute resolution. These contracts use blockchain technology to record any changes made to them in the anterior ledger.
Traditional security measures:
The traditional security measures used in smart contracts are:
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Public Key Cryptography (PKC):
This method uses public and private key pairs for secure data transmission.
- Hash Functions: These algorithms create unique digital signatures, making it difficult to create or change the content of the contract.
3
Random number generators (RNGS): They generate random numbers used in different smart contracts by adding an additional security layer.
However, these traditional events have limitations:
- They may be vulnerable to brutal force attacks on the keys and hashes.
- They need significant computing resources, making them sensitive to the offensive (DOS) attacks.
- They must not provide sufficient protection against the threat of internal information or attacks on social engineering.
AI role in smart contract security:
Artificial intelligence revolutionaries change the way smart contracts are providing. Here are some ways AI improves security:
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Expected Analytics: Machine learning algorithms can analyze a large amount of data to predict possible vulnerabilities and determine anomalies by reducing the risk of hacking attempts.
- Automated threat determination: AI -powered systems can identify and perform suspicious action in real -time, helping to prevent attacks before they occurred.
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Detection of anomaly: Advanced Analytics can identify unusual patterns or behavior that may indicate a security violation, allowing rapid action.
- Safe Key Management: On AI -based key management systems use machine learning algorithms to secure, store and manage keys to use vulnerabilities.
How AI improves the security of smart contracts:
- Improved Data Protection: AI -powered data protection systems can encrypt sensitive information by protecting it from unauthorized access.
- Improved Audit: Additional audit capabilities allow real -time monitoring and verifying smart contract operations, ensuring compliance with regulatory requirements.
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Estimated Maintenance: AI -led maintenance helps identify possible safety threats before they occur by reducing the risk of violations.
Real world examples:
Several companies are already using AI to improve the security of their smart contracts:
- Cryptocurrency Exchange: Businesses such as binance and coinbase use AI -powered threat -determination systems to prevent hacking attempts.
- Digital identity platforms: Organizations such as IBM and Microsoft are developing AI -based identity verification solutions to protect the sensitive data of users.
Conclusion:
Ai transforms the way we provide smart contracts by offering unparalleled protection against various threats.