Smart Contract Security through AI

Efficient and effective system for auditing smart contracts to determine vulnerabilities. The system extracts raw smart contract data from blockchains, analyzes it using multiple security analyzers, aggregates the findings, and generates a smart contract security report with an explainable score. This allows automated auditing with explainability, integrating into development workflows to catch vulnerabilities early.

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Securely storing and analyzing transaction data using a distributed register and machine learning for dispute resolution. The system allows secure, immutable storage of resource transactions using a distributed register. Disputes can be initiated by users, analyzed using machine learning, and resolutions determined based on historical data. The distributed register prevents unauthorized data manipulation. Users can access verified data via multiple channels.

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Automated system for reviewing contracts and other legal documents to identify potential issues and provide qualitative analysis of document terms. The system uses machine learning to analyze source documents and provide feedback on term favorability, monitor changes over time, and certify compliance. It takes a source document, classifies it based on document type and industry, and uses AI to analyze the terms. This allows rapid, substantive analysis of document provisions without extensive legal expertise. The system can also monitor documents over time to identify changes and generate alerts for significant ones.

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Determining trigger events for blockchain smart contracts using a search engine and knowledge database. The method involves obtaining smart contract data from the blockchain, processing it to determine the trigger event, generating a query based on the event, recursively searching the knowledge database with the query, and determining if the event has occurred based on the search results. This allows leveraging authoritative sources like search engines to determine blockchain smart contract triggers instead of relying solely on blockchain data.

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Detecting anomalies in blockchain transactions using artificial intelligence to identify fraud and other invalid transactions. The method involves extracting graph parameters from block transaction graphs, generating statistical approximations, comparing against thresholds, detecting irregular patterns, identifying anomalies, generating address graphs, and alerting when anomalies are found. A neural network trained to analyze graphs for irregular patterns based on statistical approximations detects anomalies in block transactions over time.

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Real-time identification of anomalies in blockchain using artificial intelligence to detect fraud and invalid transactions. The system extracts block parameters and generates statistical approximations. It compares these approximations to thresholds and detects irregular patterns indicating anomalies. The AI model identifies anomalies within the block and generates alerts when anomalies are found. This allows real-time detection and prevention of fraudulent transactions in blockchains.

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Evaluating transaction requests received by a computer system using a machine learning algorithm to improve accuracy in granting versus declining transactions. The computer system trains a machine learning model using historical transaction requests. When a new request comes in, the model scores it and compares to a threshold. If above, the request is granted, if below, declined. This improves accuracy compared to just using a fixed threshold as the model learns from prior requests. However, wrongly rejecting or granting transactions can degrade performance. To mitigate this, the model is retrained periodically using a subset of recent requests to adapt to changing conditions. This allows updating the model without having to retrain from scratch.

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Smart contract vulnerability detection method that can detect potential issues in smart contracts before they are deployed to the blockchain to prevent losses from loopholes. The method involves constructing a smart contract calling network based on the relationships between contracts, and using that network to predict vulnerabilities. Developer characteristics are also considered as a supplement. The network is modeled from the mutual calling relationships between contracts, and the aggregated feature vector is used for semi-supervised node classification tasks. This allows detection without needing source code, and can be done before deployment.

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System for providing real-time security actions for blockchain operations based on AI models trained on labeled blockchain data. The system receives blockchain data, identifies operations, generates feature inputs based on blockchain characteristics, processes through AI models trained on labeled data, determines security actions, and alerts users of potential risks before execution. The labeled data is from sources and previously processed blocks. This allows customized risk assessment and prevention for new operations.

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Smart contract security detection system using blockchain to identify and mitigate security risks in smart contracts. It analyzes code, transaction records, and access control to comprehensively detect security issues. The system uses techniques like threshold analysis, abnormal pattern detection, and data correlation to analyze transaction patterns, amounts, and frequencies. It also identifies logical errors and data privacy issues. The system integrates the analysis results and generates repair suggestions to help mitigate identified risks.

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Smart contract runtime status monitoring using formal verification to improve the reliability and completeness of monitoring compared to existing methods. The technique combines transaction analysis and code analysis, leveraging formal verification to detect abnormal states in smart contracts. It provides a framework for monitoring smart contract runtime security using formal modeling of transactions and code verification. This reduces developer workload compared to traditional formal verification while improving coverage and completeness compared to transaction and code analysis alone.

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Automatic detection of security threats in blockchain smart contracts using machine learning and anomaly detection to identify abnormal transactions and alert blockchain users. The system monitors transactions on a blockchain, generates risk scores for each transaction based on expected behavior learned from smart contract analysis, and sends alerts if scores fall below thresholds. It uses techniques like signature matching, heuristics, and machine learning models to detect malicious transactions.

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Large model-based smart contract vulnerability detection tool using a fine-tuned GPT model to automatically analyze smart contract code for security issues. The tool trains a GPT language model on open source smart contracts with known vulnerabilities to give it the ability to understand and process smart contract source code. It then uses this trained model to scan new contracts and identify potential vulnerabilities and security risks. The tool generates an evaluation report with detected vulnerabilities, weaknesses, and repair suggestions.

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An interpretable smart contract vulnerability detection and location method using reinforcement learning. The method involves generating a contract graph from the smart contract source code, using a graph neural network to classify contract vulnerabilities, and extracting the subgraphs with the greatest impact on the classification results using reinforcement learning. This allows multi-class vulnerability detection and locating specific code fragments with vulnerabilities, providing interpretable risk positioning results for smart contract vulnerability detection.

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Blockchain smart contract vulnerability detection using hypergraph representation learning. The method involves preprocessing smart contract code, generating a hypergraph representation of the code with nodes representing code elements and edges representing relations between them, extracting features from the hypergraph, learning a classification model on the hypergraph features to detect smart contract vulnerabilities, and finally applying the trained model to new smart contracts for vulnerability detection.

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Smart contract application method for drone clusters that aims to improve drone cluster security by using smart contracts on a blockchain. The method involves detecting potential smart contract vulnerabilities in drone cluster applications like flight data management, collaboration, safety, and certification. It proposes a hybrid neural network model based on attention mechanism to detect small sample smart contract vulnerabilities. This addresses the issue of training deep learning models relying on large datasets when obtaining drone attack samples is difficult. The model is designed to analyze the high-level language, blockchain, and virtual machine levels of smart contracts for vulnerabilities like integer overflow, reentrancy, timing dependency, sequence dependency, and authorization.

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Automatically evolving smart contract endorsement policies in blockchain to adapt and mitigate fraud. The method involves computing historical patterns of fraud attempts from transaction logs, predicting future fraud, correlating historical and predicted fraud, modifying endorsement policies based on correlations, and adding the modified policies to the smart contract. This allows the smart contract to dynamically evolve and adapt its endorsement rules over time based on detected fraud patterns.

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Dynamic vulnerability detection and remediation for smart contracts on blockchain networks using machine learning and virtual sandboxes to proactively identify, analyze, and mitigate potential vulnerabilities in smart contracts. The system leverages machine learning to analyze source code, bytecode, and historical vulnerability data to detect both known and unknown vulnerabilities. Virtual sandboxes provide secure testing environments for controlled execution of smart contracts while monitoring interactions for anomaly detection. Comprehensive reports and automated remediation recommendations empower stakeholders to address security risks.

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Detecting smart contract reentrancy attacks using machine learning to identify potential vulnerabilities in smart contracts that could lead to asset loss due to recursive function calls. The method involves training a reentrancy attack identification model using code snippets and function calls that represent reentrancy attack patterns in smart contract source code. This allows the model to detect similar code segments in new smart contracts to flag potential reentrancy vulnerabilities.

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Decentralized ecosystem for contracting, execution, and policy enforcement in secure zero-trust computing capsules for multi-party environments. The ecosystem allows different entities from different trust domains to accelerate agreement on collaboration terms in many-to-many relationships between participants. It provides a way for participating entities to contract and execute agreements in a decentralized, confidential, and trustless manner. The contracts are executed within the ecosystem using smart contracts, where terms, resources, legislation, and privacy regulations are federated and orchestrated across trust domains. The ecosystem supports features like dispute resolution, resource federation, privacy compliance, and trust domain federation.

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