[go: up one dir, main page]

Ye et al., 2024 - Google Patents

Detecting Unknown Threats in Smart Contracts With Domain Adaptation

Ye et al., 2024

Document ID
7031772859292111438
Author
Ye W
Yang L
Duan L
Li C
Ni W
Publication year
Publication venue
2024 IEEE International Conference on Software Services Engineering (SSE)

External Links

Snippet

Unknown smart contract vulnerabilities objectively exist in addition to common vulnerabilities, and their potential risks cannot be ignored. Therefore, it is crucial to enhance the model's ability to detect these unknown threats. Detection models are typically trained for …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3668—Software testing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
    • G06F21/577—Assessing vulnerabilities and evaluating computer system security
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/562—Static detection
    • G06F21/563—Static detection by source code analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G06N5/025—Extracting rules from data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/40—Transformations of program code
    • G06F8/41—Compilation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Le et al. Deepcva: Automated commit-level vulnerability assessment with deep multi-task learning
Tann et al. Towards safer smart contracts: A sequence learning approach to detecting security threats
Saccente et al. Project achilles: A prototype tool for static method-level vulnerability detection of java source code using a recurrent neural network
Jeon et al. AutoVAS: An automated vulnerability analysis system with a deep learning approach
Zhou et al. Vulnerability analysis of smart contract for blockchain-based IoT applications: A machine learning approach
Narayana et al. Automation and smart materials in detecting smart contracts vulnerabilities in blockchain using deep learning
Shakya et al. Smartmixmodel: machine learning-based vulnerability detection of solidity smart contracts
Wu et al. Code vulnerability detection based on deep sequence and graph models: A survey
Partenza et al. Automatic identification of vulnerable code: Investigations with an ast-based neural network
Wei et al. Toward identifying APT malware through API system calls
Sotgiu et al. Explainability-based debugging of machine learning for vulnerability discovery
Tang et al. Sevuldet: A semantics-enhanced learnable vulnerability detector
Mao et al. Explainable software vulnerability detection based on attention-based bidirectional recurrent neural networks
Gopali et al. Vulnerability detection in smart contracts using deep learning
Jain et al. An integrated deep learning model for Ethereum smart contract vulnerability detection
Duan et al. A new smart contract anomaly detection method by fusing opcode and source code features for blockchain services
EP4435649A1 (en) Apparatus and method for automatically analyzing malicious event log
Xu et al. Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model.
Pujar et al. Analyzing source code vulnerabilities in the D2A dataset with ML ensembles and C-BERT
KR102692350B1 (en) Malicious event log automatic analysis device and method
Imgrund et al. Broken promises: measuring confounding effects in learning-based vulnerability discovery
Liu et al. DL4SC: a novel deep learning-based vulnerability detection framework for smart contracts
Bu et al. Triplet-trained graph transformer with control flow graph for few-shot malware classification
He et al. Enhancing smart contract security: Leveraging pre‐trained language models for advanced vulnerability detection
Lodha et al. SQL injection and its detection using machine learning algorithms and BERT