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A Systematic Literature Review on Automated Software Vulnerability Detection Using Machine Learning

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1006/csla.1999.0128openalex-snapshot

An empirical study of smoothing techniques for language modeling

inferredcites →https://doi.org/10.1007/978-3-030-47436-2_13openalex-snapshot

Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection

inferredcites →https://doi.org/10.1007/978-3-030-68110-4_7openalex-snapshot

The Random Neural Network as a Bonding Model for Software Vulnerability Prediction

inferredcites →https://doi.org/10.1007/978-3-319-92624-7_1openalex-snapshot

Review into State of the Art of Vulnerability Assessment using Artificial Intelligence

inferredcites →https://doi.org/10.1007/s10703-005-3401-0openalex-snapshot

Using Static Analysis to Reduce Dynamic Analysis Overhead

inferredcites →https://doi.org/10.1007/s11219-019-09467-0openalex-snapshot

Cross-project bug type prediction based on transfer learning

inferredcites →https://doi.org/10.1016/j.array.2019.100011openalex-snapshot

A survey on vulnerability assessment tools and databases for cloud-based web applications

inferredcites →https://doi.org/10.1016/j.asoc.2018.04.020openalex-snapshot

Mixture of latent multinomial naive Bayes classifier

inferredcites →https://doi.org/10.1016/j.asoc.2024.111556openalex-snapshot

Effective combining source code and opcode for accurate vulnerability detection of smart contracts in edge AI systems

inferredcites →https://doi.org/10.1016/j.comnet.2024.110238openalex-snapshot

DA-GNN: A smart contract vulnerability detection method based on Dual Attention Graph Neural Network

inferredcites →https://doi.org/10.1016/j.compeleceng.2023.108766openalex-snapshot

Smart contract vulnerability detection based on a semantic code structure and a self-designed neural network

inferredcites →https://doi.org/10.1016/j.cose.2021.102286openalex-snapshot

HAN-BSVD: A hierarchical attention network for binary software vulnerability detection

inferredcites →https://doi.org/10.1016/j.cose.2021.102308openalex-snapshot

AutoVAS: An automated vulnerability analysis system with a deep learning approach

inferredcites →https://doi.org/10.1016/j.cose.2021.102417openalex-snapshot

VDSimilar: Vulnerability detection based on code similarity of vulnerabilities and patches

inferredcites →https://doi.org/10.1016/j.cose.2022.102823openalex-snapshot

HyVulDect: A hybrid semantic vulnerability mining system based on graph neural network

inferredcites →https://doi.org/10.1016/j.cose.2022.103017openalex-snapshot

Cross-domain vulnerability detection using graph embedding and domain adaptation

inferredcites →https://doi.org/10.1016/j.cose.2022.103023openalex-snapshot

BinVulDet: Detecting vulnerability in binary program via decompiled pseudo code and BiLSTM-attention

inferredcites →https://doi.org/10.1016/j.cose.2023.103247openalex-snapshot

VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs

inferredcites →https://doi.org/10.1016/j.cose.2023.103341openalex-snapshot

Vulnerability detection through cross-modal feature enhancement and fusion

inferredcites →https://doi.org/10.1016/j.cose.2023.103469openalex-snapshot

SlicedLocator: Code vulnerability locator based on sliced dependence graph

inferredcites →https://doi.org/10.1016/j.cose.2023.103508openalex-snapshot

BinAIV: Semantic-enhanced vulnerability detection for Linux x86 binaries

inferredcites →https://doi.org/10.1016/j.cose.2024.103732openalex-snapshot

VDTriplet: Vulnerability detection with graph semantics using triplet model

inferredcites →https://doi.org/10.1016/j.cose.2024.103787openalex-snapshot

A multi-type vulnerability detection framework with parallel perspective fusion and hierarchical feature enhancement

inferredcites →https://doi.org/10.1016/j.engappai.2024.108296openalex-snapshot

Graph Confident Learning for Software Vulnerability Detection

inferredcites →https://doi.org/10.1016/j.eswa.2023.121764openalex-snapshot

Detect software vulnerabilities with weight biases via graph neural networks

inferredcites →https://doi.org/10.1016/j.eswa.2023.121865openalex-snapshot

Enhancing vulnerability detection via AST decomposition and neural sub-tree encoding

inferredcites →https://doi.org/10.1016/j.future.2022.04.008openalex-snapshot

Proximal Instance Aggregator networks for explainable security vulnerability detection

inferredcites →https://doi.org/10.1016/j.infsof.2015.03.007openalex-snapshot

Guidelines for conducting systematic mapping studies in software engineering: An update

inferredcites →https://doi.org/10.1016/j.infsof.2018.10.001openalex-snapshot

Automatically identifying code features for software defect prediction: Using AST N-grams

inferredcites →https://doi.org/10.1016/j.infsof.2020.106289openalex-snapshot

BVDetector: A program slice-based binary code vulnerability intelligent detection system

inferredcites →https://doi.org/10.1016/j.infsof.2021.106576openalex-snapshot

BGNN4VD: Constructing Bidirectional Graph Neural-Network for Vulnerability Detection

inferredcites →https://doi.org/10.1016/j.infsof.2021.106809openalex-snapshot

VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python

inferredcites →https://doi.org/10.1016/j.infsof.2023.107168openalex-snapshot

SedSVD: Statement-level software vulnerability detection based on Relational Graph Convolutional Network with subgraph embedding

inferredcites →https://doi.org/10.1016/j.infsof.2023.107219openalex-snapshot

HGIVul: Detecting inter-procedural vulnerabilities based on hypergraph convolution

inferredcites →https://doi.org/10.1016/j.infsof.2023.107246openalex-snapshot

A multitype software buffer overflow vulnerability prediction method based on a software graph structure and a self-attentive graph neural network

inferredcites →https://doi.org/10.1016/j.infsof.2023.107290openalex-snapshot

DeKeDVer: A deep learning-based multi-type software vulnerability classification framework using vulnerability description and source code

inferredcites →https://doi.org/10.1016/j.infsof.2023.107328openalex-snapshot

A software vulnerability detection method based on deep learning with complex network analysis and subgraph partition

inferredcites →https://doi.org/10.1016/j.infsof.2023.107371openalex-snapshot

Vulnerability detection based on federated learning

inferredcites →https://doi.org/10.1016/j.infsof.2024.107406openalex-snapshot

Context-based statement-level vulnerability localization

inferredcites →https://doi.org/10.1016/j.infsof.2024.107442openalex-snapshot

MSGVUL: Multi-semantic integration vulnerability detection based on relational graph convolutional neural networks

inferredcites →https://doi.org/10.1016/j.infsof.2024.107453openalex-snapshot

Hybrid semantics-based vulnerability detection incorporating a Temporal Convolutional Network and Self-attention Mechanism

inferredcites →https://doi.org/10.1016/j.ins.2020.11.053openalex-snapshot

Neural software vulnerability analysis using rich intermediate graph representations of programs

inferredcites →https://doi.org/10.1016/j.ins.2023.03.132openalex-snapshot

A novel extended multimodal AI framework towards vulnerability detection in smart contracts

inferredcites →https://doi.org/10.1016/j.jisa.2022.103293openalex-snapshot

Detecting code vulnerabilities by learning from large-scale open source repositories

inferredcites →https://doi.org/10.1016/j.jisa.2023.103423openalex-snapshot

ASSBert: Active and semi-supervised bert for smart contract vulnerability detection

inferredcites →https://doi.org/10.1016/j.jisa.2023.103484openalex-snapshot

SVScanner: Detecting smart contract vulnerabilities via deep semantic extraction

inferredcites →https://doi.org/10.1016/j.jisa.2023.103555openalex-snapshot

Smart contracts vulnerability detection model based on adversarial multi-task learning

inferredcites →https://doi.org/10.1016/j.jss.2018.12.001openalex-snapshot

Fine-grained just-in-time defect prediction

inferredcites →https://doi.org/10.1016/j.jss.2020.110616openalex-snapshot

An automatic software vulnerability classification framework using term frequency-inverse gravity moment and feature selection

inferredcites →https://doi.org/10.1016/j.jss.2022.111550openalex-snapshot

Combine sliced joint graph with graph neural networks for smart contract vulnerability detection

inferredcites →https://doi.org/10.1016/j.jss.2023.111623openalex-snapshot

CSGVD: A deep learning approach combining sequence and graph embedding for source code vulnerability detection

inferredcites →https://doi.org/10.1016/j.jss.2023.111699openalex-snapshot

Optimizing smart contract vulnerability detection via multi-modality code and entropy embedding

inferredcites →https://doi.org/10.1016/j.jss.2023.111705openalex-snapshot

Smart contract vulnerability detection based on semantic graph and residual graph convolutional networks with edge attention

inferredcites →https://doi.org/10.1016/j.jss.2023.111706openalex-snapshot

LCVD: Loop-oriented code vulnerability detection via graph neural network

inferredcites →https://doi.org/10.1016/j.jss.2023.111772openalex-snapshot

BiTCN_DRSN: An effective software vulnerability detection model based on an improved temporal convolutional network

inferredcites →https://doi.org/10.1016/j.jss.2023.111775openalex-snapshot

Vulnerable smart contract function locating based on Multi-Relational Nested Graph Convolutional Network

inferredcites →https://doi.org/10.1016/j.jss.2023.111919openalex-snapshot

Fine-grained smart contract vulnerability detection by heterogeneous code feature learning and automated dataset construction

inferredcites →https://doi.org/10.1016/j.jss.2024.112014openalex-snapshot

Code-centric learning-based just-in-time vulnerability detection

inferredcites →https://doi.org/10.1016/j.jss.2024.112031openalex-snapshot

GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning

inferredcites →https://doi.org/10.1016/j.jss.2024.112039openalex-snapshot

A vulnerability severity prediction method based on bimodal data and multi-task learning

inferredcites →https://doi.org/10.1016/j.knosys.2020.106646openalex-snapshot

Multi independent latent component extension of naive Bayes classifier

inferredcites →https://doi.org/10.1016/j.knosys.2022.108852openalex-snapshot

Just-in-time defect prediction based on AST change embedding

inferredcites →https://doi.org/10.1016/j.neucom.2021.05.043openalex-snapshot

Analysis and modeling conditional mutual dependency of metrics in software defect prediction using latent variables

inferredcites →https://doi.org/10.1038/s41598-024-56871-zopenalex-snapshot

Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction

inferredcites →https://doi.org/10.1049/iet-sen.2020.0084openalex-snapshot

Literature survey of deep learning-based vulnerability analysis on source code

inferredcites →https://doi.org/10.1109/access.2020.3034766openalex-snapshot

Software Vulnerability Analysis and Discovery Using Deep Learning Techniques: A Survey

inferredcites →https://doi.org/10.1109/ase56229.2023.00084openalex-snapshot

Function-Level Vulnerability Detection Through Fusing Multi-Modal Knowledge

inferredcites →https://doi.org/10.1109/ase56229.2023.00181openalex-snapshot

An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair

inferredcites →https://doi.org/10.1109/bigdata47090.2019.9006514openalex-snapshot

Can Machine/Deep Learning Classifiers Detect Zero-Day Malware with High Accuracy?

inferredcites →https://doi.org/10.1109/bigdata50022.2020.9377803openalex-snapshot

Explainable Software vulnerability detection based on Attention-based Bidirectional Recurrent Neural Networks

inferredcites →https://doi.org/10.1109/bigdata59044.2023.10386771openalex-snapshot

SmartDT: An Effective Vulnerability Detection System of Smart Contracts Based on Deep Learning

inferredcites →https://doi.org/10.1109/comst.2018.2885561openalex-snapshot

Data-Driven Cybersecurity Incident Prediction: A Survey

inferredcites →https://doi.org/10.1109/dsaa54385.2022.10032337openalex-snapshot

MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities

inferredcites →https://doi.org/10.1109/dsn53405.2022.00026openalex-snapshot

SEVulDet: A Semantics-Enhanced Learnable Vulnerability Detector

inferredcites →https://doi.org/10.1109/esem.2013.19openalex-snapshot

When a Patch Goes Bad: Exploring the Properties of Vulnerability-Contributing Commits

inferredcites →https://doi.org/10.1109/etncc59188.2023.10284955openalex-snapshot

Multi-Objective Approach for Detecting Vulnerabilities in Ethereum Smart Contracts

inferredcites →https://doi.org/10.1109/glocom.2017.8254428openalex-snapshot

VulDigger: A Just-in-Time and Cost-Aware Tool for Digging Vulnerability-Contributing Changes

inferredcites →https://doi.org/10.1109/iceccs.2019.00011openalex-snapshot

Joint Prediction of Multiple Vulnerability Characteristics Through Multi-Task Learning

inferredcites →https://doi.org/10.1109/iceccs.2019.00012openalex-snapshot

Static Detection of Control-Flow-Related Vulnerabilities Using Graph Embedding

inferredcites →https://doi.org/10.1109/icmla.2018.00120openalex-snapshot

Automated Vulnerability Detection in Source Code Using Deep Representation Learning

inferredcites →https://doi.org/10.1109/icse43902.2021.00040openalex-snapshot

Traceability Transformed: Generating More Accurate Links with Pre-Trained BERT Models

inferredcites →https://doi.org/10.1109/icse48619.2023.00022openalex-snapshot

Data Quality for Software Vulnerability Datasets

inferredcites →https://doi.org/10.1109/icse48619.2023.00088openalex-snapshot

Fine-grained Commit-level Vulnerability Type Prediction by CWE Tree Structure

inferredcites →https://doi.org/10.1109/icse48619.2023.00129openalex-snapshot

Automated Program Repair in the Era of Large Pre-trained Language Models

inferredcites →https://doi.org/10.1109/icse48619.2023.00189openalex-snapshot

DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection

inferredcites →https://doi.org/10.1109/icse48619.2023.00190openalex-snapshot

Enhancing Deep Learning-based Vulnerability Detection by Building Behavior Graph Model

inferredcites →https://doi.org/10.1109/icse48619.2023.00191openalex-snapshot

Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

inferredcites →https://doi.org/10.1109/ictai.2016.56openalex-snapshot

Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016

inferredcites →https://doi.org/10.1109/ijcnn55064.2022.9892280openalex-snapshot

VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection

inferredcites →https://doi.org/10.1109/issre52982.2021.00020openalex-snapshot

GCN2defect : Graph Convolutional Networks for SMOTETomek-based Software Defect Prediction

inferredcites →https://doi.org/10.1109/issre52982.2021.00047openalex-snapshot

Peculiar: Smart Contract Vulnerability Detection Based on Crucial Data Flow Graph and Pre-training Techniques

inferredcites →https://doi.org/10.1109/issre52982.2021.00054openalex-snapshot

Vu1SPG: Vulnerability detection based on slice property graph representation learning

inferredcites →https://doi.org/10.1109/issre59848.2023.00024openalex-snapshot

GraBit: A Sequential Model-Based Framework for Smart Contract Vulnerability Detection

inferredcites →https://doi.org/10.1109/issre59848.2023.00030openalex-snapshot

PSCVFinder: A Prompt-Tuning Based Framework for Smart Contract Vulnerability Detection

inferredcites →https://doi.org/10.1109/issre59848.2023.00042openalex-snapshot

Automatic Static Vulnerability Detection for Machine Learning Libraries: Are We There Yet?

inferredcites →https://doi.org/10.1109/jproc.2020.2993293openalex-snapshot

Software Vulnerability Detection Using Deep Neural Networks: A Survey

inferredcites →https://doi.org/10.1109/mcsoc60832.2023.00053openalex-snapshot

Reentrancy Vulnerability Detection Based on Graph Convolutional Networks and Expert Patterns

inferredcites →https://doi.org/10.1109/msr.2019.00016openalex-snapshot

DeepJIT: An End-to-End Deep Learning Framework for Just-in-Time Defect Prediction

inferredcites →https://doi.org/10.1109/msr59073.2023.00018openalex-snapshot

Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries

inferredcites →https://doi.org/10.1109/msr59073.2023.00052openalex-snapshot

MANDO-HGT: Heterogeneous Graph Transformers for Smart Contract Vulnerability Detection

inferredcites →https://doi.org/10.1109/qrs-c60940.2023.00078openalex-snapshot

Software Vulnerability Detection via Doc2vec with path representations

inferredcites →https://doi.org/10.1109/qrs.2017.42openalex-snapshot

Software Defect Prediction via Convolutional Neural Network

inferredcites →https://doi.org/10.1109/saner53432.2022.00114openalex-snapshot

VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements

inferredcites →https://doi.org/10.1109/seaa51224.2020.00085openalex-snapshot

Using Machine Learning to Identify Code Fragments for Manual Review

inferredcites →https://doi.org/10.1109/sp.2014.44openalex-snapshot

Modeling and Discovering Vulnerabilities with Code Property Graphs

inferredcites →https://doi.org/10.1109/tdsc.2019.2954088openalex-snapshot

Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases

inferredcites →https://doi.org/10.1109/tdsc.2020.2984505openalex-snapshot

CD-VulD: Cross-Domain Vulnerability Discovery Based on Deep Domain Adaptation

inferredcites →https://doi.org/10.1109/tdsc.2021.3051525openalex-snapshot

SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

inferredcites →https://doi.org/10.1109/tdsc.2021.3076142openalex-snapshot

VulDeeLocator: A Deep Learning-Based Fine-Grained Vulnerability Detector

inferredcites →https://doi.org/10.1109/tdsc.2022.3199769openalex-snapshot

mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

inferredcites →https://doi.org/10.1109/tifs.2020.3044773openalex-snapshot

Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection

inferredcites →https://doi.org/10.1109/tifs.2021.3050051openalex-snapshot

Hunting Vulnerable Smart Contracts via Graph Embedding Based Bytecode Matching

inferredcites →https://doi.org/10.1109/tifs.2024.3374219openalex-snapshot

UltraVCS: Ultra-Fine-Grained Variable-Based Code Slicing for Automated Vulnerability Detection

inferredcites →https://doi.org/10.1109/tifs.2024.3392536openalex-snapshot

Vulnerability Detection Based on Enhanced Graph Representation Learning

inferredcites →https://doi.org/10.1109/tii.2018.2821768openalex-snapshot

Cross-Project Transfer Representation Learning for Vulnerable Function Discovery

inferredcites →https://doi.org/10.1109/tkde.2023.3333371openalex-snapshot

Smart Contract Vulnerability Detection Based on Automated Feature Extraction and Feature Interaction

inferredcites →https://doi.org/10.1109/trustcom56396.2022.00046openalex-snapshot

CPGBERT: An Effective Model for Defect Detection by Learning Program Semantics via Code Property Graph

inferredcites →https://doi.org/10.1109/trustcom56396.2022.00070openalex-snapshot

Source Code Vulnerability Detection Using Vulnerability Dependency Representation Graph

inferredcites →https://doi.org/10.1109/tse.2014.2340398openalex-snapshot

Predicting Vulnerable Software Components via Text Mining

inferredcites →https://doi.org/10.1109/tse.2018.2877612openalex-snapshot

Deep Semantic Feature Learning for Software Defect Prediction

inferredcites →https://doi.org/10.1109/tse.2018.2881961openalex-snapshot

Automatic Feature Learning for Predicting Vulnerable Software Components

inferredcites →https://doi.org/10.1109/tse.2021.3087402openalex-snapshot

Deep Learning Based Vulnerability Detection: Are We There Yet?

inferredcites →https://doi.org/10.1109/tse.2022.3140868openalex-snapshot

The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study

inferredcites →https://doi.org/10.1109/tse.2022.3207149openalex-snapshot

Open Science in Software Engineering: A Study on Deep Learning-Based Vulnerability Detection

inferredcites →https://doi.org/10.1109/tse.2023.3285910openalex-snapshot

CPVD: Cross Project Vulnerability Detection Based on Graph Attention Network and Domain Adaptation

inferredcites →https://doi.org/10.1109/tse.2023.3317209openalex-snapshot

VulHunter: Hunting Vulnerable Smart Contracts at EVM Bytecode-Level via Multiple Instance Learning

inferredcites →https://doi.org/10.1109/tse.2023.3340267openalex-snapshot

Meta-Path Based Attentional Graph Learning Model for Vulnerability Detection

inferredcites →https://doi.org/10.1145/1273442.1250746openalex-snapshot

Valgrind

inferredcites →https://doi.org/10.1145/1292414.1292416openalex-snapshot

Random testing for security

inferredcites →https://doi.org/10.1145/1348250.1348254openalex-snapshot

DSD-Crasher

inferredcites →https://doi.org/10.1145/2382196.2382284openalex-snapshot

Before we knew it

inferredcites →https://doi.org/10.1145/2420950.2421003openalex-snapshot

Generalized vulnerability extrapolation using abstract syntax trees

inferredcites →https://doi.org/10.1145/2508859.2516665openalex-snapshot

Chucky

inferredcites →https://doi.org/10.1145/2810103.2813604openalex-snapshot

VCCFinder

inferredcites →https://doi.org/10.1145/2884781.2884804openalex-snapshot

Automatically learning semantic features for defect prediction

inferredcites →https://doi.org/10.1145/3092566openalex-snapshot

Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques

inferredcites →https://doi.org/10.1145/3106237.3117771openalex-snapshot

Automated identification of security issues from commit messages and bug reports

inferredcites →https://doi.org/10.1145/3230833.3230856openalex-snapshot

Discovering software vulnerabilities using data-flow analysis and machine learning

inferredcites →https://doi.org/10.1145/3276517openalex-snapshot

DeepBugs: a learning approach to name-based bug detection

inferredcites →https://doi.org/10.1145/3360588openalex-snapshot

Improving bug detection via context-based code representation learning and attention-based neural networks

inferredcites →https://doi.org/10.1145/3379597.3387461openalex-snapshot

A Machine Learning Approach for Vulnerability Curation

inferredcites →https://doi.org/10.1145/3422622openalex-snapshot

Generative adversarial networks

inferredcites →https://doi.org/10.1145/3429444openalex-snapshot

Interpreting Deep Learning-based Vulnerability Detector Predictions Based on Heuristic Searching

inferredcites →https://doi.org/10.1145/3436877openalex-snapshot

DeepWukong

inferredcites →https://doi.org/10.1145/3457337.3457841openalex-snapshot

Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts

inferredcites →https://doi.org/10.1145/3468264.3468597openalex-snapshot

Vulnerability detection with fine-grained interpretations

inferredcites →https://doi.org/10.1145/3477535openalex-snapshot

On the Reproducibility and Replicability of Deep Learning in Software Engineering

inferredcites →https://doi.org/10.1145/3510003.3510219openalex-snapshot

MVD

inferredcites →https://doi.org/10.1145/3524842.3527949openalex-snapshot

LineVD

inferredcites →https://doi.org/10.1145/3529757openalex-snapshot

A Survey on Data-driven Software Vulnerability Assessment and Prioritization

inferredcites →https://doi.org/10.1145/3533767.3534371openalex-snapshot

Path-sensitive code embedding via contrastive learning for software vulnerability detection

inferredcites →https://doi.org/10.1145/3540250.3549165openalex-snapshot

The best of both worlds: integrating semantic features with expert features for defect prediction and localization

inferredcites →https://doi.org/10.1145/3540250.3558927openalex-snapshot

MANDO-GURU: vulnerability detection for smart contract source code by heterogeneous graph embeddings

inferredcites →https://doi.org/10.1145/3551349.3560428openalex-snapshot

Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach

inferredcites →https://doi.org/10.1145/3585386openalex-snapshot

VulANalyzeR: Explainable Binary Vulnerability Detection with Multi-task Learning and Attentional Graph Convolution

inferredcites →https://doi.org/10.1145/3597503.3623345openalex-snapshot

Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection

inferredcites →https://doi.org/10.1145/3597503.3639117openalex-snapshot

GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis

inferredcites →https://doi.org/10.1145/3611643.3616346openalex-snapshot

Commit-Level, Neural Vulnerability Detection and Assessment

inferredcites →https://doi.org/10.1145/3641846openalex-snapshot

sGuard+: Machine Learning Guided Rule-Based Automated Vulnerability Repair on Smart Contracts

inferredcites →https://doi.org/10.1145/3664602openalex-snapshot

Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection

inferredcites →https://doi.org/10.1162/neco.2006.18.7.1527openalex-snapshot

A Fast Learning Algorithm for Deep Belief Nets

inferredcites →https://doi.org/10.1201/b20091openalex-snapshot

Software Quality Assurance

inferredcites →https://doi.org/10.14722/ndss.2023.23263openalex-snapshot

Smarter Contracts: Detecting Vulnerabilities in Smart Contracts with Deep Transfer Learning

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VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities

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VEST: A System for Vulnerability Exploit Scoring & Timing

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Smart Contract Vulnerability Detection using Graph Neural Network

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Introduction to information retrieval

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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