Image de couverture de livre

Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chakraborty, Partha, Arumugam, Krishna Kanth, Alfadel, Mahmoud, Nagappan, Meiyappan, McIntosh, Shane
Format:	Preprint
Publié:	2024
Sujets:	Software Engineering Artificial Intelligence Cryptography and Security Machine Learning D.2; I.2
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

LLbezpeky: Leveraging Large Language Models for Vulnerability Detection
par: Mathews, Noble Saji, et autres
Publié: (2024)

AddressWatcher: Sanitizer-Based Localization of Memory Leak Fixes
par: Murali, Aniruddhan, et autres
Publié: (2024)

Aligning Programming Language and Natural Language: Exploring Design Choices in Multi-Modal Transformer-Based Embedding for Bug Localization
par: Chakraborty, Partha, et autres
Publié: (2024)

FuzzSlice: Pruning False Positives in Static Analysis Warnings Through Function-Level Fuzzing
par: Murali, Aniruddhan, et autres
Publié: (2024)

Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?
par: Asare, Owura, et autres
Publié: (2022)

RLocator: Reinforcement Learning for Bug Localization
par: Chakraborty, Partha, et autres
Publié: (2023)

BLAZE: Cross-Language and Cross-Project Bug Localization via Dynamic Chunking and Hard Example Learning
par: Chakraborty, Partha, et autres
Publié: (2024)

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
par: Chen, Jiachi, et autres
Publié: (2025)

A User-centered Security Evaluation of Copilot
par: Asare, Owura, et autres
Publié: (2023)

NatGVD: Natural Adversarial Example Attack towards Graph-based Vulnerability Detection
par: Rath, Avilash, et autres
Publié: (2025)

Impact of Data Snooping on Deep Learning Models for Locating Vulnerabilities in Lifted Code
par: McCully, Gary A., et autres
Publié: (2024)

SeBERTis: A Framework for Producing Classifiers of Security-Related Issue Reports
par: Masoumzadeh, Sogol, et autres
Publié: (2025)

Variables are a Curse in Software Vulnerability Prediction
par: Groppe, Jinghua, et autres
Publié: (2024)

Provable Repair of Deep Neural Network Defects by Preimage Synthesis and Property Refinement
par: Ma, Jianan, et autres
Publié: (2025)

Machine Learning-Based Detection of MCP Attacks
par: Mattsson, Tobias, et autres
Publié: (2026)

VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
par: Daneshvar, Seyed Shayan, et autres
Publié: (2024)

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
par: Miculicich, Lesly, et autres
Publié: (2025)

Deep Learning for Contextualized NetFlow-Based Network Intrusion Detection: Methods, Data, Evaluation and Deployment
par: Mahdaouy, Abdelkader El, et autres
Publié: (2026)

Automated Vulnerability Detection Using Deep Learning Technique
par: Yang, Guan-Yan, et autres
Publié: (2024)

Comparing Unidirectional, Bidirectional, and Word2vec Models for Discovering Vulnerabilities in Compiled Lifted Code
par: McCully, Gary A., et autres
Publié: (2024)

Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code
par: McCully, Gary A., et autres
Publié: (2024)

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches
par: Paramitha, Ranindya, et autres
Publié: (2025)

A Validated Prompt Bank for Malicious Code Generation: Separating Executable Weapons from Security Knowledge in 1,554 Consensus-Labeled Prompts
par: Young, Richard J., et autres
Publié: (2026)

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
par: Peng, Chenyang, et autres
Publié: (2025)

Continuous Discovery of Vulnerabilities in LLM Serving Systems with Fuzzing
par: Zhao, Yunze, et autres
Publié: (2026)

Structured Extraction of Vulnerabilities in OpenVAS and Tenable WAS Reports Using LLMs
par: Machado, Beatriz, et autres
Publié: (2025)

Eliminating Backdoors in Neural Code Models for Secure Code Understanding
par: Sun, Weisong, et autres
Publié: (2024)

CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities
par: Zhu, Yuxuan, et autres
Publié: (2025)

Adaptive and AI-Augmented Security Testing: A Systematic Survey of Program Analysis, Feedback-Driven Testing, and Hybrid Learning-Based Approaches
par: Wienczkowski, Michael
Publié: (2026)

Multi-Agent Honeypot-Based Request-Response Context Dataset for Improved SQL Injection Detection Performance
par: Yu, Hao, et autres
Publié: (2026)

The Cost of Downgrading Build Systems: A Case Study of Kubernetes
par: Ranjan, Gareema, et autres
Publié: (2025)

Detecting Vulnerabilities in Encrypted Software Code while Ensuring Code Privacy
par: Martins, Jorge, et autres
Publié: (2025)

Orion: Fuzzing Workflow Automation
par: Bazalii, Max, et autres
Publié: (2025)

A Self-Improving Architecture for Dynamic Safety in Large Language Models
par: Slater, Tyler
Publié: (2025)

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
par: Njungle, Nges Brian, et autres
Publié: (2025)

Adaptive alert prioritisation in security operations centres via learning to defer with human feedback
par: Jalalvand, Fatemeh, et autres
Publié: (2025)

Revisiting Vulnerability Patch Identification on Data in the Wild
par: Irsan, Ivana Clairine, et autres
Publié: (2026)

Towards Causal Deep Learning for Vulnerability Detection
par: Rahman, Md Mahbubur, et autres
Publié: (2023)

How Quickly Do Development Teams Update Their Vulnerable Dependencies?
par: Rahman, Imranur, et autres
Publié: (2024)

Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness
par: Sun, Weisong, et autres
Publié: (2025)