SOTAVerified

Vulnerability Detection

Vulnerability detection plays a crucial role in safeguarding against these threats by identifying weaknesses and potential entry points that malicious actors could exploit. Through advanced scanning techniques and penetration testing, vulnerability detection tools meticulously analyze web applications and websites for vulnerabilities such as SQL injection, cross-site scripting (XSS), and insecure authentication mechanisms.

By proactively identifying and addressing vulnerabilities, organizations can strengthen their online security posture and mitigate the risk of data breaches, financial loss, and reputational damage. Additionally, vulnerability detection empowers businesses to stay compliant with industry regulations and standards, demonstrating their commitment to safeguarding sensitive information and maintaining the trust of their customers. With the evolving threat landscape and increasingly sophisticated attack vectors, investing in robust vulnerability detection measures is paramount for staying one step ahead of cyber threats and ensuring the resilience of web-based platforms and services.

Papers

Showing 201216 of 216 papers

TitleStatusHype
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization0
DeFuzz: Deep Learning Guided Directed Fuzzing0
A Comparative Study of Static Code Analysis tools for Vulnerability Detection in C/C++ and JAVA Source Code0
Learning to map source code to software vulnerability using code-as-a-graph0
Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing0
Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks0
μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection0
Forbidden knowledge in machine learning -- Reflections on the limits of research and publication0
AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis ApproachCode0
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection0
SAFE: Self-Attentive Function Embeddings for Binary SimilarityCode0
Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks0
SySeVR: A Framework for Using Deep Learning to Detect Software VulnerabilitiesCode0
Automated Vulnerability Detection in Source Code Using Deep Representation LearningCode0
Automated software vulnerability detection with machine learning0
VulDeePecker: A Deep Learning-Based System for Vulnerability DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Reveal Model - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score26.18Unverified
2Devign Model - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score24.99Unverified
3Reveal Model - Tested on Bigvul (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score18.98Unverified
4Devign Model - Tested on Bigvul (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score18.51Unverified
5LineVul - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans)F1 Score17.38Unverified
6LineVul - Tested on BigVul (Training on Devign + VulScribeR 20K+ Extra Cleans)F1 Score16.23Unverified
#ModelMetricClaimedVerifiedStatus
1WizardCoderAUC0.86Unverified
2ContraBERTAUC0.85Unverified