https://github.com/dascimal-org/MDSeqVAE/blob/master/VulDeePeck.py
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
SAFE: Self-Attentive Function Embeddings for Binary Similarity
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
SAFE: Self-Attentive Function Embeddings for Binary Similarity
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
SAFE: Self-Attentive Function Embeddings for Binary Similarity
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.