Výsledky vyhledávání - "Binary code vulnerability detection"

Dyff-VD: Binary Code Vulnerability Detection Based on Dynamic Feature Fusion

Autoři: Qinghao Li Tieming Liu Chunling Liu a další

Zdroj: 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). :59-64

A Stripped Binary Code Vulnerability Detection Method Based on Sensitive Function Identification

Autoři: Binghong Liu Wei Wang Yiming Zhang a další

Zdroj: 2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS). :194-199

Binary Code Vulnerability Detection Based on Multi-Level Feature Fusion

Autoři: Guangli Wu Huili Tang

Zdroj: IEEE Access, Vol 11, Pp 63904-63915 (2023)

Témata: embeddings from language models, feature fusion, instruction level sequence features, Electrical engineering. Electronics. Nuclear engineering, Binary code vulnerability detection, word level sequence features, TK1-9971

Přístupová URL adresa: https://doaj.org/article/878e2d69e04d4b2c8b9a931011935d29

BVSNO: Binary Code Vulnerability Detection Based on Slice Semantic and Node Order

Autoři: Ningning Cui Liwei Chen Gang Shi

Zdroj: 2023 IEEE Symposium on Computers and Communications (ISCC). :373-379

Recurrent Neural Network Based Binary Code Vulnerability Detection

Autoři: Li Mingliang Xin Zhou Jianmin Pang a další

Zdroj: Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. :160-165

Témata: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A Modified Maximal Divergence Sequential Auto-Encoder and Time Delay Neural Network Models for Vulnerable Binary Codes Detection

Autoři: Marwan Ali Albahar

Zdroj: IEEE Access, Vol 8, Pp 14999-15006 (2020)

Témata: new regularization technique, time delay neural network, 0202 electrical engineering, electronic engineering, information engineering, deep learning, NDSS18 binary dataset, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Binary code vulnerability detection, TK1-9971

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/8948470/08955881.pdf
https://doaj.org/article/fd94da2c156e47e5a07396187f99ca7a
https://dblp.uni-trier.de/db/journals/access/access8.html#Albahar20

Recurrent Neural Network Based Binary Code Vulnerability Detection.

Autoři: Zheng, Jianyun Pang, Jianmin Zhang, Xiaochuan a další

Zdroj: ACM International Conference Proceeding Series; 12/20/2019, p160-165, 6p

Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection

Autoři: Li, Litao Ding, Steven H. H. Walenstein, Andrew a další

Témata: Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2404.08562

A lightweight transformer based multi task learning model with dynamic weight allocation for improved vulnerability prediction.

Autoři: Liu, Lan Hui, Zhanfa Chen, Guiming a další

Zdroj: Scientific Reports; 8/1/2025, Vol. 15 Issue 1, p1-17, 17p

Témata: COMPUTER software security, MACHINE learning, ENCODING, TRANSFORMER models, RISK assessment, DATA quality

Dynamic Neural Control Flow Execution: an Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection

Autoři: Litao Li Steven H. H. Ding Andrew Walenstein a další

Zdroj: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. :1215-1225

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/abs/2404.08562

Automating shareable cyber threat intelligence production for closed source software vulnerabilities: a deep learning based detection system.

Autoři: Arıkan, Süleyman Muhammed Koçak, Aynur Alkan, Mustafa

Zdroj: International Journal of Information Security; Oct2024, Vol. 23 Issue 5, p3135-3151, 17p

Témata: COMPUTER security vulnerabilities, MACHINE learning, CYBER intelligence (Computer security), CYBERTERRORISM, RESOURCE management

BinAIV: Semantic-enhanced vulnerability detection for Linux x86 binaries.

Autoři: Gu, Yeming Shu, Hui Kang, Fei

Zdroj: Computers & Security. Dec2023, Vol. 135, pN.PAG-N.PAG. 1p.

Témata: *COMPUTER network security, COMPILERS (Computer programs), BINARY codes

METHODS AND SYSTEMS FOR IDENTIFYING BINARY CODE VULNERABILITY

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

Autoři: LITAO LI DING, STEVEN H. H. YUAN TIAN a další

Zdroj: ACM Transactions on Privacy & Security; Aug2023, Vol. 26 Issue 3, p1-25, 25p

Témata: COMPUTER security vulnerabilities, ARTIFICIAL intelligence, MACHINE learning, CONVOLUTIONAL neural networks, RANSOMWARE

SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks.

Autoři: Zhou, Xu Duan, Bingjie Wu, Xugang a další

Zdroj: Applied Sciences (2076-3417); Feb2023, Vol. 13 Issue 4, p2271, 17p

Témata: LANGUAGE models, ARTIFICIAL intelligence, FORECASTING, COMPUTER software security, COMPUTER assisted instruction

Deep security analysis of program code.

Autoři: Sonnekalb, Tim Heinze, Thomas S. Mäder, Patrick

Zdroj: Empirical Software Engineering; Jan2022, Vol. 27 Issue 1, p1-39, 39p

A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques.

Autoři: Shen, Zhidong Chen, Si

Zdroj: Security & Communication Networks; 9/30/2020, p1-16, 16p

Témata: DEEP learning, CYBERSPACE, OPEN source software, FORECASTING, COMPUTER software, SECURITIES trading

Vestige: Identifying Binary Code Provenance for Vulnerability Detection

Autoři: Yuede Ji Lei Cui H. Howie Huang

Zdroj: Lecture Notes in Computer Science ISBN: 9783030783747

Přístupová URL adresa: https://dblp.uni-trier.de/db/conf/acns/acns2021-2.html#JiCH21
https://link.springer.com/chapter/10.1007/978-3-030-78375-4_12
https://rd.springer.com/chapter/10.1007/978-3-030-78375-4_12

Maximal divergence sequential auto-encoder for binary software vulnerability detection*

Autoři: Le, Tue Nguyen, Tuan Vu Le, Trung a další

Přispěvatelé: Le, Tue Nguyen, Tuan Vu Le, Trung a další

Zdroj: Le, T, Nguyen, T V, Le, T, Phung, D, Montague, P, De Vel, O & Qu, L 2019, Maximal divergence sequential auto-encoder for binary software vulnerability detection*. in A Rush (ed.), International Conference on Learning Representations 2019. International Conference on Learning Representations (ICLR), La Jolla CA USA, International Conference on Learning Representations 2019, New Orleans, Louisiana, United States of America, 6/05/19. < https://openreview.net/pdf?id=ByloIiCqYQ >

Témata: Vulnerabilities Detection, Sequential Auto-Encoder, Separable Representation

Popis souboru: application/pdf

Relation: urn:ISBN:9783800743629

Dostupnost: https://research.monash.edu/en/publications/96914478-198f-4d6e-bbbf-2dd4da326955
https://researchmgt.monash.edu/ws/files/285878054/285712504_oa.pdf
http://www.scopus.com/inward/record.url?scp=85083952462&partnerID=8YFLogxK
https://openreview.net/pdf?id=ByloIiCqYQ

Heuristic Path Pruning Algorithm Based on Error Handling Pattern Recognition in Detecting Vulnerability.

Autoři: Chen, Dong Zhang, Yang Cheng, Liang a další

Zdroj: 2013 IEEE 37th Annual Computer Software & Applications Conference Workshops; 2013, p95-100, 6p