Suchergebnisse - buffer-overflow vulnerability detection

Binary Static Analysis-based Buffer Overflow Vulnerability Detection Technique using Function Prologue and Epilogue

Autoren: Dongsoo Kim Taiho Kim Sangyub Kim et al.

Quelle: KIISE Transactions on Computing Practices. 30:531-536

PwnMaster: Automatic Buffer Overflow and Format String Vulnerability Detection and Exploitation

Autoren: Florin Petrean Adrian Coleşa

Quelle: 2024 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). :1-5

Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks

Autoren: Mst. Shapna Akter Hossain Shahriar Juan Rodriguez Cardenas et al.

Quelle: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). :765-776

Schlagwörter: Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Software Engineering, Computer Science - Cryptography and Security, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2306.07981
https://hdl.handle.net/20.500.11770/379017
https://doi.org/10.1109/COMPSAC57700.2023.00106

TEDVIL: Leveraging Transformer-Based Embeddings for Vulnerability Detection in Lifted Code

Autoren: Gary A. McCully John D. Hastings Shengjie Xu

Quelle: IEEE Access, Vol 13, Pp 76894-76913 (2025)

Schlagwörter: Bidirectional encoder, transformer-based embedding, machine learning, neural network, binary security, Electrical engineering. Electronics. Nuclear engineering, buffer overflow, TK1-9971

Zugangs-URL: https://doaj.org/article/e717df07d2404d6f9713bfc8ba83d3cb

Buffer Overflow Vulnerability Detection Based on Static Analysis-assisted Symbolic Execution

Autoren: Wenhui Wang PEngland Zhwng Guichen Wei et al.

Quelle: 2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). :546-550

BovdGFE: buffer overflow vulnerability detection based on graph feature extraction

Autoren: Xinghang Lv Tao Peng Jia Chen et al.

Quelle: Applied Intelligence. 53:15204-15221

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

BofAEG: Automated Stack Buffer Overflow Vulnerability Detection and Exploit Generation Based on Symbolic Execution and Dynamic Analysis

Autoren: Shenglin Xu Yongjun Wang

Quelle: Security and Communication Networks. 2022:1-9

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

Dateibeschreibung: text/xhtml

Association Rules for Buffer Overflow Vulnerability Detection Using Machine Learning

Autoren: Intisar Al-Mandhari Ahmed AlKalbani Abdulaziz Al-Abri

Quelle: Lecture Notes in Networks and Systems ISBN: 9789819932351

Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks

Autoren: Akter, Mst Shapna Shahriar, Hossain Cardenas, Juan Rodriguez et al.

Index Begriffe: Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Software Engineering, text

URL: http://arxiv.org/abs/2306.07981

Characterizing Buffer Overflow Vulnerabilities in Large C/C++ Projects

Autoren: Jose D'Abruzzo Pereira Naghmeh Ivaki Marco Vieira

Quelle: IEEE Access. 9:142879-142892

Schlagwörter: orthogonal defect classification (ODC), Software security, vulnerability detection, 0202 electrical engineering, electronic engineering, information engineering, buffer over flow, software metrics, 02 engineering and technology, static code analysis

Zugangs-URL: https://ieeexplore.ieee.org/document/9576064
https://dblp.uni-trier.de/db/journals/access/access9.html#PereiraIV21
https://hdl.handle.net/10316/101176

Research on buffer overflow vulnerability detection model based on Bi-GRU and attention mechanism

Autoren: Jiali Luo Deguo Yang Zhaoyong Yang

Quelle: Second International Conference on Informatics, Networking, and Computing (ICINC 2023). :20

Source Code Instrumentation Technique for Buffer Overflow Vulnerability Detection

Autoren: Young-Su Jang

Quelle: The Journal of Korean Institute of Information Technology. 17:133-144

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

Zugangs-URL: https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002505538
http://ki-it.com/_common/do.php?a=full&b=22&bidx=1695&aidx=21047

A Detection Approach for Buffer Overflow Vulnerability Based on Data Control Flow Graph

Autoren: Qingchen Zhang Qihao Bao Patrick Kwaku Kudjo et al.

Quelle: Communications in Computer and Information Science ISBN: 9789811534171

Zugangs-URL: https://rd.springer.com/chapter/10.1007/978-981-15-3418-8_20
https://link.springer.com/chapter/10.1007/978-981-15-3418-8_20

Accelerating firmware vulnerability detection through directed reaching definition analysis

Autoren: Kai Chen Yufei Zhao Jing Guo et al.

Quelle: ICT Express, Vol 11, Iss 5, Pp 951-956 (2025)

Schlagwörter: Directed fuzz, Reaching definition, Static analysis, Firmware vulnerability detection, Information technology, T58.5-58.64

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2405959525000700; https://doaj.org/toc/2405-9595

Zugangs-URL: https://doaj.org/article/408369dd115547e6ae42eff0bfe3c2df

A Light-Weight and Accurate Method of Static Integer-Overflow-to-Buffer-Overflow Vulnerability Detection

Autoren: Jing Ma Xinhua Li Qingjia Huang et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783030142339

Zugangs-URL: https://rd.springer.com/chapter/10.1007/978-3-030-14234-6_22
https://link.springer.com/chapter/10.1007/978-3-030-14234-6_22
https://dblp.uni-trier.de/db/conf/cisc/inscrypt2018.html#XuLXLHMLH18

Stack overflow vulnerability detection based on BiLSTM-attention KAN deep learning model.

Autoren: Zhai, Jiqiang Qi, Zuming Yang, Hailu

Quelle: Journal of Supercomputing. Jul2025, Vol. 81 Issue 10, p1-25. 25p.

Schlagwörter: *LONG short-term memory, *ARTIFICIAL neural networks, *RECURRENT neural networks, *COGNITIVE psychology, *DATA security failures, *DEEP learning

Evolutionary Constraint-Based Testing Approach for Software Vulnerability Detection

Autoren: Al Sardy, Loui

Schlagwörter: Software Vulnerability Detection, Guided Search Pattern Testing, Buffer Overflow Detection, Static and Dynamic Analysis, Genetic Algorithms in Software Testing, Heuristic Optimization in Cybersecurity, Cybersecurity, Targeted Fuzzing Techniques, Security Flaw Detection, Local vs Global Optimization Algorithms, Memory Exploitation Attacks, Machine Code Analysis, Syntactical Constraint Analysis, Disassembling Analysis, Cybersecurity Testing Methods, Software Security Testing, Overlapping Machine Instructions, Vulnerability Detection Techniques, Software Testing Efficiency, Security Testing Approaches, Optimization in Vulnerability Testing

Modern Approaches to Software Vulnerability Detection: A Survey of Machine Learning, Deep Learning, and Large Language Models.

Autoren: Shaon, Md. Shazzad Hossain Akter, Mst Shapna

Quelle: Electronics (2079-9292); Nov2025, Vol. 14 Issue 22, p4449, 34p

Schlagwörter: MACHINE learning, DEEP learning, COMPUTER software security, ARTIFICIAL intelligence, LANGUAGE models, FEATURE extraction, DETECTION algorithms

Vulnerability detection based on transformer and high‐quality number embedding.

Autoren: Cao, Yang Dong, Yunwei Peng, Jiajie

Quelle: Concurrency & Computation: Practice & Experience; Dec2024, Vol. 36 Issue 28, p1-18, 18p

Schlagwörter: COMPUTER security vulnerabilities, TRANSFORMER models, COMPUTER software security, PROGRAMMING languages, SOURCE code, DEEP learning

Trust Domain Extensions Guest Fuzzing Framework for Security Vulnerability Detection.

Autoren: Dahan, Eran Aviv, Itzhak Kiperberg, Michael

Quelle: Mathematics (2227-7390). Jun2025, Vol. 13 Issue 11, p1879. 25p.

Schlagwörter: *VIRTUAL machine systems, *COMPUTER security vulnerabilities, *DENIAL of service attacks, *HYPERVISOR (Computer software), *RANDOM numbers