Machine Learning Based Cross Language Vulnerability Detection


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Machine Learning Based Cross-language Vulnerability Detection


Machine Learning Based Cross-language Vulnerability Detection

Author: Anki Chauhan

language: en

Publisher:

Release Date: 2020


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This thesis concerns the study of Machine Learning based methods for detecting vulnerable code. Various Neural Network models have been trained to detect specific vulnerabilities on a programming language dataset. This work, entails an approach not targeting specific vulnerabilities. We also leverage the commonality among programming languages like JAVA and C# by training the model on both languages and detecting vulnerabilities.

Ubiquitous Security


Ubiquitous Security

Author: Guojun Wang

language: en

Publisher: Springer Nature

Release Date: 2023-02-15


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This book constitutes the refereed proceedings of the Second International Conference, UbiSec 2022, held in Zhangjiajie, China, during December 28–31, 2022. The 34 full papers and 4 short papers included in this book were carefully reviewed and selected from 98 submissions. They were organized in topical sections as follows: cyberspace security, cyberspace privacy, cyberspace anonymity and short papers.

Detection of Intrusions and Malware, and Vulnerability Assessment


Detection of Intrusions and Malware, and Vulnerability Assessment

Author: Manuel Egele

language: en

Publisher: Springer Nature

Release Date: 2025-07-09


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The two-volume set LNCS 15747 and 15748 constitutes the refereed conference proceedings of the 12nd International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2025, held in Graz, Austria, during July 9–11, 2025. The 25 revised full papers and 11 posters are presented in these proceedings were carefully reviewed and selected from 103 submissions. The papers are organized in the following topical sections: Part I: Web Security; Vulnerability Detection; Side channels; and Obfuscation. Part II: AI/ML & Security; Android & Patches; OS & Network; and Resilient Systems.