Search Results - JavaScript malicious code*

A Combined Model for JavaScript Malicious Code Detection Based on Deep Q-Networks

Authors: Wenming Wang Hongru Song Jiaolong Zheng

Source: Proceedings of the 2025 5th International Conference on Automation Control, Algorithm and Intelligent Bionics. :344-349

Systems and methods for causing nonpredictable environment states for exploit prevention and malicious code neutralization for javascript-enabled applications

Systems and methods for exploit prevention and malicious code neutralization using non-predictable results for JavaScript-enabled applications

SYSTEMS AND METHODS FOR CAUSING NONPREDICTABLE ENVIRONMENT STATES FOR EXPLOIT PREVENTION AND MALICIOUS CODE NEUTRALIZATION FOR JAVASCRIPT-ENABLED APPLICATIONS

Systems and methods for causing nonpredictable environment states for exploit prevention and malicious code neutralization for JavaScript-enabled applications

JACLNet:Application of adaptive code length network in JavaScript malicious code detection.

Authors: Zhining Zhang Liang Wan Kun Chu et al.

Source: PLoS ONE, Vol 17, Iss 12, p e0277891 (2022)

Subject Terms: Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/1932-6203

Access URL: https://doaj.org/article/eb6a9c9af180452ca1ce0ad7d3428689

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

Authors: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa et al.

Source: IEEE Access, Vol 11, Pp 102727-102745 (2023)

Subject Terms: Abstract syntax tree, cyber security, graph neural network, malicious JavaScript detection, source code representation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10255646/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/96b921f1abd0426fa01fe22f24802029

Detection of Obfuscated Malicious JavaScript Code

Authors: Ammar Alazab Ansam Khraisat Moutaz Alazab et al.

Source: Future Internet, Vol 14, Iss 8, p 217 (2022)

Subject Terms: malware detection, intrusion detection, obfuscated malicious, machine learning, malicious JavaScript, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://www.mdpi.com/1999-5903/14/8/217; https://doaj.org/toc/1999-5903

Access URL: https://doaj.org/article/e2fa7807e7e643e98d2b76506dbc7dd6

Detection of Obfuscated Malicious JavaScript Code.

Authors: Alazab, Ammar Khraisat, Ansam Alazab, Moutaz et al.

Source: Future Internet. Aug2022, Vol. 14 Issue 8, p217-N.PAG. 15p.

Subject Terms: *JavaScript programming language, *Machine learning, *Internet users, *Cyberterrorism, *Malware, *Websites, Intrusion detection systems (Computer security)

PDF Malicious Indicators Extraction Technique Based on Improved Symbolic Execution

Authors: SONG Enzhou, HU Tao, YI Peng, WANG Wenbo

Source: Jisuanji kexue, Vol 51, Iss 7, Pp 389-396 (2024)

Subject Terms: malicious documents, javascript code, indicator extraction, symbolic execution, code rewriting, constraint solving optimization, Computer software, QA76.75-76.765, Technology (General), T1-995

File Description: electronic resource

Relation: https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2024-51-7-389.pdf; https://doaj.org/toc/1002-137X

Access URL: https://doaj.org/article/79a039d822d2491d9e19425eaac35223

SYSTEMS AND METHODS FOR CAUSING NONPREDICTABLE ENVIRONMENT STATES FOR EXPLOIT PREVENTION AND MALICIOUS CODE NEUTRALIZATION FOR JAVASCRIPT-ENABLED APPLICATIONS

SYSTEMS AND METHODS FOR EXPLOIT PREVENTION AND MALICIOUS CODE NEUTRALIZATION USING NON-PREDICTABLE RESULTS FOR JAVASCRIPT-ENABLED APPLICATIONS

Systems and methods for causing nonpredictable environment states for exploit prevention and malicious code neutralization for javascript-enabled applications

Systems and methods for exploit prevention and malicious code neutralization using non-predictable results for JavaScript-enabled applications

JavaScript Malicious Codes Analysis Based on Naive Bayes Classification

Authors: Hongliang Liang Qian Zhao Daijie Zhang et al.

Source: 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. :513-519

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 16. Peace & justice

Access URL: https://www.computer.org/csdl/proceedings-article/3pgcic/2014/4171a513/12OmNxFsmCT
https://ieeexplore.ieee.org/document/7024638/

SYSTEMS AND METHODS FOR CAUSING NONPREDICTABLE ENVIRONMENT STATES FOR EXPLOIT PREVENTION AND MALICIOUS CODE NEUTRALIZATION FOR JAVASCRIPT-ENABLED APPLICATIONS

SYSTEMS AND METHODS FOR EXPLOIT PREVENTION AND MALICIOUS CODE NEUTRALIZATION USING NON-PREDICTABLE RESULTS FOR JAVASCRIPT-ENABLED APPLICATIONS

Research on Malicious JavaScript Detection Technology Based on LSTM

Authors: Yong Fang Cheng Huang Liang Liu et al.

Source: IEEE Access, Vol 6, Pp 59118-59125 (2018)

Subject Terms: JavaScript, malicious code detection, bytecode, word vector, LSTM, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/8482113/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/10eaad4b416443e996b2dfbad65517e4

Behavior Analysis Usage with Behavior Tures Adoption for Malicious Code Detection on JAVASCRIPT Scenarios Example

Authors: Y. M. Tumanov S.V. Gavrilyuk

Source: Безопасность информационных технологий, Vol 17, Iss 1, Pp 115-117 (2010)

Subject Terms: malicious code detection, behavior analysis, behavior signature, Information technology, T58.5-58.64, Information theory, Q350-390

File Description: electronic resource

Relation: https://bit.mephi.ru/index.php/bit/article/view/791; https://doaj.org/toc/2074-7128; https://doaj.org/toc/2074-7136

Access URL: https://doaj.org/article/1ee7b69964a2440c926708454e78aada

JACLNet:Application of adaptive code length network in JavaScript malicious code detection.

Authors: Zhang, Zhining Wan, Liang Chu, Kun et al.

Source: PLoS ONE; 12/14/2022, Vol. 17 Issue 12, p1-27, 27p

Subject Terms: DEEP learning, JAVASCRIPT programming language, LINEAR network coding, FEATURE extraction, DATA mining, INFORMATION modeling, SYNTAX (Grammar)