Search Results - obfuscated JavaScript*

Obfuscated Clone Search in JavaScript based on Reinforcement Subsequence Learning

Authors: Leo Song Steven H. H. Ding Yuan Tian et al.

Source: ACM Transactions on Software Engineering and Methodology. 34:1-28

A Novel Runtime Monitoring Framework for WebAssembly-Obfuscated Malicious JavaScript Classification

Authors: Bangyan Ju Yu Zhao Chong Yu et al.

Detection and Analysis of Obfuscated and Minified JavaScript in the Croatian Web Space

Authors: Toni Dujmović Bruno Skendrović Ivan Kovačević et al.

Source: 2023 46th MIPRO ICT and Electronics Convention (MIPRO). :1252-1257

Subject Terms: JavaScript, cybersecurity, minification, automated detection, Croatian web space, obfuscation

Automatic Identification of Obfuscated JavaScript using Machine Learning

Authors: Susana Maria de Sousa Lima

Subject Terms: Engenharia electrotécnica, electrónica e informática, Electrical engineering, Electronic engineering, Information engineering

File Description: application/pdf

Availability: https://hdl.handle.net/10216/135504

Obfuscated JavaScript Code Detection using Machine Learning with AST-based Syntactic and Lexical Analysis

Authors: Eren Kılıç Mehmet Tahir Sandıkkaya

Source: 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech). :1-6

TransAST: A Machine Translation-Based Approach for Obfuscated Malicious JavaScript Detection

Authors: Yan Qin Weiping Wang Zixian Chen et al.

Source: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :327-338

Looking for Criminal Intents in JavaScript Obfuscated Code

Authors: Cerutti, Federico di San Pietro, Daniele Barattieri Gringoli, Francesco et al.

Source: Procedia Computer Science. 207:867-876

Subject Terms: JavaScript, web, cybercrime, code obfuscation, code deobfuscation, control flow flattening, reverse engineering, e-commerce, user tracking, fingerprinting, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

File Description: application/pdf

Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement

Authors: Samuel Ndichu Sangwook Kim Seiichi Ozawa

Source: CAAI Transactions on Intelligence Technology (2020)

Subject Terms: obscure code, obfuscated benign js codes, learned feature vectors, 02 engineering and technology, long short-term memory model, QA76.75-76.765, vectors, js code editor, 0202 electrical engineering, electronic engineering, information engineering, Computer software, fasttext model, feature extraction, unpacking, java, term frequency–inverse document frequency model, original js code, invasive software, text analysis, multilayer obfuscation, machine learning models detection, dud-preprocessed obfuscated malicious js codes, formatted js code, paragraph vector models, Computational linguistics. Natural language processing, learning (artificial intelligence), deobfuscation methods, internet, P98-98.5, undetectable code

Access URL: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/trit.2020.0026
https://doaj.org/article/1ec26bbd25b54437bb5faa62e4cf5359

On improvements of robustness of obfuscated JavaScript code detection

Authors: G. S. Ponomarenko P. G. Klyucharev

Source: Journal of Computer Virology and Hacking Techniques. 19:387-398

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Detection of Obfuscated Javascript Code Based on Abstract Syntax Trees Coloring

Authors: G. S. Ponomarenko P. G. Klyucharev

Source: Mathematics and Mathematical Modeling. :1-24

Subject Terms: 0102 computer and information sciences, 0101 mathematics, 01 natural sciences

Access URL: https://www.mathmelpub.ru/jour/article/download/218/167

Detecting Malicious Campaigns in Obfuscated JavaScript with Scalable Behavioral Analysis

Authors: Oleksii Starov Yuchen Zhou Jun Wang

Source: 2019 IEEE Security and Privacy Workshops (SPW). :218-223

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ieeexplore.ieee.org/ielx7/8834415/8844588/08844636.pdf

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

From Obfuscated to Obvious: A Comprehensive JavaScript Deobfuscation Tool for Security Analysis

Authors: Zhou, Dongchao Ying, Lingyun Chai, Huajun et al.

Subject Terms: Cryptography and Security, Software Engineering

Relation: http://arxiv.org/abs/2512.14070

Availability: http://arxiv.org/abs/2512.14070
https://doi.org/10.14722/ndss.2026.242198

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)

JSimpo: Structural Deobfuscation of JavaScript Programs.

Authors: Chen, Tianyu Li, Ding Zhang, Ying et al.

Source: ACM Transactions on Software Engineering & Methodology; Sep2025, Vol. 34 Issue 7, p1-28, 28p

Subject Terms: JAVASCRIPT programming language, MALWARE

Obfuscated malicious javascript detection scheme using the feature based on divided URL

Authors: Shoya Morishige Shuichiro Haruta Hiromu Asahina et al.

Source: 2017 23rd Asia-Pacific Conference on Communications (APCC). :1-6

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Detecting obfuscated JavaScripts using machine learning

Authors: Aebersold, Simon Kryszczuk, Krzysztof Paganoni, Sergio et al.

Subject Terms: Detection, Malicious JavaScript, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 006: Spezielle Computerverfahren, Obfuscated JavaScript

Detecting obfuscated suspicious JavaScript based on collaborative training

Authors: Hongcheng Wu Sujuan Qin

Source: 2017 IEEE 17th International Conference on Communication Technology (ICCT). :1962-1966

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

An Efficient Method for Detecting Obfuscated Suspicious JavaScript Based on Text Pattern Analysis

Authors: Jiawei Su Katsunari Yoshioka Junji Shikata et al.

Source: Proceedings of the 2016 ACM International on Workshop on Traffic Measurements for Cybersecurity. :3-11

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement.

Authors: Kim, Sangwook Ozawa, Seiichi Ndichu, Samuel

Index Terms: invasive, software, Java, Internet, feature extraction, text analysis, vectors, learning (artificial, intelligence), formatted JS code, deobfuscation methods, unpacking, DUD-preprocessed obfuscated malicious JS codes, term frequency-inverse document frequency model, long short-term memory model, paragraph vector models, obfuscated benign JS codes, learned feature vectors, FastText model, JS code editor, multilayer obfuscation, original JS code, undetectable code, obscure code, machine learning models detection, Journal Article., VoR.

URL: https://hdl.handle.net/20.500.14094/90009107
https://doi.org/10.1049/trit.2020.0026
https://da.lib.kobe-u.ac.jp/da/kernel/90009107/90009107.pdf