Search Results - "Obfuscated JavaScript"

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

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

MOJI: Character-level convolutional neural networks for Malicious Obfuscated JavaScript Inspection

Authors: Minato Ishida Naoshi Kaneko Kazuhiko Sumi

Source: Applied Soft Computing. 137:110138

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

JSObfusDetector: A binary PSO-based one-class classifier ensemble to detect obfuscated JavaScript code

Authors: Mehran Jodavi Mahdi Abadi Elham Parhizkar

Source: 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP). :322-327

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

Detecting Obfuscated JavaScript Malware Using Sequences of Internal Function Calls

Authors: Alireza Gorji Mahdi Abadi

Source: Proceedings of the 2014 ACM Southeast Regional Conference. :1-6

Subject Terms: 2. Zero hunger, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 16. Peace & justice

Automatic Simplification of Obfuscated JavaScript Code: A Semantics-Based Approach

Authors: Gen Lu Saumya Debray

Source: 2012 IEEE Sixth International Conference on Software Security and Reliability. :31-40

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

Characterizing Obfuscated JavaScript Using Abstract Syntax Trees: Experimenting with Malicious Scripts

Authors: Gregory Blanc Daisuke Miyamoto Mitsuaki Akiyama et al.

Source: 2012 26th International Conference on Advanced Information Networking and Applications Workshops. :344-351

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences

JaSt: Fully Syntactic Detection of Malicious (Obfuscated) JavaScript

Authors: Aurore Fass Robert P. Krawczyk Michael Backes et al.

Source: Lecture Notes in Computer Science ISBN: 9783319934105

Subject Terms: 3. Good health

Social Engineering Attacks Using Technical Job Interviews: Real-Life Case Analysis and AI-Assisted Mitigation Proposals

Authors: Tomás de J. Mateo Sanguino

Source: Information ; Volume 17 ; Issue 1 ; Pages: 98

Subject Terms: social engineering, technical job interviews, malicious code detection, obfuscated JavaScript, AI-assisted cybersecurity, crypto wallet compromise, threat mitigation strategies, remote code execution

File Description: application/pdf

Relation: Artificial Intelligence; https://dx.doi.org/10.3390/info17010098

Availability: https://doi.org/10.3390/info17010098

Automatic Simplification of Obfuscated JavaScript Code (Extended Abstract)

Authors: Gen Lu Kevin Coogan Saumya Debray

Source: Communications in Computer and Information Science ISBN: 9783642291654

Hybrid Obfuscated Javascript Strength Analysis System for Detection of Malicious Websites

Authors: Krishnaveni, R. Chellappan, C. Dhanalakshmi, R. et al.

Contributors: Krishnaveni, R. Chellappan, C. Dhanalakshmi, R. et al.

Source: Lecture Notes in Computer Science ISBN: 9783642356056

Subject Terms: Malicious Web Sites, JavaScript Obfuscation, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, [INFO] Computer Science [cs], JavaScript Extraction, Hybrid Strength Analysis System

File Description: application/pdf

Access URL: https://hal.inria.fr/hal-01551330/file/978-3-642-35606-3_15_Chapter.pdf
https://inria.hal.science/hal-01551330v1
https://doi.org/10.1007/978-3-642-35606-3_15
https://inria.hal.science/hal-01551330v1/document

Performance analysis of obfuscated JavaScript

Authors: Häyrynen, Mikko

Subject Terms: suorituskyky, JavaScript, Mathematical Information Technology, performance (capacity), Tietotekniikka, obfuscation, web

File Description: application/pdf; fulltext

Access URL: http://urn.fi/URN:NBN:fi:jyu-202206143294

Obfuscated JavaScript detection using syntactically and lexically enhanced machine learning ; Perdelenmiş JavaScript kodlarının sözdizimsel ve anlamsal yönden iyileştirilmiş makina öğrenmesi ile tespiti

Authors: Kılıç, Eren Sandıkkaya, Mehmet Tahir 866235 et al.

Contributors: Kılıç, Eren Sandıkkaya, Mehmet Tahir 866235 et al.

Subject Terms: computer security, computer science, control

File Description: application/pdf

Relation: https://hdl.handle.net/11527/25804

Availability: https://hdl.handle.net/11527/25804

Dynamically obfuscated javascript

Social Engineering Attacks Using Technical Job Interviews: Real-Life Case Analysis and AI-Assisted Mitigation Proposals.

Authors: Sanguino, Tomás de J. Mateo¹ (AUTHOR)

Source: Information. Jan2026, Vol. 17 Issue 1, p98. 25p.

Subject Terms: *Malware, Social engineering (Fraud), Field research, Language models, Internet security, Employment interviewing

MOJI: Character-level convolutional neural networks for Malicious Obfuscated JavaScript Inspection.

Authors: Ishida, Minato Kaneko, Naoshi Sumi, Kazuhiko

Source: Applied Soft Computing. Apr2023, Vol. 137, pN.PAG-N.PAG. 1p.

Subject Terms: Convolutional neural networks, JavaScript programming language, Antivirus software, Feature extraction, Deep learning

JaSt: Fully Syntactic Detection of Malicious (Obfuscated) JavaScript

Authors: Fass, Aurore Krawczyk, Robert Backes, Michael et al.

File Description: text

Relation: https://publications.cispa.saarland/2594/1/fass2018jast.pdf; Fass, Aurore and Krawczyk, Robert and Backes, Michael and Stock, Ben (2018) JaSt: Fully Syntactic Detection of Malicious (Obfuscated) JavaScript. In: Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA '18). Conference: DIMVA GI International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment

Availability: https://publications.cispa.saarland/2594/
https://publications.cispa.saarland/2594/1/fass2018jast.pdf