Search Results - malicious JavaScripts*

MalJs: Lexical, Structural and Behavioral Analysis of Malicious JavaScripts Using Ensemble Classifier

Authors: Surendran K Prabaharan Poornachandran Aravind Ashok Nair et al.

Source: Communications in Computer and Information Science ISBN: 9789811027376

Malicious JavaScript Detection by Features Extraction

Authors: Gerardo Canfora Francesco Mercaldo Corrado Aaron Visaggio

Source: e-Informatica Software Engineering Journal, Vol 8, Iss 1, Pp 65-78 (2015)

Subject Terms: malicious JavaScripts, Computer software, QA76.75-76.765

File Description: electronic resource

Relation: http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_8/eInformatica2014Art5.pdf; https://doaj.org/toc/1897-7979; https://doaj.org/toc/2084-4840

Access URL: https://doaj.org/article/ffb1293311cf45bfb4865f519bf6927b

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

Malicious JavaScript Detection by Features Extraction

Authors: Gerardo Canfora Francesco Mercaldo Corrado Aaron Visaggio et al.

Contributors: Gerardo Canfora Francesco Mercaldo Corrado Aaron Visaggio et al.

Source: e-Informatica Software Engineering Journal, Vol 8, Iss 1, Pp 65-78 (2015)

Subject Terms: QA76.75-76.765, malicious web pages, JavaScript, security, static analysis, machine learning, 0202 electrical engineering, electronic engineering, information engineering, informatyka - czasopisma, Computer software, 02 engineering and technology, malicious JavaScripts, 3. Good health

File Description: application/pdf

Access URL: https://doaj.org/article/ffb1293311cf45bfb4865f519bf6927b
https://hdl.handle.net/11695/115414
https://doi.org/10.5277/e-Inf140105

Detecting obfuscated JavaScripts using machine learning

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

Index Terms: Obfuscated JavaScript, Detection, Malicious JavaScript, Machine learning, 006: Spezielle Computerverfahren, Konferenz: Paper, Text

URL: https://doi.org/10.21256/zhaw-3848
ICIMP 2016 the Eleventh International Conference on Internet Monitoring and Protection : May 22-26, 2016, Valencia, Spain
ICIMP 2016 the Eleventh International Conference on Internet Monitoring and Protection, Valencia, Spain, 22-26 May 2016

Performance evaluations of AI‐based obfuscated and encrypted malicious script detection with feature optimization.

Authors: Kim, Kookjin Shin, Jisoo Park, Jong‐Geun et al.

Source: ETRI Journal; Aug2025, Vol. 47 Issue 4, p753-770, 18p

Subject Terms: ARTIFICIAL intelligence, MACHINE learning, SECURITY management, APPLIED sciences, MALWARE, DATA encryption, DETECTION algorithms

JStrack: Enriching Malicious JavaScript Detection Based on AST Graph Analysis and Attention Mechanism.

Authors: Ban, Tao Ozawa, Seiichi Kim, Sangwook et al.

Index Terms: Cyber security, Malicious JavaScript, Abstract syntax tree, Graph neural network, Conference Paper., AM.

URL: https://hdl.handle.net/20.500.14094/0100481143
https://doi.org/10.1007/978-3-030-92270-2
https://da.lib.kobe-u.ac.jp/da/kernel/0100481143/0100481143.pdf
978-3-030-92270-2

Automatic attack signature generation technology for malicious javascript.

Authors: Yoon, Soojin Jung, JongHun Noh, MyoungSun et al.

Source: Proceedings of 2014 International Conference on Modelling, Identification & Control; 2014, p351-354, 4p

Detecting obfuscated JavaScripts from known and unknown obfuscators using machine learning

Authors: Tellenbach, Bernhard Paganoni, Sergio Rennhard, Marc

Index Terms: Machine Learning, JavaScript obfuscation, 006: Spezielle Computerverfahren, Beitrag in wissenschaftlicher Zeitschrift, Text

URL: https://doi.org/10.21256/zhaw-1537
International Journal on Advances in Security

Hybrid Optimization Driven Technique for Malicious Javascript Detection Based on Deep Learning Classifier

Source: International Journal of Innovative Technology and Exploring Engineering. 9:794-797

Malicious Office Macro Detection: Combined Features with Obfuscation and Suspicious Keywords.

Authors: Chen, Xiang Wang, Wenbo Han, Weitao

Source: Applied Sciences (2076-3417); Nov2023, Vol. 13 Issue 22, p12101, 15p

Subject Terms: MACHINE learning, ANTI-malware (Computer software), FEATURE selection, PHISHING, EMAIL security

Company/Entity: MICROSOFT Corp.

Detecting obfuscated JavaScripts from known and unknown obfuscators using machine learning

Authors: Tellenbach, Bernhard Paganoni, Sergio Rennhard, Marc

Subject Terms: Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 006: Spezielle Computerverfahren, JavaScript obfuscation

Malicious Script Blocking Detection Technology Using a Local Proxy.

Authors: Oh, SangHwan Bae, HanChul Yoon, SooJin et al.

Source: 2016 10th International Conference on Innovative Mobile & Internet Services in Ubiquitous Computing (IMIS); 2016, p495-498, 4p

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, INTRUSION detection systems (Computer security), MACHINE learning, INTERNET users, CYBERTERRORISM, MALWARE, WEBSITES

INCREMENTAL AND SPECULATIVE ANALYSIS OF JAVASCRIPTS BASED ON A MULTI-INSTANCE MODEL FOR WEB SECURITY

Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens.

Authors: Han, KyungHyun Hwang, Seong Oun

Source: Applied Sciences (2076-3417); Sep2020, Vol. 10 Issue 17, p6116, 19p

Subject Terms: JAVASCRIPT programming language, TOKENS

Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection.

Authors: Alex, Scaria Dhiliphan Rajkumar, T.

Source: International Journal of Intelligent Systems; Dec2021, Vol. 36 Issue 12, p7153-7176, 24p

Subject Terms: MATHEMATICAL optimization, WEB-based user interfaces, COMPUTER network security, FEATURE extraction, VIRAL transmission, COMPUTER networks

Malicious JavaScript Detection Based on Bidirectional LSTM Model.

Authors: Song, Xuyan Chen, Chen Cui, Baojiang et al.

Source: Applied Sciences (2076-3417); May2020, Vol. 10 Issue 10, p3440, 21p

Subject Terms: JAVASCRIPT programming language, FORGERY

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 ; 9th International Conference on Network and Parallel Computing (NPC) ; https://inria.hal.science/hal-01551330 ; 9th International Conference on Network and Parallel Computing (NPC), Sep 2012, Gwangju, South Korea. pp.129-137, ⟨10.1007/978-3-642-35606-3_15⟩

Subject Terms: Malicious Web Sites, JavaScript Obfuscation, JavaScript Extraction, Hybrid Strength Analysis System, [INFO]Computer Science [cs]

Subject Geographic: Gwangju, South Korea

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

An ensemble-based stegware detection system for information hiding malware attacks.

Authors: Monika, A. Eswari, R.

Source: Journal of Ambient Intelligence & Humanized Computing; Apr2023, Vol. 14 Issue 4, p4401-4417, 17p