Search Results - "malicious JavaScript detection"

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

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

A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors

Authors: Ndichu, Samuel Kim, Sangwook Ozawa, Seiichi et al.

Subject Terms: Cybersecurity, Machine learning, Doc2Vec, Malicious JavaScript detection, Feature learning, Abstract Syntax Tree

Relation: info:doi/10.1016/j.asoc.2019.105721

Availability: http://www.lib.kobe-u.ac.jp/handle_kernel/90006845
http://www.lib.kobe-u.ac.jp/repository/90006845.pdf

A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors

Authors: Samuel Ndichu Sangwook Kim Seiichi Ozawa et al.

Source: Applied Soft Computing. 84:105721

Subject Terms: Cybersecurity, Machine learning, Malicious JavaScript detection, Feature learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Abstract Syntax Tree, Doc2Vec

Access URL: https://ci.nii.ac.jp/naid/120006811119
https://dblp.uni-trier.de/db/journals/asc/asc84.html#NdichuKOMM19
https://www.sciencedirect.com/science/article/pii/S1568494619305022

A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors.

Authors: Kim, Sangwook Ozawa, Seiichi Misu, Takeshi et al.

Index Terms: Cybersecurity, Machine learning, Doc2Vec, Malicious JavaScript detection, Feature learning, Abstract Syntax Tree, Journal Article., VoR.

URL: https://hdl.handle.net/20.500.14094/90006845
https://doi.org/10.1016/j.asoc.2019.105721
https://da.lib.kobe-u.ac.jp/da/kernel/90006845/90006845.pdf