Search Results - Malicious JavaScript detection*

A Graph Learning Approach for Malicious Javascript Detection

Authors: Ngoc C. Lê Duong Thai Quach Tien-Manh Nguyen et al.

Source: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783032023612

Malicious Javascript Detection based on Clustering Techniques

Authors: Nguyen Hong Son Dung, Ha Thanh

Source: International Journal of Network Security & Its Applications. 13:11-21

Subject Terms: Malicious JavaScript, Detection model, Prediction support parameter, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, K-means clustering algorithm

Malicious JavaScript Detection in Realistic Environments with SVM and MLP Models

Authors: Ngoc Minh Phung Mamoru Mimura

Source: Journal of Information Processing. 32:748-756

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

Authors: Liang, Zhihong Wang, Xin Hu, Zhenhuang et al.

Subject Terms: Machine Learning, FOS: Computer and information sciences, Cryptography and Security, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2507.22447

JSHint: Revealing API Usage to Improve Detection of Malicious JavaScript

Authors: Shaown Sarker Kasimir Schulz Aleksandr Nahapetyan et al.

Source: Lecture Notes in Computer Science ISBN: 9783031757631

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

A study of malicious JavaScript detection methods in a realistic environment

Authors: Phung Minh Ngoc

File Description: application/pdf

Access URL: https://ndlsearch.ndl.go.jp/books/R100000039-I14460990

Degree: 博士(工学) -- 大学改革支援・学位授与機構

Detection Approach of Malicious JavaScript Code Based on deep learning

Authors: Liyuan Zheng Dongcheng Zhang Xin Xie et al.

Source: 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). :1075-1079

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

Research on Evasion and Detection of Malicious JavaScript Code

Authors: Yujie Ma Haokai Wu Yu-an Tan et al.

Source: Lecture Notes in Computer Science ISBN: 9789819724574

ZipAST: Enhancing malicious JavaScript detection with sequence compression

Authors: Zixian Chen Weiping Wang Yan Qin et al.

Source: Computers & Security. 153:104390

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

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, 0202 electrical engineering, electronic engineering, information engineering, bytecode, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, malicious code detection, LSTM, word vector, TK1-9971

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

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

Authors: Scaria Alex T. Dhiliphan Rajkumar

Source: International Journal of Intelligent Systems. 36:7153-7176

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

JStap: a static pre-filter for malicious JavaScript detection

Authors: Fass, Aurore Backes, Michael Stock, Ben

Source: Proceedings of the 35th Annual Computer Security Applications Conference. :257-269

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

Deep Neural Networks for Malicious JavaScript Detection Using Bytecode Sequences

Authors: Muhammad Fakhrur Rozi Sangwook Kim Seiichi Ozawa

Source: 2020 International Joint Conference on Neural Networks (IJCNN). :1-8

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

Adaptive Spider Bird Swarm Algorithm-Based Deep Recurrent Neural Network for Malicious JavaScript Detection Using Box-Cox Transformation

Authors: Scaria Alex T. Dhiliphan Rajkumar

Source: International Journal of Open Source Software and Processes. 11:46-59

Malicious JavaScript Detection Based on AST Analysis and Key Feature Re-sampling in Realistic Environments

Authors: Ngoc Minh Phung Mamoru Mimura

Source: Lecture Notes in Computer Science ISBN: 9783031330162

Detection and classification of malicious JavaScript via attack behavior modelling

Authors: XUE, Yinxing WANG, Junjie LIU, Yang et al.

Source: Proceedings of the 2015 International Symposium on Software Testing and Analysis. :48-59

Subject Terms: malware detection, L, Programming Languages and Compilers, 0202 electrical engineering, electronic engineering, information engineering, Software Engineering, 02 engineering and technology, malicious JavaScript, behavior modelling, 3. Good health

File Description: application/pdf

Access URL: https://ink.library.smu.edu.sg/sis_research/4953

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