Výsledky vyhledávání - Malicious JavaScript~

A Graph Learning Approach for Malicious Javascript Detection

Autoři: Ngoc C. Lê Duong Thai Quach Tien-Manh Nguyen a další

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

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

Autoři: Bangyan Ju Yu Zhao Chong Yu a další

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

Autoři: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa a další

Zdroj: IEEE Access, Vol 11, Pp 102727-102745 (2023)

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

Přístupová URL adresa: https://doaj.org/article/96b921f1abd0426fa01fe22f24802029

Malicious Javascript Detection based on Clustering Techniques

Autoři: Nguyen Hong Son Dung, Ha Thanh

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

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

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

Autoři: Liang, Zhihong Wang, Xin Hu, Zhenhuang a další

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

Přístupová URL adresa: http://arxiv.org/abs/2507.22447

Malicious JavaScript Detection in Realistic Environments with SVM and MLP Models

Autoři: Ngoc Minh Phung Mamoru Mimura

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

JSMBox—A Runtime Monitoring Framework for Analyzing and Classifying Malicious JavaScript

Autoři: Phu H. Phung Allen Varghese Bojue Wang a další

Zdroj: Communications in Computer and Information Science ISBN: 9783031752001

JSHint: Revealing API Usage to Improve Detection of Malicious JavaScript

Autoři: Shaown Sarker Kasimir Schulz Aleksandr Nahapetyan a další

Zdroj: Lecture Notes in Computer Science ISBN: 9783031757631

An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors

Autoři: Kunlun Ren Weizhong Qiang Yueming Wu a další

Zdroj: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. :1420-1432

A study of malicious JavaScript detection methods in a realistic environment

Autoři: Phung Minh Ngoc

Popis souboru: application/pdf

Přístupová URL adresa: 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

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

Autoři: Yan Qin Weiping Wang Zixian Chen a další

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

JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

Autoři: Kunlun Ren Weizhong Qiang Yueming Wu a další

Zdroj: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :339-351

Detection Approach of Malicious JavaScript Code Based on deep learning

Autoři: Liyuan Zheng Dongcheng Zhang Xin Xie a další

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

Research on Evasion and Detection of Malicious JavaScript Code

Autoři: Yujie Ma Haokai Wu Yu-an Tan a další

Zdroj: Lecture Notes in Computer Science ISBN: 9789819724574

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

Autoři: Samuel Ndichu Sangwook Kim Seiichi Ozawa

Zdroj: CAAI Transactions on Intelligence Technology (2020)

Témata: 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

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

ZipAST: Enhancing malicious JavaScript detection with sequence compression

Autoři: Zixian Chen Weiping Wang Yan Qin a další

Zdroj: Computers & Security. 153:104390

Research on Malicious JavaScript Detection Technology Based on LSTM

Autoři: Yong Fang Cheng Huang Liang Liu a další

Zdroj: IEEE Access, Vol 6, Pp 59118-59125 (2018)

Témata: 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

Přístupová URL adresa: https://doaj.org/article/10eaad4b416443e996b2dfbad65517e4

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

Autoři: Scaria Alex T. Dhiliphan Rajkumar

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

Témata: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Pyrhoh: A Meta-Learning Analysis Framework for Determining the Impact of Compilation on Malicious Javascript Identification

Autoři: Eli Fulkerson Eric Yocam Varghese Vaidyan a další

Zdroj: Machine Learning with Applications. 21:100724

DeepSecure: Malicious JavaScript and NPM Package Scanner

Autoři: Lim, Jun Wei Fabian Chia, Wen Ming Micah Ong, Jun Leng a další

Zdroj: Proceedings of the 9th IRC Conference on Science, Engineering, and Technology ISBN: 9789819983681

Témata: System and network security, Software and application security