Search Results - "malicious JavaScript"

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

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

Authors: Bangyan Ju Yu Zhao Chong Yu et al.

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

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

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

Authors: Phu H. Phung Allen Varghese Bojue Wang et al.

Source: Communications in Computer and Information Science ISBN: 9783031752001

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

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

Authors: Kunlun Ren Weizhong Qiang Yueming Wu et al.

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

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

JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

Authors: Kunlun Ren Weizhong Qiang Yueming Wu et al.

Source: 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

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

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

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

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

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

Authors: Eli Fulkerson Eric Yocam Varghese Vaidyan et al.

Source: Machine Learning with Applications. 21:100724

DeepSecure: Malicious JavaScript and NPM Package Scanner

Authors: Lim, Jun Wei Fabian Chia, Wen Ming Micah Ong, Jun Leng et al.

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

Subject Terms: System and network security, Software and application security

HideNoSeek: Camouflaging Malicious JavaScript in Benign ASTs

Authors: Fass, Aurore Backes, Michael Stock, Ben

Source: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. :1899-1913

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