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 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

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

ZipAST: Enhancing malicious JavaScript detection with sequence compression

Authors: Zixian Chen Weiping Wang Yan Qin et al.

Source: Computers & Security. 153:104390

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

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

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

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

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

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

ScriptNet: Neural Static Analysis for Malicious JavaScript Detection

Authors: Stokes, Jack W. Agrawal, Rakshit McDonald, Geoff et al.

Source: MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1-8

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Access URL: http://arxiv.org/pdf/1904.01126
http://arxiv.org/abs/1904.01126

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

A Machine Learning Approach to Malicious JavaScript Detection using Fixed Length Vector Representation

Authors: Samuel Ndichu Seiichi Ozawa Takeshi Misu et al.

Source: 2018 International Joint Conference on Neural Networks (IJCNN). :1-8

Subject Terms: 13. Climate action, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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

Malicious javascript detection based on abstract syntax trees (AST) and deep machine learning (DML)

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

Authors: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa et al.

Source: Lecture Notes in Computer Science ISBN: 9783030922696

Malicious JavaScript Detection Method Based on Multilayer Perceptron

Authors: Zhenyuan Guo

Source: Advances in Intelligent Systems and Computing ISBN: 9783030627423

Obfuscated malicious javascript detection scheme using the feature based on divided URL

Authors: Shoya Morishige Shuichiro Haruta Hiromu Asahina et al.

Source: 2017 23rd Asia-Pacific Conference on Communications (APCC). :1-6

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health