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

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

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

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

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

Detection of Obfuscated Malicious JavaScript Code.

Authors: Alazab, Ammar Khraisat, Ansam Alazab, Moutaz et al.

Source: Future Internet. Aug2022, Vol. 14 Issue 8, p217-N.PAG. 15p.

Subject Terms: *JavaScript programming language, *Machine learning, *Internet users, *Cyberterrorism, *Malware, *Websites, Intrusion detection systems (Computer security)

JSForce: A Forced Execution Engine for Malicious JavaScript Detection

Authors: HU, Xunchao CHENG, Yao DUAN, Yue et al.

Source: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783319788128

Subject Terms: FOS: Computer and information sciences, Computer Science - Cryptography and Security, Forced execution, Information Security, 02 engineering and technology, Malicious javascript, False positive and false negatives, Design and implementations, 0202 electrical engineering, electronic engineering, information engineering, Detection rates, Dynamic analysis techniques, Execution engine, Analysis techniques, Cryptography and Security (cs.CR)

File Description: application/pdf

Access URL: http://arxiv.org/pdf/1701.07860
http://arxiv.org/abs/1701.07860

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

Malicious JavaScript Detection Method Based on Multilayer Perceptron

Authors: Zhenyuan Guo

Source: Advances in Intelligent Systems and Computing ISBN: 9783030627423