Výsledky vyhľadávania - malicious JavaScript detection

A Graph Learning Approach for Malicious Javascript Detection

Autori: Ngoc C. Lê Duong Thai Quach Tien-Manh Nguyen a ďalší

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

Malicious Javascript Detection based on Clustering Techniques

Autori: Nguyen Hong Son Dung, Ha Thanh

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

Predmety: 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

Autori: Ngoc Minh Phung Mamoru Mimura

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

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

Autori: Liang, Zhihong Wang, Xin Hu, Zhenhuang a ďalší

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

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

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

Autori: Yan Qin Weiping Wang Zixian Chen a ďalší

Zdroj: 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

Autori: Phung Minh Ngoc

Popis súboru: application/pdf

Prístupová 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

ZipAST: Enhancing malicious JavaScript detection with sequence compression

Autori: Zixian Chen Weiping Wang Yan Qin a ďalší

Zdroj: Computers & Security. 153:104390

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

Autori: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa a ďalší

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

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

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

Research on Malicious JavaScript Detection Technology Based on LSTM

Autori: Yong Fang Cheng Huang Liang Liu a ďalší

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

Predmety: 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

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

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

Autori: Scaria Alex T. Dhiliphan Rajkumar

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

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

JStap: a static pre-filter for malicious JavaScript detection

Autori: Fass, Aurore Backes, Michael Stock, Ben

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

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

Deep Neural Networks for Malicious JavaScript Detection Using Bytecode Sequences

Autori: Muhammad Fakhrur Rozi Sangwook Kim Seiichi Ozawa

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

Predmety: 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

Autori: Scaria Alex T. Dhiliphan Rajkumar

Zdroj: 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

Autori: Ngoc Minh Phung Mamoru Mimura

Zdroj: Lecture Notes in Computer Science ISBN: 9783031330162

Hybrid Optimization Driven Technique for Malicious Javascript Detection Based on Deep Learning Classifier

Zdroj: International Journal of Innovative Technology and Exploring Engineering. 9:794-797

ScriptNet: Neural Static Analysis for Malicious JavaScript Detection

Autori: Stokes, Jack W. Agrawal, Rakshit McDonald, Geoff a ďalší

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

Predmety: 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)

Prístupová URL adresa: http://arxiv.org/pdf/1904.01126
http://arxiv.org/abs/1904.01126

Detection of Obfuscated Malicious JavaScript Code.

Autori: Alazab, Ammar Khraisat, Ansam Alazab, Moutaz a ďalší

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

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

JSForce: A Forced Execution Engine for Malicious JavaScript Detection

Autori: HU, Xunchao CHENG, Yao DUAN, Yue a ďalší

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

Predmety: 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)

Popis súboru: application/pdf

Prístupová URL adresa: 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

Autori: Samuel Ndichu Seiichi Ozawa Takeshi Misu a ďalší

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

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

Malicious JavaScript Detection Method Based on Multilayer Perceptron

Autori: Zhenyuan Guo

Zdroj: Advances in Intelligent Systems and Computing ISBN: 9783030627423