Výsledky vyhledávání - "Malicious JavaScript detection"

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

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

ZipAST: Enhancing malicious JavaScript detection with sequence compression

Autoři: Zixian Chen Weiping Wang Yan Qin a další

Zdroj: Computers & Security. 153:104390

An Approach for Malicious JavaScript Detection Using Adaptive Taylor Harris Hawks Optimization-Based Deep Convolutional Neural Network

Autoři: Scaria Alex Dhiliphan Rajkumar T.

Zdroj: International Journal of Distributed Systems and Technologies. 13:1-20

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

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

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

Přístupová URL adresa: https://onlinelibrary.wiley.com/doi/10.1002/int.22584

Malicious JavaScript Detection Based on AST Analysis and Key Feature Re-sampling in Realistic Environments

Autoři: Ngoc Minh Phung Mamoru Mimura

Zdroj: Lecture Notes in Computer Science ISBN: 9783031330162

JStap: a static pre-filter for malicious JavaScript detection

Autoři: Aurore Fass Michael Backes Ben Stock

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

Témata: 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

Autoři: Scaria Alex T. Dhiliphan Rajkumar

Zdroj: International Journal of Open Source Software and Processes. 11:46-59

Přístupová URL adresa: https://www.igi-global.com/article/adaptive-spider-bird-swarm-algorithm-based-deep-recurrent-neural-network-for-malicious-javascript-detection-using-box-cox-transformation/270895
https://doi.org/10.4018/IJOSSP.2020100103
https://dblp.uni-trier.de/db/journals/ijossp/ijossp11.html#AlexR20

Deep Neural Networks for Malicious JavaScript Detection Using Bytecode Sequences

Autoři: Muhammad Fakhrur Rozi Seiichi Ozawa Sangwook Kim

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

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

Přístupová URL adresa: https://ieeexplore.ieee.org/document/9207134
https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2020.html#RoziKO20

ScriptNet: Neural Static Analysis for Malicious JavaScript Detection

Autoři: Jack W. Stokes Matthew Hausknecht Rakshit Agrawal a další

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

Témata: 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)

Přístupová URL adresa: http://arxiv.org/pdf/1904.01126
http://arxiv.org/abs/1904.01126
https://arxiv.org/pdf/1904.01126.pdf
https://ui.adsabs.harvard.edu/abs/2019arXiv190401126S/abstract
https://arxiv.org/abs/1904.01126
https://dblp.uni-trier.de/db/journals/corr/corr1904.html#abs-1904-01126
https://www.microsoft.com/en-us/research/publication/scriptnet-neural-static-analysis-for-malicious-javascript-detection/

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

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
https://doaj.org/article/10eaad4b416443e996b2dfbad65517e4
https://ieeexplore.ieee.org/document/8482113
https://dblp.uni-trier.de/db/journals/access/access6.html#FangHLX18

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

Autoři: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa a další

Zdroj: Lecture Notes in Computer Science ISBN: 9783030922696

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

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

Autoři: Kouichirou Okada Samuel Ndichu Takeshi Misu a další

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

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

Přístupová URL adresa: https://ieeexplore.ieee.org/document/8489414
https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2018.html#NdichuOMO18

Malicious JavaScript Detection Method Based on Multilayer Perceptron

Autoři: Zhenyuan Guo

Zdroj: Advances in Intelligent Systems and Computing ISBN: 9783030627423

Přístupová URL adresa: https://link.springer.com/chapter/10.1007/978-3-030-62743-0_74

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

Autoři: Iwao Sasase Hiromu Asahina Shoya Morishige a další

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

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

Přístupová URL adresa: https://dblp.uni-trier.de/db/conf/apcc/apcc2017.html#MorishigeHAS17
https://keio.pure.elsevier.com/en/publications/obfuscated-malicious-javascript-detection-scheme-using-the-featur
https://doi.org/10.23919/APCC.2017.8303992

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

Autoři: Ban, Tao Ozawa, Seiichi Kim, Sangwook a další

Témata: Cyber security, Malicious JavaScript, Abstract syntax tree, Graph neural network, Conference Paper., AM.

URL: https://hdl.handle.net/20.500.14094/0100481143
https://doi.org/10.1007/978-3-030-92270-2
https://da.lib.kobe-u.ac.jp/da/kernel/0100481143/0100481143.pdf
978-3-030-92270-2