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

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

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

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

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

Autoři: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa a další

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

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

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

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

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

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

Detection of Obfuscated Malicious JavaScript Code.

Autoři: Alazab, Ammar Khraisat, Ansam Alazab, Moutaz a další

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

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

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

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/

JSForce: A Forced Execution Engine for Malicious JavaScript Detection

Autoři: HU, Xunchao CHENG, Yao DUAN, Yue a další

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

Témata: 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 souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/pdf/1701.07860
http://arxiv.org/abs/1701.07860
https://dblp.uni-trier.de/db/journals/corr/corr1701.html#HuCDHY17
https://link.springer.com/content/pdf/10.1007%2F978-3-319-78813-5_37.pdf
http://ui.adsabs.harvard.edu/abs/2017arXiv170107860H/abstract
https://rd.springer.com/chapter/10.1007/978-3-319-78813-5_37
https://arxiv.org/abs/1701.07860
https://link.springer.com/chapter/10.1007/978-3-319-78813-5_37

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

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

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