Výsledky vyhľadávania - "Malicious JavaScript"

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

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

Detection of Obfuscated Malicious JavaScript Code

Autori: Ammar Alazab Ansam Khraisat Moutaz Alazab a ďalší

Zdroj: Future Internet, Vol 14, Iss 8, p 217 (2022)

Predmety: malware detection, intrusion detection, obfuscated malicious, machine learning, malicious JavaScript, Information technology, T58.5-58.64

Relation: https://www.mdpi.com/1999-5903/14/8/217; https://doaj.org/toc/1999-5903; https://doaj.org/article/e2fa7807e7e643e98d2b76506dbc7dd6

Dostupnosť: https://doi.org/10.3390/fi14080217
https://doaj.org/article/e2fa7807e7e643e98d2b76506dbc7dd6

Malicious and Benign Webpages Dataset

Autori: A.K. Singh

Zdroj: Data in Brief, Vol 32, Iss , Pp 106304- (2020)

Predmety: Web security, Malicious webpages, Machine learning, Deep learning, Malicious JavaScript, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390

Popis súboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352340920311987; https://doaj.org/toc/2352-3409

Prístupová URL adresa: https://doaj.org/article/34101d9e644f48669079516a660f049f

A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors

Autori: Ndichu, Samuel Kim, Sangwook Ozawa, Seiichi a ďalší

Predmety: Cybersecurity, Machine learning, Doc2Vec, Malicious JavaScript detection, Feature learning, Abstract Syntax Tree

Relation: info:doi/10.1016/j.asoc.2019.105721

Dostupnosť: http://www.lib.kobe-u.ac.jp/handle_kernel/90006845
http://www.lib.kobe-u.ac.jp/repository/90006845.pdf

Malicious JavaScript Detection Based on Bidirectional LSTM Model

Autori: Xuyan Song Chen Chen Baojiang Cui a ďalší

Zdroj: Applied Sciences, Vol 10, Iss 3440, p 3440 (2020)

Predmety: cyber security, malware detection, program slice, deep learning, malicious JavaScript, Bidirectional LSTM, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/10/10/3440; https://doaj.org/toc/2076-3417; https://doaj.org/article/4cd8785c01064d518ea72e49e9c81cf6

Dostupnosť: https://doi.org/10.3390/app10103440
https://doaj.org/article/4cd8785c01064d518ea72e49e9c81cf6

Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens

Autori: KyungHyun Han Seong Oun Hwang

Zdroj: Applied Sciences, Vol 10, Iss 6116, p 6116 (2020)

Predmety: malicious JavaScript, abstract syntax tree, static analysis, obfuscation detection, redirection detection, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/10/17/6116; https://doaj.org/toc/2076-3417; https://doaj.org/article/7b5df539245b4bc1ab99a72b2bb9f1e9

Dostupnosť: https://doi.org/10.3390/app10176116
https://doaj.org/article/7b5df539245b4bc1ab99a72b2bb9f1e9

Evaluation of a cGAN Model and Random Seed Oversampling on Imbalanced JavaScript Datasets

Autori: Mamoru Mimura Ngoc Minh Phung

Zdroj: Journal of Information Processing. 2022, 30:591

ImageSubXSS: an image substitute technique to prevent Cross-Site Scripting attacks

Autori: Nagarjun, PMD Ahamad, Shaik Shakeel

Predmety: Cross-site scripting, ImageSubXSS, Malicious JavaScript, Web application attacks, XSS attacks

Relation: https://zenodo.org/records/4066153; oai:zenodo.org:4066153

Dostupnosť: https://doi.org/10.11591/ijece.v9i2.pp1393-1398
https://zenodo.org/records/4066153

Detection and classification of malicious JavaScript via attack behavior modelling

Autori: XUE, Yinxing WANG, Junjie LIU, Yang a ďalší

Zdroj: Proceedings of the 2015 International Symposium on Software Testing and Analysis. :48-59

Predmety: malware detection, L, Programming Languages and Compilers, 0202 electrical engineering, electronic engineering, information engineering, Software Engineering, 02 engineering and technology, malicious JavaScript, behavior modelling, 3. Good health

Popis súboru: application/pdf

Prístupová URL adresa: https://ink.library.smu.edu.sg/sis_research/4953
https://ink.library.smu.edu.sg/sis_research/4953/
https://dl.acm.org/citation.cfm?id=2771814
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5956&context=sis_research
https://doi.org/10.1145/2771783.2771814
https://dblp.uni-trier.de/db/conf/issta/issta2015.html#XueWLXSC15

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
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

Detecting obfuscated JavaScripts using machine learning

Autori: Aebersold, Simon Kryszczuk, Krzysztof Paganoni, Sergio a ďalší

Zdroj: ISBN:978-1-61208-475-6.

Predmety: Obfuscated JavaScript, Detection, Malicious JavaScript, Machine learning, info:eu-repo/classification/ddc/006

Popis súboru: application/pdf

Relation: https://www.thinkmind.org/index.php?view=article&articleid=icimp_2016_1_20_30023; https://digitalcollection.zhaw.ch/handle/11475/7717

Dostupnosť: https://doi.org/10.21256/zhaw-3848
https://www.thinkmind.org/index.php?view=article&articleid=icimp_2016_1_20_30023
https://digitalcollection.zhaw.ch/handle/11475/7717

Static detection of malicious JavaScript-bearing PDF documents

Autori: Pavel Laskov The Pennsylvania State University CiteSeerX Archives

Prispievatelia: Pavel Laskov The Pennsylvania State University CiteSeerX Archives

Predmety: Categories and Subject Descriptors D.4.6 [Software, Operating Systems—Security and Protection, I.2.6 [Computing Methodologies, Artificial Intelligence—Learning Keywords Malware detection, malicious JavaScript, PDF documents, machine

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.369.7192

Dostupnosť: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.369.7192

Analiza i detekcija zlonamjernog Javascript koda

Autori: Pušelj, Dora Vuković, Marin

Prispievatelia: Pušelj, Dora Vuković, Marin

Predmety: JavaScript, machine learning, AST stablo, TECHNICAL SCIENCES. Computing, TEHNIČKE ZNANOSTI. Računarstvo, AST tree, zlonamjerni JavaScript, malicious JavaScript, strojno učenje

Popis súboru: application/pdf

Prístupová URL adresa: https://repozitorij.fer.unizg.hr/islandora/object/fer:8557
https://urn.nsk.hr/urn:nbn:hr:168:452696
https://repozitorij.fer.unizg.hr/islandora/object/fer:8557/datastream/PDF

A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors

Autori: Samuel Ndichu Sangwook Kim Seiichi Ozawa a ďalší

Zdroj: Applied Soft Computing. 84:105721

Predmety: Cybersecurity, Machine learning, Malicious JavaScript detection, Feature learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Abstract Syntax Tree, Doc2Vec

Prístupová URL adresa: https://ci.nii.ac.jp/naid/120006811119
https://dblp.uni-trier.de/db/journals/asc/asc84.html#NdichuKOMM19
https://www.sciencedirect.com/science/article/pii/S1568494619305022

Studying JavaScript Security Through Static Analysis

Autori: Fass, Aurore Backes, Michael

Prispievatelia: Fass, Aurore Backes, Michael

Predmety: ddc:004, JavaScript, Malicious JavaScript, Browser Extensions, Computersicherheit, Statische Analyse, Static Analysis, Data Flow, Control Flow, Machine Learning, Web Security, Vulnerable JavaScript, AST, Adversarial Attacks

Prístupová URL adresa: https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31484
https://publications.cispa.saarland/id/eprint/3471
https://publikationen.sulb.uni-saarland.de/bitstream/20.500.11880/31484/1/Thesis-final_Fass.pdf

FEPDF: a robust feature extractor for malicious PDF detection

Autori: M Li Y Liu M Yu a ďalší

Predmety: Artificial intelligence not elsewhere classified, malware detection, malicious JavaScript, PDF documents, code obfuscation, School of Information Technology, 4604 Cybersecurity and privacy, 4612 Software engineering

Relation: http://hdl.handle.net/10536/DRO/DU:30104365; https://figshare.com/articles/conference_contribution/FEPDF_a_robust_feature_extractor_for_malicious_PDF_detection/20825080

Dostupnosť: http://hdl.handle.net/10536/DRO/DU:30104365
https://figshare.com/articles/conference_contribution/FEPDF_a_robust_feature_extractor_for_malicious_PDF_detection/20825080

Detekce škodlivých webových stránek pomocí strojového učení

Detection of Malicious Websites using Machine Learning

Autori: Šulák, Ladislav

Thesis Advisors: Černocký, Jan, Beneš, Karel

Predmety: malware, škodlivý software, malicious JavaScript, deep learning, web-based threats, škodlivý JavaScript, detekcia škodlivých URL, static analysis, statická analýya, drive-by-download, strojové učenie, webové hrozby, machine learning, hlboké učenie, malicious URL detection

Dostupnosť: http://www.nusl.cz/ntk/nusl-385990

Методи виявлення шкідливих Javascript-сценаріїв

Prispievatelia: Грайворонський, Микола Владленович ELAKPI

Predmety: machine training, методи виявлення, classification, static analysis, шкідливий програмний засіб, malicious javascript, класифікація, javascript, детектування, статичний аналіз, машинне навчання, detection methods

Popis súboru: application/pdf

Prístupová URL adresa: https://ela.kpi.ua/handle/123456789/31425

DETECTION, DIAGNOSIS AND MITIGATION OF MALICIOUS JAVASCRIPT WITH ENRICHED JAVASCRIPT EXECUTIONS

Autori: Hu, Xunchao

Zdroj: Dissertations - ALL

Predmety: Cybersecurity, Exploit Diagnosis, Exploit Mitigation, Forced Execution, Malicious JavaScript, Engineering

Popis súboru: application/pdf

Relation: https://surface.syr.edu/etd/802; https://surface.syr.edu/context/etd/article/1803/viewcontent/HU_XUNCHAO.pdf

Dostupnosť: https://surface.syr.edu/etd/802
https://surface.syr.edu/context/etd/article/1803/viewcontent/HU_XUNCHAO.pdf