Suchergebnisse - "Malicious JavaScript"

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

Autoren: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa et al.

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

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

Zugangs-URL: https://doaj.org/article/96b921f1abd0426fa01fe22f24802029

Malicious Javascript Detection based on Clustering Techniques

Autoren: Nguyen Hong Son Dung, Ha Thanh

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

Schlagwörter: 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

Autoren: Ammar Alazab Ansam Khraisat Moutaz Alazab et al.

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

Schlagwörter: 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

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

Malicious and Benign Webpages Dataset

Autoren: A.K. Singh

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

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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/34101d9e644f48669079516a660f049f

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

Autoren: Mamoru Mimura Ngoc Minh Phung

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

Malicious JavaScript Detection Based on Bidirectional LSTM Model

Autoren: Xuyan Song Chen Chen Baojiang Cui et al.

Quelle: Applied Sciences ; Volume 10 ; Issue 10 ; Pages: 3440

Schlagwörter: cyber security, malware detection, program slice, deep learning, malicious JavaScript, Bidirectional LSTM

Dateibeschreibung: application/pdf

Relation: Computing and Artificial Intelligence; https://dx.doi.org/10.3390/app10103440

Verfügbarkeit: https://doi.org/10.3390/app10103440

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

Autoren: Ndichu, Samuel Kim, Sangwook Ozawa, Seiichi et al.

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

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

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

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

Autoren: KyungHyun Han Seong Oun Hwang

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

Schlagwörter: 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

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

Detection and classification of malicious JavaScript via attack behavior modelling

Autoren: XUE, Yinxing WANG, Junjie LIU, Yang et al.

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

Schlagwörter: 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

Dateibeschreibung: application/pdf

Zugangs-URL: https://ink.library.smu.edu.sg/sis_research/4953

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

Autoren: Nagarjun, PMD Ahamad, Shaik Shakeel

Quelle: International Journal of Electrical and Computer Engineering (IJECE); Vol 9, No 2: April 2019; 1393-1398 ; 2722-2578 ; 2088-8708 ; 10.11591/ijece.v9i2

Schlagwörter: Computer and Informatics, Telecommunication, cross-site scripting, web application attacks, imageSubXSS, malicious javaScript, XSS attacks

Dateibeschreibung: application/pdf

Relation: https://ijece.iaescore.com/index.php/IJECE/article/view/12849/11769; https://ijece.iaescore.com/index.php/IJECE/article/view/12849

Verfügbarkeit: https://ijece.iaescore.com/index.php/IJECE/article/view/12849
https://doi.org/10.11591/ijece.v9i2.pp1393-1398

JSForce: A Forced Execution Engine for Malicious JavaScript Detection

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

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

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/pdf/1701.07860
http://arxiv.org/abs/1701.07860

Detecting obfuscated JavaScripts using machine learning

Autoren: Aebersold, Simon Kryszczuk, Krzysztof Paganoni, Sergio et al.

Schlagwörter: Detection, Malicious JavaScript, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 006: Spezielle Computerverfahren, Obfuscated JavaScript

Static detection of malicious JavaScript-bearing PDF documents

Autoren: Pavel Laskov The Pennsylvania State University CiteSeerX Archives

Weitere Verfasser: Pavel Laskov The Pennsylvania State University CiteSeerX Archives

Schlagwörter: 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

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

Analiza i detekcija zlonamjernog Javascript koda

Autoren: Pušelj, Dora Vuković, Marin

Weitere Verfasser: Pušelj, Dora Vuković, Marin

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

Dateibeschreibung: application/pdf

Zugangs-URL: 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

Studying JavaScript Security Through Static Analysis

Autoren: Fass, Aurore Backes, Michael

Weitere Verfasser: Fass, Aurore Backes, Michael

Schlagwörter: 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

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

Autoren: Samuel Ndichu Sangwook Kim Seiichi Ozawa et al.

Quelle: Applied Soft Computing. 84:105721

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

FEPDF: a robust feature extractor for malicious PDF detection

Autoren: M Li Y Liu M Yu et al.

Schlagwörter: 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

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

Методи виявлення шкідливих Javascript-сценаріїв

Weitere Verfasser: Грайворонський, Микола Владленович ELAKPI

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

Dateibeschreibung: application/pdf

Zugangs-URL: https://ela.kpi.ua/handle/123456789/31425

Detekce škodlivých webových stránek pomocí strojového učení

Detection of Malicious Websites using Machine Learning

Autoren: Šulák, Ladislav

Thesis Advisors: Černocký, Jan, Beneš, Karel

Schlagwörter: 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

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

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

Autoren: Hu, Xunchao

Quelle: Dissertations - ALL

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

Dateibeschreibung: application/pdf

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

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