Search Results - "malicious javaScript"

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

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

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

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

Access URL: https://doaj.org/article/96b921f1abd0426fa01fe22f24802029

Malicious Javascript Detection based on Clustering Techniques

Authors: Nguyen Hong Son Dung, Ha Thanh

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

Subject Terms: 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

Authors: Ammar Alazab Ansam Khraisat Moutaz Alazab et al.

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

Subject Terms: 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

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

Malicious and Benign Webpages Dataset

Authors: A.K. Singh

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/34101d9e644f48669079516a660f049f

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

Authors: Mamoru Mimura Ngoc Minh Phung

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

Malicious JavaScript Detection Based on Bidirectional LSTM Model

Authors: Xuyan Song Chen Chen Baojiang Cui et al.

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

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

File Description: application/pdf

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

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

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

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

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

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

Availability: 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

Authors: KyungHyun Han Seong Oun Hwang

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

Subject Terms: 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

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

Detection and classification of malicious JavaScript via attack behavior modelling

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

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

Subject Terms: 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

File Description: application/pdf

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

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

Authors: Nagarjun, PMD Ahamad, Shaik Shakeel

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

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

File Description: application/pdf

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

Availability: 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

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

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

Subject Terms: 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)

File Description: application/pdf

Access URL: http://arxiv.org/pdf/1701.07860
http://arxiv.org/abs/1701.07860

Detecting obfuscated JavaScripts using machine learning

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

Subject Terms: 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

Authors: Pavel Laskov The Pennsylvania State University CiteSeerX Archives

Contributors: Pavel Laskov The Pennsylvania State University CiteSeerX Archives

Subject Terms: 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

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

Analiza i detekcija zlonamjernog Javascript koda

Authors: Pušelj, Dora Vuković, Marin

Contributors: Pušelj, Dora Vuković, Marin

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

File Description: application/pdf

Access 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

Authors: Fass, Aurore Backes, Michael

Contributors: Fass, Aurore Backes, Michael

Subject Terms: 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

Authors: Samuel Ndichu Sangwook Kim Seiichi Ozawa et al.

Source: Applied Soft Computing. 84:105721

Subject Terms: 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

Authors: M Li Y Liu M Yu et al.

Subject Terms: 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

Availability: 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

Contributors: Грайворонський, Микола Владленович ELAKPI

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

File Description: application/pdf

Access URL: https://ela.kpi.ua/handle/123456789/31425

Detekce škodlivých webových stránek pomocí strojového učení

Detection of Malicious Websites using Machine Learning

Authors: Šulák, Ladislav

Thesis Advisors: Černocký, Jan, Beneš, Karel

Subject Terms: 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

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

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

Authors: Hu, Xunchao

Source: Dissertations - ALL

Subject Terms: Cybersecurity, Exploit Diagnosis, Exploit Mitigation, Forced Execution, Malicious JavaScript, Engineering

File Description: application/pdf

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

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