Search Results - Malicious JavaScript code detection

Malicious JavaScript Code Detection Based on Hybrid Analysis

Authors: Chunliu Cha Xincheng He Lei Xu

Source: 2018 25th Asia-Pacific Software Engineering Conference (APSEC). :365-374

Subject Terms: 13. Climate action, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Access URL: http://dblp.uni-trier.de/db/conf/apsec/apsec2018.html#He0C18
https://dblp.uni-trier.de/db/conf/apsec/apsec2018.html#He0C18

Detection of Obfuscated Malicious JavaScript Code.

Authors: Alazab, Ammar Khraisat, Ansam Alazab, Moutaz et al.

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

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

Behavior Analysis Usage with Behavior Tures Adoption for Malicious Code Detection on JAVASCRIPT Scenarios Example

Authors: Y. M. Tumanov S.V. Gavrilyuk

Source: Безопасность информационных технологий, Vol 17, Iss 1, Pp 115-117 (2010)

Subject Terms: malicious code detection, behavior analysis, behavior signature, Information technology, T58.5-58.64, Information theory, Q350-390

File Description: electronic resource

Relation: https://bit.mephi.ru/index.php/bit/article/view/791; https://doaj.org/toc/2074-7128; https://doaj.org/toc/2074-7136

Access URL: https://doaj.org/article/1ee7b69964a2440c926708454e78aada

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/e2fa7807e7e643e98d2b76506dbc7dd6

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

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10255646/; https://doaj.org/toc/2169-3536

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

Polymorphic Malicious JavaScript Code Detection for APT Attack Defence

Authors: Choi,Junho Choi,Chang You,Ilsun et al.

Source: JUCS-Journal of Universal Computer Science 21(3): 369-383

Subject Terms: conceptual graph, 0202 electrical engineering, electronic engineering, information engineering, 0102 computer and information sciences, 02 engineering and technology, malicious code detection, APT attack defence, 01 natural sciences

File Description: text/html

Access URL: http://www.jucs.org/jucs_21_3/polymorphic_malicious_javascript_code/jucs_21_03_0369_0383_choi.pdf
https://www.jucs.org/jucs_21_3/polymorphic_malicious_javascript_code/jucs_21_03_0369_0383_choi.pdf
http://www.jucs.org/doi?doi=10.3217/jucs-021-03-0369
https://dblp.uni-trier.de/db/journals/jucs/jucs21.html#ChoiCYK15
https://lib.jucs.org/article/23035
https://lib.jucs.org/article/23035/download/pdf/
https://lib.jucs.org/article/23035/
https://lib.jucs.org/article/23035/download/pdf/
https://doi.org/10.3217/jucs-021-03-0369

Research on Malicious JavaScript Detection Technology Based on LSTM

Authors: Yong Fang Cheng Huang Liang Liu et al.

Source: IEEE Access, Vol 6, Pp 59118-59125 (2018)

Subject Terms: JavaScript, malicious code detection, bytecode, word vector, LSTM, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/8482113/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/10eaad4b416443e996b2dfbad65517e4

JACLNet:Application of adaptive code length network in JavaScript malicious code detection.

Authors: Zhang Z Wan L Chu K et al.

Source: PloS one [PLoS One] 2022 Dec 14; Vol. 17 (12), pp. e0277891. Date of Electronic Publication: 2022 Dec 14 (Print Publication: 2022).

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

MeSH Terms: Algorithms* , Computer Security*

An improved bytecode similarity measurement for malicious JavaScript code detection

一個改良位元組碼相似度計算之JavaScript惡意程式偵測方法

Authors: Jun-Xu Yang 楊鈞旭

Thesis Advisors: Jun-Xu Yang 楊鈞旭

File Description: 31

Availability: http://ndltd.ncl.edu.tw/handle/dyhtkn

Malicious JavaScript Detection by Features Extraction

Authors: Gerardo Canfora Francesco Mercaldo Corrado Aaron Visaggio

Source: e-Informatica Software Engineering Journal, Vol 8, Iss 1, Pp 65-78 (2015)

Subject Terms: malicious JavaScripts, Computer software, QA76.75-76.765

File Description: electronic resource

Relation: http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_8/eInformatica2014Art5.pdf; https://doaj.org/toc/1897-7979; https://doaj.org/toc/2084-4840

Access URL: https://doaj.org/article/ffb1293311cf45bfb4865f519bf6927b

Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement

Authors: Samuel Ndichu Sangwook Kim Seiichi Ozawa

Source: CAAI Transactions on Intelligence Technology (2020)

Subject Terms: invasive software, java, internet, feature extraction, text analysis, vectors, learning (artificial intelligence), formatted js code, deobfuscation methods, unpacking, dud-preprocessed obfuscated malicious js codes, term frequency–inverse document frequency model, long short-term memory model, paragraph vector models, obfuscated benign js codes, learned feature vectors, fasttext model, js code editor, multilayer obfuscation, original js code, undetectable code, obscure code, machine learning models detection, Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765

File Description: electronic resource

Relation: https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0026; https://doaj.org/toc/2468-2322

Access URL: https://doaj.org/article/1ec26bbd25b54437bb5faa62e4cf5359

A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

Authors: Md. Fahimuzzman Sohan Anas Basalamah

Source: IEEE Access, Vol 8, Pp 190539-190552 (2020)

Subject Terms: Cybersecurity, systematic literature review, malicious code detection, javascript attacks, javascript malware detection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9226527/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/166a15a34eb547178523936e1969c86f

PDF Malicious Indicators Extraction Technique Based on Improved Symbolic Execution

Authors: SONG Enzhou, HU Tao, YI Peng, WANG Wenbo

Source: Jisuanji kexue, Vol 51, Iss 7, Pp 389-396 (2024)

Subject Terms: malicious documents, javascript code, indicator extraction, symbolic execution, code rewriting, constraint solving optimization, Computer software, QA76.75-76.765, Technology (General), T1-995

File Description: electronic resource

Relation: https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2024-51-7-389.pdf; https://doaj.org/toc/1002-137X

Access URL: https://doaj.org/article/79a039d822d2491d9e19425eaac35223

The power of obfuscation techniques in malicious JavaScript code: A measurement study.

Authors: Xu, Wei Zhang, Fangfang Zhu, Sencun

Source: 2012 7th International Conference on Malicious & Unwanted Software; 1/ 1/2012, p9-16, 8p

Malicious PDF Detection Model against Adversarial Attack Built from Benign PDF Containing JavaScript

Authors: Ah Reum Kang Young-Seob Jeong Se Lyeong Kim et al.

Source: Applied Sciences, Vol 9, Iss 22, p 4764 (2019)

Subject Terms: malicious pdf, malware, detection, machine-learning, adversarial attack, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/9/22/4764; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/5006a5fd41d44a0cb4c5095ed957c2ce

Context-Aware Malicious Code Detection

Authors: Gu, Boxuan

Subject Terms: Computer Science, intrusion detection, malicious code detection, web security, javascript security, smartphone security, android security, information flow tracking, information leaking, worm detection, shellcode, shellcode detection

Availability: http://rave.ohiolink.edu/etdc/view?acc_num=osu1354523604

Analysis and Identification of Malicious JavaScript Code.

Authors: Fraiwan, Mohammad Al-Salman, Rami Khasawneh, Natheer et al.

Source: Information Security Journal: A Global Perspective; 2012, Vol. 21 Issue 1, p1-11, 11p

Subject Terms: JAVASCRIPT programming language, MALWARE, SCRIPTING languages (Computer science), WEBSITES, PHISHING

A deep learning approach for detecting malicious JavaScript code.

Authors: Wang, Yao Cai, Wan-dong Wei, Peng-cheng

Source: Security & Communication Networks; 7/25/2016, Vol. 9 Issue 11, p1520-1534, 15p

Subject Terms: JAVASCRIPT programming language, MACHINE learning, DATA transmission systems, LOGISTIC regression analysis

Intelligent Defense against Malicious JavaScript Code.

Authors: Krueger, Tammo Rieck, Konrad

Source: PIK - Praxis der Informationsverarbeitung und Kommunikation; 2012, Vol. 35 Issue 1, p54-60, 7p, 6 Diagrams, 4 Charts

Subject Terms: JAVASCRIPT programming language, SUPPORT vector machines, APPLICATION software, MALWARE, INSTRUCTIONAL systems, COMPUTER architecture, WEBSITES, INFORMATION technology

Reviews & Products: DATA Security & Privacy Law: Combating Cyberthreats (Book)

JsSandbox: A Framework for Analyzing the Behavior of Malicious JavaScript Code using Internal Function Hooking.

Authors: Hyoung Chun Kim Young Han Choi Dong Hoon Lee

Source: KSII Transactions on Internet & Information Systems. Feb2012, Vol. 6 Issue 2, p766-783. 18p. 6 Diagrams, 4 Charts, 1 Graph.

Subject Terms: Web browser security, JavaScript programming language, Malware, Website access control, Sandboxes (Computer science)