Search Results - "malicious JavaScripts"

MalJs: Lexical, Structural and Behavioral Analysis of Malicious JavaScripts Using Ensemble Classifier

Authors: Surendran K Prabaharan Poornachandran Aravind Ashok Nair et al.

Source: Communications in Computer and Information Science ISBN: 9789811027376

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

Malicious JavaScript Detection by Features Extraction

Authors: Gerardo Canfora Francesco Mercaldo Corrado Aaron Visaggio et al.

Contributors: Gerardo Canfora Francesco Mercaldo Corrado Aaron Visaggio et al.

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

Subject Terms: QA76.75-76.765, malicious web pages, JavaScript, security, static analysis, machine learning, 0202 electrical engineering, electronic engineering, information engineering, informatyka - czasopisma, Computer software, 02 engineering and technology, malicious JavaScripts, 3. Good health

File Description: application/pdf

Access URL: https://doaj.org/article/ffb1293311cf45bfb4865f519bf6927b
https://hdl.handle.net/11695/115414
https://doi.org/10.5277/e-Inf140105

Automatic attack signature generation technology for malicious javascript.

Authors: Yoon, Soojin Jung, JongHun Noh, MyoungSun et al.

Source: Proceedings of 2014 International Conference on Modelling, Identification & Control; 2014, p351-354, 4p

Detecting obfuscated JavaScripts using machine learning

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

Index Terms: Obfuscated JavaScript, Detection, Malicious JavaScript, Machine learning, 006: Spezielle Computerverfahren, Konferenz: Paper, Text

URL: https://doi.org/10.21256/zhaw-3848
ICIMP 2016 the Eleventh International Conference on Internet Monitoring and Protection : May 22-26, 2016, Valencia, Spain
ICIMP 2016 the Eleventh International Conference on Internet Monitoring and Protection, Valencia, Spain, 22-26 May 2016

Preventing Privacy-Violating Information Flows in JavaScript Applications Using Dynamic Labelling

Authors: Ghosal, Sandip Shyamasundar, R. K.

Index Terms: Software Engineering, Programvaruteknik, Computer Sciences, Datavetenskap (datalogi), Conference paper, info:eu-repo/semantics/conferenceObject, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-498072
Lecture Notes in Computer Science, 0302-9743 ; 13784
Information Systems Security : 18th International Conference, ICISS 2022, Tirupati, India, December 16–20, 2022, Proceedings, p. 202-219

Next-generation antivirus for JavaScript malware detection based on dynamic features.

Authors: de Lima, Sidney M. L. Souza, Danilo M. Pinheiro, Ricardo P. et al.

Source: Knowledge & Information Systems; Feb2024, Vol. 66 Issue 2, p1337-1370, 34p

Subject Terms: ANTIVIRUS software, JAVASCRIPT programming language, MALWARE, INFORMATION technology security, MACHINE learning, DIGITAL forensics, VIRUS inhibitors

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

An ensemble-based stegware detection system for information hiding malware attacks.

Authors: Monika, A. Eswari, R.

Source: Journal of Ambient Intelligence & Humanized Computing; Apr2023, Vol. 14 Issue 4, p4401-4417, 17p

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, INTRUSION detection systems (Computer security), MACHINE learning, INTERNET users, CYBERTERRORISM, MALWARE, WEBSITES

Raising the Bar Really High: An MTD Approach to Protect Data in Embedded Browsers

Authors: Fadi Mohsen Haadi Jafaarian

Source: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). :786-794

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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

Authors: Han, KyungHyun Hwang, Seong Oun

Source: Applied Sciences (2076-3417); Sep2020, Vol. 10 Issue 17, p6116, 19p

Subject Terms: JAVASCRIPT programming language, TOKENS

Malicious JavaScript Detection Based on Bidirectional LSTM Model.

Authors: Song, Xuyan Chen, Chen Cui, Baojiang et al.

Source: Applied Sciences (2076-3417); May2020, Vol. 10 Issue 10, p3440, 21p

Subject Terms: JAVASCRIPT programming language, FORGERY

JStrack: Enriching Malicious JavaScript Detection Based on AST Graph Analysis and Attention Mechanism.

Authors: Ban, Tao Ozawa, Seiichi Kim, Sangwook et al.

Index Terms: 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

Detection of Drive-by Download Attacks Using Machine Learning Approach.

Authors: Aldwairi, Monther Hasan, Musaab Balbahaith, Zayed

Source: International Journal of Information Security & Privacy; Oct-Dec2017, Vol. 11 Issue 4, p16-28, 13p

FrontMatter.

Source: Security in Computing & Communications (9789811027376); 2016, pI-XXII, 22p

A set of features to detect web security threats.

Authors: Canfora, Gerardo Visaggio, Corrado

Source: Journal of Computer Virology & Hacking Techniques; Nov2016, Vol. 12 Issue 4, p243-261, 19p

Detection of Malicious Web Pages Using System Calls Sequences.

Authors: Canfora, Gerardo Medvet, Eric Mercaldo, Francesco et al.

Source: Availability, Reliability & Security in Information Systems; 2014, p226-238, 13p

MJBlocker: A Lightweight and Run-Time Malicious JavaScript Extensions Blocker.

Authors: Wang, Pingjian Wang, Lei Xiang, Ji et al.

Source: 2013 IEEE 7th International Conference on Software Security & Reliability; 2013, p119-128, 10p

Light-Weight CSRF Protection by Labeling User-Created Contents.

Authors: Sung, Yin-Chang Cho, Michael Cheng Yi Wang, Chi-Wei et al.

Source: 2013 IEEE 7th International Conference on Software Security & Reliability; 2013, p60-69, 10p