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

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

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Access URL: http://www.jucs.org/jucs_21_3/polymorphic_malicious_javascript_code/jucs_21_03_0369_0383_choi.pdf
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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
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https://doi.org/10.3217/jucs-021-03-0369

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

A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model.

Authors: Sun, Enbo Han, Jiaxuan Li, Yiquan et al.

Source: Future Internet; Jul2024, Vol. 16 Issue 7, p235, 18p

Subject Terms: MACHINE learning, RESEARCH personnel, INTERNET

Multi-Modal Deep Learning for Effective Malicious Webpage Detection.

Authors: Belfedhal, Alaa Eddine

Source: Revue d'Intelligence Artificielle; Aug2023, Vol. 37 Issue 4, p1005-1013, 9p

Subject Terms: MACHINE learning, DEEP learning, UNIFORM Resource Locators, INTERNET content, DATA security failures, INTERNET users

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)

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

Authors: Zhang, Zhining Wan, Liang Chu, Kun et al.

Source: PLoS ONE; 12/14/2022, Vol. 17 Issue 12, p1-27, 27p

Subject Terms: DEEP learning, JAVASCRIPT programming language, LINEAR network coding, FEATURE extraction, DATA mining, INFORMATION modeling, SYNTAX (Grammar)

DDIML: Explainable detection model for drive-by-download attacks.

Authors: Liu, Xiaole Huang, Cheng Fang, Yong

Source: Journal of Intelligent & Fuzzy Systems; 2022, Vol. 43 Issue 3, p3429-3442, 14p

Subject Terms: FEATURE extraction, DEEP learning, MACHINE learning, RANDOM forest algorithms, MALWARE, WEBSITES

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

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer's Point of View.

Authors: Levshun, Dmitry Chechulin, Andrey Kotenko, Igor

Source: Sensors (14248220); Jul2022, Vol. 22 Issue 13, p5063-N.PAG, 34p

Subject Terms: SMARTPHONES, INTELLIGENT transportation systems, MOBILE apps, SECURITY systems

An efficient malicious webpage static detection framework based on optimized Bayesian and hybrid machine learning.

Authors: Yang, Fan Zhu, Chaoqun Xu, Heng et al.

Source: Concurrency & Computation: Practice & Experience; May2022, Vol. 34 Issue 10, p1-15, 15p

Subject Terms: MACHINE learning, BLENDED learning, JAVASCRIPT programming language, WEBSITES, UNIFORM Resource Locators, FEATURE extraction

Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS.

Authors: Shakeela, Shaikh Shankar, N. Sai Reddy, P. Mohan et al.

Source: International Journal of Electronics & Telecommunications; 2021, Vol. 67 Issue 2, p267-275, 9p

Subject Terms: ANALYSIS of variance, DECISION trees, BIG data, CLASSIFICATION algorithms, CYBERTERRORISM

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

Abnormal behavior pattern mining for unknown threat detection.

Authors: Chang Choi Junho Choi Pankoo Kim

Source: Computer Systems Science & Engineering; Mar2017, Vol. 32 Issue 2, p171-177, 7p

Subject Terms: DATA mining, MALWARE, COMPUTER network security, COMPUTER hacking, COUNTERTERRORISM, CYBERTERRORISM

Why Web Servers Should Fear Their Clients.

Authors: Rodriguez, Juan D. Parra Posegga, Joachim

Source: Security & Privacy in Communication Networks: 11th International Conference, SecureComm 2015, Dallas, TX, USA, October 26-29, 2015, Revised Selected Papers; 2015, p401-417, 17p

An Evasion Resilient Approach to the Detection of Malicious PDF Files.

Authors: Maiorca, Davide Ariu, Davide Corona, Igino et al.

Source: Information Systems Security & Privacy; 2015, p68-85, 18p

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

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

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