Výsledky vyhľadávania - Malicious JavaScript detection

  1. 1

    Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection Autor Alex, Scaria, Dhiliphan Rajkumar, T.

    ISSN: 0884-8173, 1098-111X
    Vydavateľské údaje: New York John Wiley & Sons, Inc 01.12.2021
    “… The malicious script, like, JavaScript, is a major threat to computer networks in terms of network security…”
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  2. 2

    ZipAST: Enhancing malicious JavaScript detection with sequence compression Autor Chen, Zixian, Wang, Weiping, Qin, Yan, Zhang, Shigeng

    ISSN: 0167-4048
    Vydavateľské údaje: Elsevier Ltd 01.06.2025
    Vydané v Computers & security (01.06.2025)
    “… With the advancements in deep learning technologies, deep learning networks have shown the ability to automatically learn strong feature representations from malicious JavaScript…”
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  3. 3

    JSContana: Malicious JavaScript detection using adaptable context analysis and key feature extraction Autor Huang, Yunhua, Li, Tao, Zhang, Lijia, Li, Beibei, Liu, Xiaojie

    ISSN: 0167-4048, 1872-6208
    Vydavateľské údaje: Elsevier Ltd 01.05.2021
    Vydané v Computers & security (01.05.2021)
    “… Although malicious JavaScript detection methods are becoming increasingly effective, the existing methods based on feature matching or static word embeddings are difficult to detect different…”
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  4. 4

    JStrong: Malicious JavaScript detection based on code semantic representation and graph neural network Autor Fang, Yong, Huang, Chaoyi, Zeng, Minchuan, Zhao, Zhiying, Huang, Cheng

    ISSN: 0167-4048, 1872-6208
    Vydavateľské údaje: Amsterdam Elsevier Ltd 01.07.2022
    Vydané v Computers & security (01.07.2022)
    “… However, the attacker uses the dynamic characteristics of the JavaScript language to embed malicious code into web pages to achieve the purpose of smuggling, redirection, and so on…”
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  5. 5

    Research on Malicious JavaScript Detection Technology Based on LSTM Autor Fang, Yong, Huang, Cheng, Liu, Liang, Xue, Min

    ISSN: 2169-3536, 2169-3536
    Vydavateľské údaje: Piscataway IEEE 2018
    Vydané v IEEE access (2018)
    “… By analyzing the existing researches on malicious JavaScript detection, a malicious JavaScript detection model based on LSTM…”
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  6. 6

    Malicious JavaScript Detection Based on Bidirectional LSTM Model Autor Song, Xuyan, Chen, Chen, Cui, Baojiang, Fu, Junsong

    ISSN: 2076-3417, 2076-3417
    Vydavateľské údaje: Basel MDPI AG 01.05.2020
    Vydané v Applied sciences (01.05.2020)
    “… To solve this problem, many learning-based methods for malicious JavaScript detection are being explored…”
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  7. 7

    Detecting malicious JavaScript code based on semantic analysis Autor Fang, Yong, Huang, Cheng, Su, Yu, Qiu, Yaoyao

    ISSN: 0167-4048, 1872-6208
    Vydavateľské údaje: Amsterdam Elsevier Ltd 01.06.2020
    Vydané v Computers & security (01.06.2020)
    “… However, attackers use the dynamics feature of JavaScript language to embed malicious code into web pages for the purpose of drive-by-download, redirection, etc…”
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  8. 8

    Spider bird swarm algorithm with deep belief network for malicious JavaScript detection Autor Alex, Scaria, Dhiliphan Rajkumar, T

    ISSN: 0167-4048, 1872-6208
    Vydavateľské údaje: Amsterdam Elsevier Ltd 01.08.2021
    Vydané v Computers & security (01.08.2021)
    “…) algorithm for malicious JavaScript detection. The proposed S-BSA is designed by the integration of Spider Monkey Optimization (SMO…”
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  9. 9

    Detection of Obfuscated Malicious JavaScript Code Autor Alazab, Ammar, Khraisat, Ansam, Alazab, Moutaz, Singh, Sarabjot

    ISSN: 1999-5903, 1999-5903
    Vydavateľské údaje: Basel MDPI AG 01.08.2022
    Vydané v Future internet (01.08.2022)
    “… To secure Internet users, an adequate intrusion-detection system (IDS) for malicious JavaScript must be developed…”
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  10. 10

    JACLNet:Application of adaptive code length network in JavaScript malicious code detection Autor Zhang, Zhining, Wan, Liang, Chu, Kun, Li, Shusheng, Wei, Haodong, Tang, Lu

    ISSN: 1932-6203, 1932-6203
    Vydavateľské údaje: United States Public Library of Science 14.12.2022
    Vydané v PloS one (14.12.2022)
    “…Currently, JavaScript malicious code detection methods are becoming more and more…”
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  11. 11

    TransAST: A Machine Translation-Based Approach for Obfuscated Malicious JavaScript Detection Autor Qin, Yan, Wang, Weiping, Chen, Zixian, Song, Hong, Zhang, Shigeng

    ISSN: 2158-3927
    Vydavateľské údaje: IEEE 01.01.2023
    “…As an essential part of the website, JavaScript greatly enriches its functions. At the same time, JavaScript has become the most common attack payload on malicious website…”
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  12. 12

    ScriptNet: Neural Static Analysis for Malicious JavaScript Detection Autor Stokes, Jack W., Agrawal, Rakshit, McDonald, Geoff, Hausknecht, Matthew

    ISSN: 2155-7586
    Vydavateľské údaje: IEEE 01.11.2019
    “… For internet-scale processing, static analysis offers substantial computing efficiencies. We propose the ScriptNet system for neural malicious JavaScript detection which is based on static analysis…”
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  13. 13

    Detection of malicious javascript on an imbalanced dataset Autor Phung, Ngoc Minh, Mimura, Mamoru

    ISSN: 2542-6605, 2542-6605
    Vydavateľské údaje: Elsevier B.V 01.03.2021
    “…In order to be able to detect new malicious JavaScript with low cost, methods with machine learning techniques have been proposed and gave positive results…”
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  14. 14

    An Approach for Malicious JavaScript Detection Using Adaptive Taylor Harris Hawks Optimization-Based Deep Convolutional Neural Network Autor Alex, Scaria, T, Dhiliphan Rajkumar

    ISSN: 1947-3532, 1947-3540
    Vydavateľské údaje: IGI Global 20.05.2022
    “… This paper devises a novel technique for detecting malicious JavaScript. Here, JavaScript codes are fed to the feature extraction phase for extracting the noteworthy…”
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  15. 15

    Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation Autor Rozi, Muhammad Fakhrur, Ban, Tao, Ozawa, Seiichi, Yamada, Akira, Takahashi, Takeshi, Kim, Sangwook, Inoue, Daisuke

    ISSN: 2169-3536, 2169-3536
    Vydavateľské údaje: Piscataway IEEE 2023
    Vydané v IEEE Access (2023)
    “…Malicious JavaScript code in web applications poses a significant threat as cyber attackers exploit it to perform various malicious activities…”
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  16. 16

    A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors Autor Ndichu, Samuel, Kim, Sangwook, Ozawa, Seiichi, Misu, Takeshi, Makishima, Kazuo

    ISSN: 1568-4946, 1872-9681
    Vydavateľské údaje: Elsevier B.V 01.11.2019
    Vydané v Applied soft computing (01.11.2019)
    “… Most of these websites use JavaScript (JS) to create dynamic content. The exploitation of vulnerabilities in servers, plugins, and other third-party systems enables the insertion of malicious codes into website…”
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  17. 17

    Malicious JavaScript Code Detection Based on Hybrid Analysis Autor He, Xincheng, Xu, Lei, Cha, Chunliu

    ISSN: 2640-0715
    Vydavateľské údaje: IEEE 01.12.2018
    “… However, since the heavy use of obfuscation techniques, many methods no longer apply to malicious JavaScript code detection, and it has been a huge challenge to de-obfuscate obfuscated malicious Java…”
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  18. 18

    Adaptive Spider Bird Swarm Algorithm-Based Deep Recurrent Neural Network for Malicious JavaScript Detection Using Box-Cox Transformation Autor Alex, Scaria, Rajkumar, T Dhiliphan

    ISSN: 1942-3926, 1942-3934
    Vydavateľské údaje: Hershey IGI Global 01.10.2020
    “…) is proposed for detecting the malicious JavaScript codes in web applications. However, the proposed adaptive SBSA is designed by integrating the adaptive concept with the bird swarm algorithm (BSA…”
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  19. 19

    Detecting Malicious Javascript in PDF through Document Instrumentation Autor Daiping Liu, Haining Wang, Stavrou, Angelos

    ISSN: 1530-0889
    Vydavateľské údaje: IEEE 01.06.2014
    “… In this paper, we propose a context-aware approach for detection and confinement of malicious Javascript in PDF…”
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  20. 20

    Malicious JavaScript Detection by Features Extraction Autor Gerardo Canfora, Francesco Mercaldo, Corrado Aaron Visaggio

    ISSN: 1897-7979, 2084-4840
    Vydavateľské údaje: Wroclaw University of Science and Technology 01.06.2015
    “… Existing techniques for detecting malicious JavaScripts could fail for different reasons…”
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