Výsledky vyhledávání - Characterization and Detection of Android Malware

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    KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization Autor Guerra-Manzanares, Alejandro, Bahsi, Hayretdin, Nõmm, Sven

    ISSN: 0167-4048, 1872-6208
    Vydáno: Amsterdam Elsevier Ltd 01.11.2021
    Vydáno v Computers & security (01.11.2021)
    “…). Critical factors to take into account when aiming to build more effective, robust, and long-lasting Android malware detection systems…”
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    Journal Article
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    Android malware concept drift using system calls: Detection, characterization and challenges Autor Guerra-Manzanares, Alejandro, Luckner, Marcin, Bahsi, Hayretdin

    ISSN: 0957-4174, 1873-6793
    Vydáno: Elsevier Ltd 15.11.2022
    Vydáno v Expert systems with applications (15.11.2022)
    “…•Demonstrates the existence of concept drift issues in Android malware detection…”
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    Journal Article
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    Towards a Network-Based Framework for Android Malware Detection and Characterization Autor Lashkari, Arash Habibi, A.Kadir, Andi Fitriah, Gonzalez, Hugo, Mbah, Kenneth Fon, A. Ghorbani, Ali

    Vydáno: IEEE 01.08.2017
    “…Mobile malware is so pernicious and on the rise, accordingly having a fast and reliable detection system is necessary for the users…”
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    Konferenční příspěvek
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    Android Malware: Detection, Characterization, and Mitigation Autor Zhou, Yajin

    ISBN: 1339761998, 9781339761992
    Vydáno: ProQuest Dissertations & Theses 01.01.2015
    “… These malicious apps have posed serious threats to user security and privacy. The primary goal of my research is to understand and mitigate the Android malware threats…”
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    Dissertation
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    Dissecting Android Malware: Characterization and Evolution Autor Zhou, Yajin, Jiang, Xuxian

    ISBN: 9781467312448, 1467312444
    ISSN: 1081-6011
    Vydáno: IEEE 01.05.2012
    “…The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android…”
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    Konferenční příspěvek
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    DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling Autor Haipeng Cai, Na Meng, Ryder, Barbara, Yao, Daphne

    ISSN: 1556-6013, 1556-6021
    Vydáno: New York IEEE 01.06.2019
    “…Most existing Android malware detection and categorization techniques are static approaches, which suffer from evasion attacks, such as obfuscation…”
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    Journal Article
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    Lightweight, Effective Detection and Characterization of Mobile Malware Families Autor Elish, Karim O., Elish, Mahmoud O., Almohri, Hussain M. J.

    ISSN: 0018-9340, 1557-9956
    Vydáno: New York IEEE 01.11.2022
    Vydáno v IEEE transactions on computers (01.11.2022)
    “… Despite numerous approaches and previous studies to develop solutions for detecting and preventing Android malware, the rapid continuous development of new malware variants requires a careful…”
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    Journal Article
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    Uncovering the Face of Android Ransomware: Characterization and Real-Time Detection Autor Chen, Jing, Wang, Chiheng, Zhao, Ziming, Chen, Kai, Du, Ruiying, Ahn, Gail-Joon

    ISSN: 1556-6013, 1556-6021
    Vydáno: IEEE 01.05.2018
    “…In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android…”
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    Journal Article
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    DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data Autor Dali Zhu, Hao Jin, Ying Yang, Di Wu, Weiyi Chen

    Vydáno: IEEE 01.07.2017
    “… Traditional malware detection approaches based on signatures or abnormal behaviors are invalid when dealing with novel malware…”
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    Konferenční příspěvek
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    MCGDroid: An android malware classification method based on multi-feature class-call graph characterization Autor He, Mingkun, Ge, Jike, Chen, Zuqin, Ling, Jin, Kong, Weiquan

    ISSN: 0167-4048
    Vydáno: Elsevier Ltd 01.01.2026
    Vydáno v Computers & security (01.01.2026)
    “…Malicious software (malware) attacks constitute a major category of security risks affecting the Android operating system…”
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    Journal Article
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    Android Malware Detection: an Eigenspace Analysis Approach Autor Yerima, Suleiman Y, Sezer, Sakir, Muttik, Igor

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 27.07.2016
    Vydáno v arXiv.org (27.07.2016)
    “… Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization…”
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    Paper
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    SAC: Collaborative learning of structure and content features for Android malware detection framework Autor Yang, Jin, Liang, Huijia, Ren, Hang, Jia, Dongqing, Wang, Xin

    ISSN: 0925-2312
    Vydáno: Elsevier B.V 07.07.2025
    Vydáno v Neurocomputing (Amsterdam) (07.07.2025)
    “…With the rapid development of Internet of Things (IoT) technology, Android devices have increasingly become primary targets for malware attacks…”
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    Journal Article
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    An Empirical Study on Android Malware Characterization by Social Network Analysis Autor Zhao, Haojun, Wu, Yueming, Zou, Deqing, Jin, Hai

    ISSN: 0018-9529, 1558-1721
    Vydáno: New York IEEE 01.03.2024
    Vydáno v IEEE transactions on reliability (01.03.2024)
    “…Android malware detection has always been a hot research field. Prior work has validated that graph-based Android malware detection methods are effective…”
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    Journal Article
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    Android malware detection: An eigenspace analysis approach Autor Yerima, Suleiman Y., Sezer, Sakir, Muttik, Igor

    Vydáno: IEEE 01.07.2015
    “… Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization…”
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    Konferenční příspěvek
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    An Android Malware Detection Approach Using Weight-Adjusted Deep Learning Autor Li, Wenjia, Wang, Zi, Cai, Juecong, Cheng, Sihua

    Vydáno: IEEE 01.03.2018
    “…) which severely threaten the security of Android smartphones, we propose an Android malware characterization and identification approach that uses deep learning algorithm to address the urgent need for malware detection…”
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    Android Malware Characterization Using Metadata and Machine Learning Techniques Autor Guzmán, Antonio, Muñoz, Alfonso, Hernández, José Alberto, Martín, Ignacio

    ISSN: 1939-0114, 1939-0122
    Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
    Vydáno v Security and communication networks (01.01.2018)
    “…) other features publicly available at Android markets are more relevant in detecting malware, such as the application developer and certificate issuer; and (3…”
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    Journal Article