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Obfuscated Malware Detection: Impacts on Detection Methods

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1863))

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Abstract

Obfuscated malware poses a challenge to traditional malware detection methods as it uses various techniques to disguise its behavior and evade detection. This paper focuses on the impacts of obfuscated malware detection techniques using a variety of detection methods. Furthermore, this paper discusses the current state of obfuscated malware, the methods used to detect it, and the limitations of those methods. The impact of obfuscation on the effectiveness of detection methods is also discussed. An approach for the creation of advanced detection techniques based on machine learning algorithms is offered, along with an empirical examination of malware detection performance assessment to battle obfuscated malware. Overall, this paper highlights the importance of staying ahead of the constantly evolving threat landscape to safeguard computer networks and systems.

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Acknowledgements

This work was supported in part by the Ministry of Higher Education through the Fundamental Research Grant Scheme under Grant FRGS/1/2018/ICT04/UTM/01/1; and in part by the Faculty of Informatics and Management, University of Hradec Králové, through the Specific Research Project (SPEV), “Smart Solutions in Ubiquitous Computing Environments”, under Grant 2102/2023. We are also grateful for the support of student Michal Dobrovolny in consultations regarding application aspects.

Author information

Authors and Affiliations

  1. Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Nor Zakiah Gorment & Ali Selamat

  2. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Johor, Malaysia

    Ali Selamat

  3. MagicX (Media and Games Center of Excellence), Universiti Teknologi Malaysia, Johor Bahru, 81310, Johor, Malaysia

    Ali Selamat

  4. College of Computing and Informatics, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia

    Nor Zakiah Gorment

  5. Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, 50003, Hradec Kralove, Czech Republic

    Ali Selamat & Ondrej Krejcar

Authors
  1. Nor Zakiah Gorment
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  2. Ali Selamat
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  3. Ondrej Krejcar
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Corresponding author

Correspondence to Ali Selamat .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University, Iwate, Japan

    Hamido Fujita

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut's Institute of Technology, Ladkrabang, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

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Gorment, N.Z., Selamat, A., Krejcar, O. (2023). Obfuscated Malware Detection: Impacts on Detection Methods. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_5

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  • DOI: https://doi.org/10.1007/978-3-031-42430-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42429-8

  • Online ISBN: 978-3-031-42430-4

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