Citation

Ahmed, Haseeb and Ali, Hasnat and Batool, Komal and Islam, Muhammad and Irshad, Azeem (2026) A Comparative Analysis of Dynamic and Static Malware Detection Tools. In: AI-Driven Security for Next-Generation IoT Systems. Springer Nature Link, pp. 85-105. ISBN 978-303208784-3, 978-303208783-6

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Abstract

Malware poses a significant threat to the modern computer world, and various tools and techniques exist for detecting malware. This research article aims to address this challenge by presenting a comprehensive performance comparison of existing malware detection tools and techniques. Moreover, in this paper, a performance comparison of existing tools and techniques for malware detection is presented. To assess the performance of these tools, a virtual malware analysis lab is created using VMware workstation. We have selected 17 widely used malware detection tools and utilized known malware samples for our comparison. Through rigorous testing and analysis, we evaluate the performance of these tools based on multiple parameters, i.e., Accuracy, Specificity, Sensitivity, and Efficiency which are also presented graphically. The findings indicate that the top three tools, based on specific parameters and the given dataset, are Process monitor, IDA freeware, and Intezer. These tools demonstrate remarkable performance in terms of detection accuracy, low false positive rates, efficient detection speed, optimized resource utilization, and intuitive user interfaces. This performance comparison study contributes to the field of malware detection by providing valuable insights into the strengths and limitations of different tools and techniques. The results can aid security professionals, researchers, and developers in making informed decisions when selecting and implementing malware detection solutions.

Item Type:	Book Section
Uncontrolled Keywords:	Computer Vision, Motion Detection, Personal Computing
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions:	Others
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Jun 2026 02:54
Last Modified:	08 Jun 2026 05:44
URII:	http://shdl.mmu.edu.my/id/eprint/15911

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