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InfoTech Spectrum: Iraqi Journal of Data Science

· eISSN 3007-5467 · DOI 10.51173/ijds
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Research article Open Access EN pp. 1–15 · 01 Jun 2024

Enhancing Malware Detection Through Machine Learning Techniques

  1. Department of computer engineering, University of Qom, Qom, Iran
✱ Corresponding author: [email protected]
DOI 10.51173/ijds.v1i1.58 IJDS-TRK-2026-58 3 views 0 downloads

Abstract

Malware detection is important to computer network security since it is the principal attack vector against modern enterprises. As a result, firms must remove viruses from computer systems. Using artificial intelligence, namely machine learning techniques, to function in real-time with an IT system is the ideal solution to this problem. This issue has yet to be fixed, but it is still significant because a lack of processing power and memory constrains these features. The most popular method for evaluating systems and intrusion detection models is using the Application Program Interface (API) calls via the KDD-CUP99 data set to give this solution. KDD-CUP99 has more than three hundred thousand samples, each with 54 features. However, the data set attributes were designed and chosen to provide us with a high malware detection rate. The quality of this data was lowered to produce results. To get the desired results, the attributes of this data were reduced. Data transformation and purification are used in this process. Inaccurate, unnecessary, duplicated, or missing information is eliminated by data cleansing. Data cleaning eliminates inaccurate, excessive, redundant, or lacking information. By comparing this study to earlier research that employed lengthy sequences of software interface (API) calls with the same machine-learning classifiers, data transformation includes discretization, which transforms the continuous process of discretizing continuous data into discrete forms is a type of data transformation. Using more advanced algorithms to do the task at hand with the best precision and the least expense increases accuracy and performance. The data set was divided into two categories using a Support Vector Machine (SVM), Decision Tree (DT), and Iterative Dichotomiser 3 (ID3). The findings revealed that little previous research uses a five-class classification strategy for malware detection. The accuracy of several works is comparable to the accuracy acquired in the proposed work.
Keywords
Computer science Malware Data mining Artificial intelligence Machine learning Intrusion detection system Interface (matter) Process (computing) Data set Transformation (genetics) Data transformation Support vector machine Set (abstract data type) Data warehouse Operating system

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Zeina S. Jassim and Mohamad M. Kassir. 2024. Enhancing Malware Detection Through Machine Learning Techniques. InfoTech Spectrum: Iraqi Journal of Data Science 1, 1 (2024), 1–15. https://doi.org/10.51173/ijds.v1i1.58

Jassim, Z. S. ; Kassir, M. M.. Enhancing Malware Detection Through Machine Learning Techniques. InfoTech Spectrum: Iraqi Journal of Data Science 2024, 1 (1), 1–15. https://doi.org/10.51173/ijds.v1i1.58

Jassim, Z. S. & Kassir, M. M. (2024). Enhancing Malware Detection Through Machine Learning Techniques. InfoTech Spectrum: Iraqi Journal of Data Science, 1(1), 1–15. https://doi.org/10.51173/ijds.v1i1.58

JASSIM, Zeina S. ; KASSIR, Mohamad M.. Enhancing Malware Detection Through Machine Learning Techniques. InfoTech Spectrum: Iraqi Journal of Data Science, v. 1, n. 1, p. 1–15, 2024. https://doi.org/10.51173/ijds.v1i1.58

Jassim, Zeina S. and Kassir, Mohamad M.. 2024. "Enhancing Malware Detection Through Machine Learning Techniques." InfoTech Spectrum: Iraqi Journal of Data Science 1 (1): 1–15. https://doi.org/10.51173/ijds.v1i1.58

Jassim, Z. S. and Kassir, M. M. (2024) 'Enhancing Malware Detection Through Machine Learning Techniques', InfoTech Spectrum: Iraqi Journal of Data Science, 1(1), pp. 1–15. https://doi.org/10.51173/ijds.v1i1.58

Z. S. Jassim and M. M. Kassir, "Enhancing Malware Detection Through Machine Learning Techniques," InfoTech Spectrum: Iraqi Journal of Data Science, vol. 1, no. 1, pp. 1–15, 2024. doi: https://doi.org/10.51173/ijds.v1i1.58.

Jassim, Zeina S. and Kassir, Mohamad M.. "Enhancing Malware Detection Through Machine Learning Techniques." InfoTech Spectrum: Iraqi Journal of Data Science, vol. 1, no. 1, 2024, pp. 1–15. https://doi.org/10.51173/ijds.v1i1.58

Zeina S. Jassim and Mohamad M. Kassir. "Enhancing Malware Detection Through Machine Learning Techniques." InfoTech Spectrum: Iraqi Journal of Data Science 1, no. 1 (2024): 1–15. https://doi.org/10.51173/ijds.v1i1.58

Jassim ZS , Kassir MM. Enhancing Malware Detection Through Machine Learning Techniques. InfoTech Spectrum: Iraqi Journal of Data Science. 2024;1(1):1–15. https://doi.org/10.51173/ijds.v1i1.58

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