Cite this DOI

DharmendraThapa, Hari Narayan Ray Yadav, & Madhav Dhakal (2025). Malware Detection using the concept of Random Forest Algorithm. *Journal of Science & Technology*, *10*(5), 38–49. https://doi.org/10.46243/jst.2025.v10.i05.pp38-49

@article{dharmendrathapa2025malware,
  author    = {DharmendraThapa and Hari Narayan Ray Yadav and Madhav Dhakal},
  title     = {{Malware Detection using the concept of Random Forest Algorithm}},
  journal   = {Journal of Science \& Technology},
  year      = {2025},
  month     = {may},
  volume    = {10},
  number    = {5},
  pages     = {38--49},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2025.v10.i05.pp38-49},
  url       = {https://doi.org/10.46243/jst.2025.v10.i05.pp38-49},
  abstract  = {Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats fromvarious sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can causesystems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form ofviruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. In this study, theproposed machine learning algorithm is RF algorithm which use Gini index CART algorithm to create multiple decision treewith majority of the outputs from each decision trees. Here, total 1,38,047 data is collected which contain 96,724 malware and41,323 legit. RF algorithm achieved 99.54\% accuracy during malware detection followed by 99.13\% precision, 99.35\% recalland 99.24\% f1 score respectively during testing.}
}

TY  - JOUR
TI  - Malware Detection using the concept of Random Forest Algorithm
AU  - DharmendraThapa
AU  - Hari Narayan Ray Yadav
AU  - Madhav Dhakal
JO  - Journal of Science & Technology
PY  - 2025
DA  - 2025/05/21/
VL  - 10
IS  - 5
SP  - 38
EP  - 49
PB  - Longman Publishers
SN  - 2456-5660
AB  - Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats fromvarious sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can causesystems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form ofviruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. In this study, theproposed machine learning algorithm is RF algorithm which use Gini index CART algorithm to create multiple decision treewith majority of the outputs from each decision trees. Here, total 1,38,047 data is collected which contain 96,724 malware and41,323 legit. RF algorithm achieved 99.54% accuracy during malware detection followed by 99.13% precision, 99.35% recalland 99.24% f1 score respectively during testing.
DO  - 10.46243/jst.2025.v10.i05.pp38-49
UR  - https://doi.org/10.46243/jst.2025.v10.i05.pp38-49
ER  -

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    "type": "article-journal",
    "id": "10.46243/jst.2025.v10.i05.pp38-49",
    "DOI": "10.46243/jst.2025.v10.i05.pp38-49",
    "URL": "https://doi.org/10.46243/jst.2025.v10.i05.pp38-49",
    "title": "Malware Detection using the concept of Random Forest Algorithm",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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            "family": "DharmendraThapa"
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            "family": "Hari Narayan Ray Yadav"
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            "family": "Madhav Dhakal"
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    "volume": "10",
    "issue": "5",
    "page": "38-49",
    "publisher": "Longman Publishers",
    "abstract": "Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats fromvarious sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can causesystems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form ofviruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. In this study, theproposed machine learning algorithm is RF algorithm which use Gini index CART algorithm to create multiple decision treewith majority of the outputs from each decision trees. Here, total 1,38,047 data is collected which contain 96,724 malware and41,323 legit. RF algorithm achieved 99.54% accuracy during malware detection followed by 99.13% precision, 99.35% recalland 99.24% f1 score respectively during testing.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2025.v10.i05.pp38-49 gives all four in one JSON answer.