Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Pdf Malware Detection: Explainable Machine Learning Modelling Pdf Malware Detection

Author(s) Ms. Samula Christeena Kotrike, Prof. Dr. Garima Sinha
Country India
Abstract The issue regarding the sharing of file in PDF format is one issue that the digital technology has made a stride in the lead and thus, viruses have come to infect computers considering that the software is so widespread. The project titled as Detecting Malware in PDFs: Advancing Machine Learning Models with Interpretability Assessment is in fact executed since it does not just create machine learning algorithms capable of identifying malware concealed within PDF files but also analyses them in terms of effectiveness. The taking of the Kaggled corpus with labelling of the poisonous and non-poisonous PDFs being the core of the data is considered one of the significant components of the experiment. Additionally, RF, C5.0, J48, SVM, AdaBoost, DNN, GBM, and KNN are some of the techniques that will be subject to practical testing. The last two goals are to attain optimal detection rates, and simultaneously to provide a compromise of what the model has decided and give the researcher the clues on what made the model arrive at this decision. The machine learning techniques will be implemented and the project will enable the aggregation of greater cybersecurity solutions that are a community protection against the threats that could have initially surpassed via PDF files and thus their spread is being intercepted.
Keywords PDF malware detectors, ML, RF, SVM, DNN, explainability, cybersecurity, rogue PDF, classification algorithms, Kaggle Dataset.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-16
DOI https://doi.org/10.63363/aijfr.2026.v07i02.4215
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