International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

PDF Malware Detection Using Machine Learning Models

Abstract

PDFs are widely used for document sharing, but their popularity also makes them a common target for malware. The software, titled "PDF Malware Detection Using Machine Learning Models," aims to develop and compare ml learning models for detecting malware in PDFs. Using a Kaggle dataset containing examples of both hazardous and secure PDFs, various methods such as Random Forest, C5.0, J48, Support Vector Machines, AdaBoost, Deep Neural Networks, Gradient Boosting Machines, and K-Nearest Neighbors will be employed. The main goal is to attain high detection accuracy while integrating explainability to gain a deeper understanding of the models' behaviour. By leveraging machine learning techniques, this project seeks to enhance cybersecurity measures, offering a robust solution to identify and mitigate potential threats embedded in PDF documents.

Country : India

1 A.Komala2 Boya Chandu3 Medivala Reddy Hemanth

  1. Dept. of CSE-Cybersecurity, Madanapalle Institute of Technology & Science, Madanapalle, India
  2. Dept. of CSE-Cybersecurity, Madanapalle Institute of Technology & Science, Madanapalle, India
  3. Dept. of CSE-Cybersecurity, Madanapalle Institute of Technology & Science, Madanapalle, India

IRJIET, Volume 9, Special Issue of INSPIRE’25 April 2025 pp. 278-283

doi.org/10.47001/IRJIET/2025.INSPIRE45

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