PDF Malware Detection: Toward Machine Learning Modelling with Explainability Analysis
Keywords:
PDF malware detection, ML, RF, SVM, DNN, explainability, cybersecurity, malicious PDF, classification algorithms, Kaggle datasetAbstract
In the digital age, PDF files are widely used for document sharing, but their popularity also makes them a target for malware attacks. This project, titled " Detecting Malware in PDFs: Advancing Machine Learning Models with Interpretability Assessment," aims the goal is to design and assess machine learning models aimed at identifying malware within PDF files. Utilizing a dataset from Kaggle, which contains labelled examples of malicious and benign PDFs, various algorithms including RF, C5.0, J48, SVM, AdaBoost, DNN, GBM, and KNN will be applied. The primary focus is on achieving high detection accuracy while also providing explainability to gain insight into how the models make decisions. 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.
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