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@article{168761,
        author = {Rahul Khade and Sanika Jadhav and Shivani Lande and Pranjali Garud and Amruta V Patil},
        title = {A Survey: PDF Malware Detection: Toward Machine Learning Modeling with Explainability Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1848-1854},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168761},
        abstract = {The paper "PDF Malware Detection: Toward Machine Learning Modeling with Explainability Analysis" explores machine learning techniques for detecting malware contained in PDF documents. Malicious PDFs constitute a growing concern, highlighting the importance of effective detection systems. The authors present a model that identifies suspected malware and provides insight into its decision-making process, improving transparency and trust in the detection system. The study uses machine learning techniques and explainability tools to improve malware detection accuracy and assist analysts understand the underlying factors that influence predictions. The research highlights the need for a combination of powerful detection approaches and explainable AI to effectively combat PDF malware. The methodology leverages a combination of static and dynamic information collected from PDF files, such as metadata, structural, and content-related attributes.},
        keywords = {PDF malware detection, machine learning, explainability analysis, feature importance and cybersecurity.},
        month = {October},
        }