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                        "unstructured": "Aboaoja, F. A., Zainal, A., Ghaleb, F. A., Al-rimy, B. A. S., Eisa, T. A. E., & Elnour, A. A. H. (2022). Malware Detection Issues, Challenges, and Future Directions: A Survey. Applied Sciences, 12(17), 8482. https://doi.org/10.3390/ app12178482"
                    },
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                        "key": "ref8",
                        "doi": "10.1109/imcsit.2009.5352759",
                        "unstructured": "Gavrilut, D., Cimpoesu, M., Anton, D., & Ciortuz, L. (2009). Malware Detection Using Machine Learning. 4"
                    }
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