安卓恶意软件检测与分类数据集-2021-meghnadhalaria
数据来源：互联网公开数据
标签：安卓恶意软件,检测,分类,静态特征,动态特征,网络安全,数据集,研究
数据概述：
本数据集包含用于安卓恶意软件检测和分类的两组数据集。第一组数据集（Dataset-1）包含352个静态特征和323个动态特征，这些特征是从1800个良性应用和1747个恶意应用中提取的。第二组数据集（Dataset-2）同样包含了352个静态特征和323个动态特征，但仅针对1747个恶意应用，并细分为13个恶意软件家族。AndroMD数据集结合了上述两个数据集中的静态和动态特征，适用于全面的分析和研究。
数据用途概述：
该数据集适用于安卓恶意软件检测和分类的研究，适合用于二分类和多分类的实验研究。研究人员和安全专家可以利用这些数据进行特征提取、模型训练、性能评估等，以开发更有效的安卓恶意软件检测和分类方法。此外，教育机构也可以利用这些数据进行教学和演示，帮助学生理解安卓恶意软件检测的原理和技术。
参考文献：
Meghna Dhalaria, Ekta Gandotra (2021). "A Hybrid Approach for Android Malware Detection and Family Classification", International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, issue Regular Issue, no. 6, pp. 174-188. https://doi.org/10.9781/ijimai.2020.09.001
Meghna Dhalaria, and Ekta Gandotra, “MalDetect: A Classifier Fusion Approach for Detection of Android Malware,” Expert Systems with Applications, vol. 235, pp. 121155, 2023. https://doi.org/10.1016/j.eswa.2023.121155
Meghna Dhalaria, and Ekta Gandotra, “Binary and Multi-class Classification of Android Applications using Static Features.” International Journal of Applied Management Science. vol. 15, no. 2, pp. 117-140, 2023. https://doi.org/10.1504/IJAMS.2023.131670
Meghna Dhalaria, and Ekta Gandotra, "CSForest: an approach for imbalanced family classification of android malicious applications." International Journal of Information Technology, vol. 13 no. 3, pp. 1-13, 2021. https://doi.org/10.1007/s41870-021-00661-7
Meghna Dhalaria, and Ekta Gandotra, “A Framework for Detection of Android Malware using Static Features,” In 2020 IEEE 17th India Council International Conference (INDICON), pp. 1-7, IEEE, 2020.
https://doi.org/10.1109/INDICON49873.2020.9342511
Meghna Dhalaria, and Ekta Gandotra, “Android Malware Detection using Chi-Square Feature Selection and Ensemble Learning Method,” In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 36-41, IEEE, 2020. https://doi.org/10.1109/PDGC50313.2020.9315818
