Empirical evaluation of minority oversampling techniques in the context of Android malware detection

• Main Authors: SHAR, Lwin Khin, TA, Nguyen Binh Duong, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Malware detection; Oversampling; Imbalanced learning; SMOTE; SMOTE variants; Android malware; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6852; https://ink.library.smu.edu.sg/context/sis_research/article/7855/viewcontent/Smote_2021.pdf
• Institution: Singapore Management University

In Android malware classification, the distribution of training data among classes is often imbalanced. This causes the learning algorithm to bias towards the dominant classes, resulting in mis-classification of minority classes. One effective way to improve the performance of classifiers is the syn...