Labels Predicted by AI
Malware Classification Using Machine Learning Imbalanced Dataset Data Preprocessing
Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.
Abstract
Malware, malicious software designed to damage computer systems and perpetrate scams, is proliferating at an alarming rate, with thousands of new threats emerging daily. Android devices, prevalent in smartphones, smartwatches, tablets, and IoTs, represent a vast attack surface, making malware detection crucial. Although advanced analysis techniques exist, Machine Learning (ML) emerges as a promising tool to automate and accelerate the discovery of these threats. This work tests ML algorithms in detecting malicious code from dynamic execution characteristics. For this purpose, the CICMalDroid2020 dataset, composed of dynamically obtained Android malware behavior samples, was used with the algorithms XGBoost, Naıve Bayes (NB), Support Vector Classifier (SVC), and Random Forest (RF). The study focused on empirically evaluating the impact of the SMOTE technique, used to mitigate class imbalance in the data, on the performance of these models. The results indicate that, in 75