Android Malware Defense: Leveraging Machine Learning Models

The rapid increase in the number of Android devices has made it a major target for malware attacks that put many users around the globe at risk of security breaches. As technology advances and malware becomes more complex and diverse at a pace conventional methods relying solely on recognizing specific patterns are becoming less effective in safeguarding against these threats. This study delves into the use of Machine Learning (ML) models as a line of defense against Android Malware. We delve into advancements in ML techniques specifically tailored for detecting Android malware, highlighting their advantages and constraints. We are looking into how different models such as Logistic Regression (LR) Support Vector Machine (SVM) Decision Trees (DT) k. Nearest Neighbor (k. NN) Naive Bayes (NB) Neural Networks (NN) Random Forest (RF) Gradient Boosting Machine (GBM) AdaBoost (AdaB) Bagging Classifier (BC) and XGBoost (XGB) can accurately identify malicious applications with high precision and low rates of false positives using the " TUNADROMD " dataset. The models were thoroughly evaluated based on accuracy, precision, recall, F1 score, and ROC AUC criteria. The outcomes reveal that methods such as k-NN, NN, and RF achieved a flawless score of 1 across all measures signifying their exceptional accuracy in recognizing malware without any incorrect identifications. The results highlight the promise of ML in transforming Android security and laying groundwork for creating more dynamic defenses against malware threats.