Robust Android Security: A Random Forest-Based Approach to Malware Detection

With an accuracy of 97.03°/0, this work offers a method for identifying Android malware utilising a Random Forest model. Growing frequency of malware aimed at Android devices calls for sophisticated and dependable detection techniques. Using a strong ensemble learning method called Random Forest, applications are categorised as either benign or malicious depending on a thorough collection of features gleaned from them. The model uses several decision trees, each of which helps to produce a consensus decision, hence improving the general predictive performance and lowering the overfitting risk. On a varied collection of Android applications, extensive tests and evaluations were carried out to show the model's capacity for precisely spotting malware. The great accuracy rate highlights how well the Random Forest method is in differentiating between good and bad uses, so providing a potential means of enhancing Android security. This approach guarantees strong protection for Android users by not only offering dependable detection but also changing to new and developing threats.