Machine Learning Techniques for Intrusion Detection in Malware-Infected Android Devices

With the proliferation of mobile devices especially Android smartphones malware infections have grown. By use of Decision Trees (DT), Support Vector Machines (SVMs), Random Forest (RFs), and K-nearest neighbours (KNNs), our study addresses this problem by enhancing Android security. We protect Android cellphones from viruses employing a tried-through method. These machine-learning techniques will be compared to other diseases. Android cell phones have brought security issues that need creative solutions to identify fresh threats. Android security might be replaced by pattern-recognizing, anticipatory machine learning systems. We built a robust malware-detecting system using DT, KNN, RF, and SVM techniques. Based on speed, accuracy, and efficiency, we assess several Android malware detection machine-learning systems. These results should guide the choice and use of Android security machine-learning solutions. While support vector machines excel in probabilistic classification and KNN identifies Android infections from similarity patterns better, decision trees are clear and open, random forests employ ensemble learning. The best security solution is usually offered via comparative research. The project will improve Android's security by employing machine learning and provide the framework for mobile device security research. Once security holes are patched, Android cell phones will be less vulnerable to fresh threats.