Machine Learning Techniques for Intrusion Detection System Malware Android Devices

Malware infestations have increased with the rise of mobile devices, notably Android smartphones. Our work fixes this issue by improving Android security using Decision Trees (DT), Support Vector Machines (SVMs), Random Forest (RFs), and K-nearest neighbours (KNNs). We defend Android smartphones from malware using a proven way. We will test these machine learning methods against diverse illnesses. Android smartphones have raised security concerns, necessitating innovative techniques to recognize new threats. Machine learning algorithms that recognize and anticipate patterns may replace Android security. DT, KNN, RF, and SVM algorithms helped us construct a strong malware detection system. We evaluate numerous Android malware detection machine-learning algorithms based on speed, accuracy, and effectiveness. These findings should improve Android security machine-learning solution selection and implementation. Decision trees are transparent and open, random forests use ensemble learning, support vector machines excel in probabilistic classification, and KNN detects Android infections from similarity patterns better. A comparison study can provide the finest security solution. The initiative will provide the basis for mobile device security research and enhance Android's security using machine learning. Android smartphones will be safer from new attacks when security gaps are closed.