Machine Learning Classification Algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis

摘要: Exponential growth experienced in Internet usage has paved the way to exploit users of Internet, a phishing attack is one means that can be used obtained victim confidential details unwittingly across Internet. A high false-positive rate and low accuracy have been setback detection. In this research 17 different supervised learning techniques such as RandomForest, Systematically Developed Forest (SysFor), Spectral Areas Ratios Classifier (SPAARC), Reduces Error Pruning Tree (RepTree), RandomTree, Logic Model (LMT), by Penalizing Attributes (ForestPA), JRip, PART, Nearest Neighbor with Generalization (NNge), One Rule (OneR), AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, Library for Support Vector Machine (LibSVM), BayesNet were employed achieve comparative analysis machine classifier. The performance classifier algorithms was rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Relative False Positive Rate True WEKA data mining tool. revealed quite several classifiers also exist which if properly explored will yield more accurate results RandomForest found an excellent gives best 0.9838 false positive 0.017. result indicates achievement classification suggests anti-phishing application developer implement algorithm discovered study enhance feature detection classification.