In the current world of technology, people are updating to smart devices day by day. Users often keep personal information on their smartphones, but it increases the threat to the privacy of their data. There are many applications which require the user data to provide specific services such as WhatsApp, Instagram, Paytm, PhonePe, etc., There are special permissions which can be misused by some applications, like READ_EXTERNAL_STORAGE, READ_SMS, SEND_SMS, WRITE_EXTERNAL_STORAGE, READ_CONTACTS, etc., Many applications take users data to their servers without the knowledge of user by running in the background. In this paper, it is shown that how an application runs in the background by running various services, and performs the background activities like notifications, displaying ads, etc., A proposed algorithm is described that how every activity of the background services can be monitored using Android Log and user can be alerted by showing which data is being accessed by the particular application.
Feature selection plays a vital role for every data analysis application. Feature selection aims to choose prominent set of features after removing redundant and irrelevant features from original set of features. High Dimensional dataset poses a challenging task for Machine Learning algorithms. Many state-of-art solutions were developed to handle this issue. High dimensionality in addition to imbalance ratio in the dataset becomes a tedious task. To overcome the issue, this paper introduces a novel method namely Pearson’s Redundancy Based Multi Filter algorithm with improved BAT algorithm (PRBMF-iBAT) to obtain multiple feature subsets. PRBMF is implemented using multiple filters to obtain highly relevant features. iBAT algorithm uses these features to find best subset of features for classification. The results prove that PRBMF-iBAT perform better for the classifier in terms of Accuracy, Precision, Recall and F- Measure for three micro array datasets with SVM classifier. The proposed system achieves 97.99% of accuracy as highest compared to the existing rCBR-BGOA algorithm.
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