Cyber security for federated learning environment using AI technique

Abstract

AbstractA cyber‐security threat in the Android environment is seen as malicious code. Due to the rapid development of Android malware deployments, manually detecting malicious apps inside the Android environment is almost impossible. In the end of an outcome, machine learning has emerged as a promising method for detecting malware. Because the increasing accessibility and appropriate attributes have a significant impact on machine learning performance, selecting features becomes more critical in malware detection using the machine learning. In the existing system, a filter‐based feature selection method was used. The filter‐based feature subset selection strategies are mathematically efficient for computation. But they refuse to address concerns including such multicollinearity, which impacts the filter methods' accuracy. To overcome this issue, this research develops a hybrid method that combines the findings of both static and dynamic malware examination. This method tackles the issue of more effectively analysing, identifying, and classifying Android malware. Many feature selection approaches are compared and contrasted based on a variety of parameters, such as the composition of important extracted features. By using extracted features, whilst completing static and dynamic malware examinations, several machine learning methods are used to identify and characterize malware. The experimental result shows the evaluation of the feature selection method which is varied from other algorithms. It enhances the accuracy and minimizes the run time of the learning models.