A CLASSIFICATION FUSION SYSTEM FOR DETECTING MALICIOUS ANDROID APPLICATION IN SMARTPHONES

In 20th century, the usage of smartphones have been increased wisely. A lot of people around the world are able to connect themselves with their smartphones in many ways. As the usages of smartphones are increasing steadily, some intruders are creating malicious Android application to steal the sensitive data from the smartphones. Hence an effective and efficient malicious application are needed to tackle these serious problems. Traditionally numerous malware detection software have been developed, but some of the software are not able to detect newly created malware application and applications infected by various Trojan horse, worms, and spyware. The ML algorithms provide a unique way of enhancement to the malicious applications detection software. ML algorithms for classification such as Decision Tree, Support Vector Machine (SVM), Naive Bayes and Classifier Fusion (CF) system called classifier fusion algorithm are used to enhance the malware application detection in proposed system. The proposed ML classification and fusion algorithms will increase the performance metrics like accuracy of exposing the malware application and decreasing the time complexity for detection. The proposed system brings the detection software into a trained application to install into an Android smartphones and detect the malicious application in user's accessibility.