Detection of anomalous behavior of smartphones using signal processing and machine learning techniques

Different applications in smartphones result in different power consumption patterns. The fact that every application has been coded to perform different tasks leads to the claim that every action onboard (whether software or hardware) will consequently have a trace in the power consumption of the smartphone. Even though the power consumed by the application might not be the same every time it is used, there still remains a similarity in the power consumption pattern. An anomalous behavior on the smartphone would result in a reduction in the similarity of the power consumption pattern. This change in similarity can be used to detect the presence of anomalous behavior of smartphones. We have proposed two approaches to detecting anomalous behavior on smartphones based on the power consumption pattern. The first approach is based on signal processing and the second approach explores the area of statistical learning in detecting malware. The two approaches have been analysed, evaluated, and compared. It has been observed that the signal processing method of detection performed better for anomalous behavior of lower intensity and the statistical learning method performed better for higher intensity anomalous behavior. It was also observed that both the methods are complementary.