A Hybrid Approach Based On Boosting Algorithm For Effective Android Malware Detection

Abstract

Mobile phones are being used much more often and play a crucial role in everyday lives. These gadgets hold a tonne of personal information and provide a variety of functions and services. Mobile devices have become indispensable for those who utilise technology and communication since they are practical and effective. However, mobile systems are vulnerable to virus assaults just like any other type of information system. The development of hardware technologies has increased the complexity and performance of mobile apps. Additionally, the danger of security breaches and data theft rises with continued usage of mobile devices. Malicious actors may use mobile system flaws to obtain sensitive data, such as login passwords, financial information, and personal information. Mobile device makers and app developers are constantly changing their software to enhance security and performance in order to solve these issues. For instance, to safeguard user data, many mobile operating systems now have built-in security measures like firewalls, encryption, and two-factor authentication. In this paper, we present a linear regression model for detecting malware on the Android platform. This technique can assist in the prompt identification and obstruction of Android malware assaults, as well as improve app security by flagging any unnecessary permissions. Additionally, developers can use this approach to enhance the security of their apps and protect user data from unauthorized access.