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.