An Effect of Machine Learning based Algorithm for Identify Android Malware

Malware is a substantial challenge in operating systems and software, and the Android system is no exception to this pervasive issue. Despite the array of existing detection and analysis techniques, accurately identifying new malware remains a persistent challenge. This paper aims to delve into and shed light on the prevailing detection and analysis methodologies employed for identifying malicious code in the Android ecosystem. In addition to exploring existing methods, the architecture proposes the utilization of machine learning algorithms for a more robust analysis of such malware, complemented by semantic analysis. A curated dataset comprising permissions associated with malicious applications will be employed, allowing for a comparative analysis against permissions extracted from the application under analysis. Through this approach, users will gain insights into the extent of malicious permissions embedded within an application. Further analysis will be conducted through comment analysis, enriching the depth of our understanding. In the contemporary landscape, detection techniques for smartphone devices have lagged behind the rapid proliferation of the mobile population. Recent surveys highlight the vast presence of approximately 2.1 million Android applications on the market, underscoring the need for an enhanced approach to combat the surge of Android malware. This influx of malware can be attributed to the burgeoning use of the Android system and the subsequent proliferation of applications developed by third parties. Moreover, it's essential to acknowledge that the Google Android market, despite its popularity, cannot guarantee the absolute absence of threats within its listed applications. Reports of Trojan applications with concealed malicious code further emphasise the necessity for more effective detection mechanisms, particularly during the app publication process in the Google Android market.