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In 2020, despite showing a slight decline, Android still led the market at 84.1% compared to iOS 15.9% and others 0%. The International Data Corporation (IDC) report on worldwide shipments stated that Android mobiles are leading the market, with an increase from 85.1% in 2018 to 87.0% in 2019. The McAfee report also indicated that the incidence of malware attacks is increasing every year, with over 30 million mobile malware attacks detected in 2018.Īmong the various mobile devices available, Android mobiles are the most commonly targeted by malware. Hidden applications and adware were noted to be the most common form of mobile threats in the Android operating system.
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The McAfee Report noted that malware such as backdoors, crypto mining, fake applications, and banking trojans increased substantially in the latter half of 2019. Malware can be classified according to the mechanism by which it gains access to a system: worms, backdoors, trojans, rootkits, spyware, and adware. Malware is malicious software that attacks the files or programmes that are stored within mobile devices. This proliferation of technology has also provided opportunities for the deployment of malware codes designed to target mobile devices. The convenience of mobile devices enables many online activities to be performed, for instance, the online streaming of information, social networking, video viewing, and online banking. The use of mobile devices has rapidly increased throughout the world in recent decades, with most people now owning mobile device. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection. The performances of the different sets of classifiers were then compared. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. While both perform security evaluations successfully, there is still room for improvement.
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Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. Therefore, adequate security evaluations that detect Android malware are crucial. The evolution of malware is causing mobile devices to crash with increasing frequency.