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Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

Computer Systems Science & Engineering 2024年第5期48卷 1171-1191页

作者： Mohammad Ababneh Ayat Al-Droos Ammar El-Hassan School of Computing Sciences Princess Sumaya University for TechnologyAmman11941Jordan

With the high level of proliferation of connected mobile devices,the risk of intrusion becomes *** Intelligence(AI)and Machine Learning(ML)algorithms started to feature in protection software and showed effective *** algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’malware,especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness *** this article,we present a framework for mobile malware detection based on a new dataset containing new categories of mobile *** focus on categories of malware that were not tested before by Machine Learning algorithms proven effective in malware *** carefully select an optimal number of features,do necessary preprocessing,and then apply Machine Learning algorithms to discover malicious code *** our experiments,we have found that the Random Forest algorithm is the best-performing algorithm with such mobile malware with detection rates of around 99%.We compared our results from this work and found that they are aligned well with our previous *** also compared our work with State-of-the-Art works of others and found that the results are very close and competitive.

关键词： Android malware detect prevent artificial intelligence machine learning mobile CICMalDroid2020 cccscic-andmal-2020

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