Abstract:
In the recent era of modern world, Android malware
continues to escalate, the challenges associated with its usage are
growing at an unprecedented rate. This cause a rapid growth in
Android malware infections points to an alarming and swift rise
in their prevalence, signalling a cause for concern. Traditional
anti-malware systems, reliant on signature-based detection,
prove inadequate in addressing the expanding scope of newly
developed malware. Various strategies have been introduced to
counter the escalating threat in the Android mobile field, with
many leaning towards machine learning (ML) models limited by
a constrained set of features. This paper introduces a novel
approach employing a deep learning (DL) framework,
incorporating a significant number of diverse features. The
proposed framework uses Deep Neural Network (DNN)
techniques on OmniDroid dataset, comprising 25,999 features
extracted from 22,000 Android Package Kits (APKs). Of these,
16,380 features are meticulously selected for analysis,
encompassing Permission, Opcodes, API calls, System
Commands, Activities, and Services. Additionally the data is
partitioned feature wise and subjected to feature selection on
each feature set to ensure equitable consideration of all features.
A comparative analysis is presented by comparing the
framework accuracy with the accuracies produced by the
existing ML models. The presented framework demonstrates
notable enhancements in detection accuracy, achieving 89.04%
accuracy, attributed to the incorporation of a substantial number
of features.