An Interactive Dashboard for Visualizing and Evaluating Mobile Malware Detection Performance Using Machine Learning and Deep Learning Algorithms
Abstract
In recent years, mobile devices' ubiquitous presence has been paralleled by an escalating surge in mobile malware threats, highlighting unprecedented security concerns. This research endeavor delineates the development of an advanced interactive dashboard, tailored to visualise and critically assess an array of machine and deep learning algorithms for mobile malware detection. Designed as a specialized interface, the dashboard equips cybersecurity experts, data analysts, and researchers with the means to engage dynamically with pertinent data, offering exploratory avenues for crucial performance metrics—namely, accuracy, precision, recall, and the F1 score. Built on a dataset that integrates mobile malware classifications, the dashboard extends instantaneous perceptiveness into algorithmic efficacy. A rigorous analytical process underscores the algorithmic selection, including Random Forest, Logistic Regression, Naive Bayes, Neural Networks (NN), and Deep Neural Networks (DNN). Each algorithm is meticulously evaluated to ascertain its merit and relevance for detecting mobile malware. The research magnifies the significance of state-of-the-art deep learning modalities, primarily focusing on NNs and DNNs, aiming to bolster the precision and resilience of detection mechanisms. The dashboard’s incorporation of these paradigms stands instrumental in enhancing defenses against progressive malware challenges. Supplemented by diverse graphical representations, the dashboard elucidates intricate facets of algorithmic output, while diagrams such as the Entity-Relationship (ERD), Sequence, Deployment, State, Use Case, and Activity cohesively offer an exhaustive insight into the system's structural blueprint and operative capacities. Conclusively, this undertaking epitomises a pivotal evolution in mobile malware detection paradigms. By orchestrating cutting-edge algorithms, all-encompassing datasets, and lucid visual aids, it forges a pathway towards effectively navigating and mitigating the complexities intrinsic to mobile malware threats. This novel interface stands as a vanguard, priming stakeholders with an astute instrument for strategic decision-making and fostering progressive strides in mobile security domains.
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