Synthetic Mobile Application Security Dataset for ML Vulnerability Detection

This dataset contains synthetically generated data representing security profiles of mobile applications, designed for training and evaluating machine learning models for vulnerability detection. It was created as part of the research described in the paper \Machine Learning-Based Vulnerability Detection in Mobile Applications\ by Yanguema and Yin.The dataset comprises 100 samples, each characterized by 10 engineered security features: `storage_encryption_level`, `api_security_score`, `data_transmission_security`, `authentication_strength`, `input_validation_score`, `network_communication_security`, `third_party_library_risk`, `runtime_permissions_management`, `code_obfuscation_level`, and `certificate_pinning_implementation`. These features are provided as raw numeric scores (floating-point). Each sample is labeled with a binary target variable, `vulnerability_label`, indicating the simulated presence (1) or absence (0) of security vulnerabilities.The data generation process aimed to mirror realistic distributions and correlations relevant to mobile application security assessments. Note: The feature values in this file require normalization Min-Max scaling to [0,1] as a preprocessing step, as described in the associated paper's methodology, before being used for model training. This dataset can be used to reproduce the results presented in the associated paper, benchmark alternative machine learning approaches, and serve as a resource for research into automated mobile security analysis. The data is provided in CSV format.