Detecting Illicit Data Leaks on Android Smartphones Using an Artificial Intelligence Models
- IFIP advances in information and communication technology , AICT-712 : 186-200
Résumé
In today’s digital landscape, hackers and espionage agents are increasingly targeting Android, the world’s most prevalent mobile operating system. We introduce DeepDetector - a system based on artificial intelligence to recognize data thefts in Android. This model is based upon a large dataset comprising of clean and tainted network traffic trained using a Random Forest Classifier. DeepDetector scores high in two main areas as it achieves 82.9% accuracy for connection anomaly detection and 89.9% recall in connection anomaly detection whereas it gets 78.9% accuracy and 81.6 recall in terms of detection of under the system mounted with Raspberry Pi, automatic data collection, preparing of a dataset, training and testing of the model, as well as leak detection are ensured. In this regard, DeepDetector offers a viable way of enhancing Android user security.
Mots-clés
Android (operating system), Computer science, Computer security, Artificial intelligence, Operating system, Android, Mobile security, Data leak, Machine learning, Anomaly detection