Exploring data leakage in encrypted payload using supervised machine learning

Abstract

Data security includes but not limited to, data encryption and key management practices that protect data across all applications and platforms. In this paper, we aim to explore whether any data leakage takes place in data encryption when encrypted data is analyzed using supervised machine learning techniques. To this end, we analyze four encryption algorithms with different key sizes using five supervised learning techniques on two different datasets. The results show that as the encryption algorithms get stronger, the data leakage decreases, even though the data leakage is never zero percent.