Detecting compromised email accounts via login behavior characterization

Abstract

Abstract The illegal use of compromised email accounts by adversaries can have severe consequences for enterprises and society. Detecting compromised email accounts is more challenging than in the social network field, where email accounts have only a few interaction events (sending and receiving). To address the issue of insufficient features, we propose a novel approach to detecting compromised accounts by combining time zone differences and alternate logins to identify abnormal behavior. Based on this approach, we propose a compromised email account detection framework that relies on widely available and less sensitive login logs and does not require labels. Our framework characterizes login behaviors to identify logins that do not belong to the account owner and outputs a list of account-subnet pairs ranked by their likelihood of having abnormal login relationships. This approach reduces the number of account-subnet pairs that need to be investigated and provides a reference for investigation priority. Our evaluation demonstrates that our method can detect most email accounts that have been accessed by disclosed malicious IP addresses and outperforms similar research. Additionally, our framework has the capability to uncover undisclosed malicious IP addresses.

Keywords

• Compromised account detection
• Mixture model
• Login log analysis
• Attribution and forensic