Software behavior control detects abnormal login behavior by establishing a baseline of normal user activities and monitoring deviations from this baseline. It analyzes patterns such as login time, location, device, frequency, and sequence of actions to identify suspicious activities.
How It Works:
- Baseline Establishment: The system learns typical login behaviors (e.g., user logs in from a specific IP range during business hours).
- Real-Time Monitoring: It continuously tracks login attempts, comparing them against the established patterns.
- Anomaly Detection: If a login deviates (e.g., logins at unusual hours, from a new country, or multiple failed attempts), it flags the activity as suspicious.
- Response Mechanisms: Depending on the severity, it may block the login, require multi-factor authentication (MFA), or alert administrators.
Example:
- A user normally logs in from New York (IP: 192.168.1.10) between 9 AM–6 PM.
- Suddenly, a login attempt is detected from Tokyo (IP: 203.0.113.45) at 3 AM. The system recognizes this as abnormal due to unusual location and time, triggering an alert or blocking the access.
Recommended Solution (Cloud-Based):
For enhanced security, Tencent Cloud provides Behavioral Risk Management and Login Protection services. These tools use machine learning to detect anomalies, enforce MFA, and block suspicious logins in real time. Additionally, Tencent Cloud Security Center offers centralized monitoring and automated threat responses.