Detecting Insider Threats with Beehive

TL;DR

Beehive can detect a Snowden-like actor (a malicious insider) by focusing on unusual data access patterns, privilege escalation attempts, and exfiltration behaviours. This guide outlines how to configure Beehive for this purpose, covering user behaviour analytics, alerting rules, and investigation techniques.

1. Understanding the Threat

A Snowden-like actor typically exhibits these characteristics:

• Data Hoarding: Copying large amounts of sensitive data over time.
• Privilege Abuse: Using elevated permissions to access restricted information.
• Unusual Access Times: Accessing data outside normal working hours.
• Exfiltration Attempts: Transferring data to unauthorized locations (e.g., personal devices, cloud storage).

2. Configuring Beehive for User Behaviour Analytics

Beehive relies on collecting and analysing user activity logs. Ensure you’re ingesting logs from the following sources:

• Authentication Logs: Track logins, logoffs, failed attempts.
• File Access Logs: Monitor file opens, reads, writes, copies, deletes.
• Network Traffic Logs: Capture data transfer activity (e.g., uploads, downloads).
• Privilege Management Systems: Record changes to user permissions and roles.

Within Beehive, configure the relevant data sources under ‘Data Ingestion’.

3. Creating Baseline Profiles

Beehive needs to learn normal user behaviour before it can identify anomalies. Create baseline profiles for each user or groups of users with similar roles:

1. Access Frequency: How often does a user access specific files/folders?
2. Data Volume: What’s the typical amount of data accessed per session?
3. Time of Access: When does the user usually work (normal working hours)?
4. Access Locations: Where does the user typically access data from (e.g., office network, VPN)?

Beehive automatically builds these profiles over time. Review and adjust them as needed under ‘User Profiles’.

4. Defining Alerting Rules

Create rules to trigger alerts when suspicious activity is detected. Here are some examples:

• Large Data Copy: Alert if a user copies more than X GB of data in a single session.
• Access Outside Hours: Alert if a user accesses sensitive files between Y and Z hours (outside normal working hours).
• Privilege Escalation: Alert when a user requests or is granted elevated permissions.
• Unusual Destination: Alert if data is transferred to an unfamiliar IP address or cloud storage provider.

Example alerting rule configuration (using Beehive’s rule editor):

rule large_data_copy {
  meta:
    description = "Detects users copying large amounts of data"
  condition:
    user.bytes > 1024 * 1024 * 1000 // 1GB
  action:
    severity = "high"
    notify = "[email protected]"
}

5. Investigating Alerts

When an alert is triggered, follow these steps:

1. Review the Alert Details: Examine the user, files accessed, time of access, and source/destination IP addresses.
2. Check User Activity History: Look for patterns of suspicious behaviour leading up to the alert.
3. Correlate with Other Logs: Investigate authentication logs, network traffic logs, and privilege management system logs for related activity.
4. Consider Context: Is there a legitimate business reason for the user’s actions? (e.g., a project deadline, a new assignment).

6. Enhancing Detection

• Threat Intelligence Integration: Integrate Beehive with threat intelligence feeds to identify known malicious IP addresses and domains.
• Machine Learning Models: Utilize Beehive’s machine learning capabilities to detect more subtle anomalies that may not be caught by rule-based alerting.
• Regular Rule Tuning: Continuously refine your alerting rules based on false positives and new threat patterns.