3-Step Guide on Cyber Threat Hunting using Machine Learning Algorithms

As cyber-attacks become more frequent and sophisticated, organizations are looking for new ways to detect and respond to security threats. One approach gaining popularity is threat hunting, a proactive technique that involves searching for signs of malicious activity in an organization’s network. Unlike traditional cybersecurity measures that rely on reactive defenses, advanced threat hunting aims to detect and neutralize threats before they cause significant damage. By actively searching for indicators of compromise (IOCs) and advanced persistent threats (APTs), organizations can identify and respond to potential breaches swiftly, minimizing their impact.

However, cyber threat hunting can be a daunting task, particularly for organizations that lack the necessary expertise and resources. Machine learning (ML) algorithms can help organizations automate and streamline the process of threat hunting, making it more effective and efficient.

In this article, we outline a three-step approach to threat hunting using ML algorithms.

Step 1: Collecting the Right Logs

The first step in threat hunting is collecting the right logs from various sources such as EDR, Proxy, DNS logs, and Firewall telemetry. The logs provide a wealth of information that can help identify suspicious activity in the network. However, with the volume of data being generated every day, it can be challenging to identify relevant information. This is where ML algorithms come in handy. By training ML models on historical data, organizations can identify patterns and anomalies that may indicate malicious activity.

For instance, ML algorithms can be used to analyze logs and identify suspicious IP addresses, domains, and user accounts. Additionally, they can analyze network traffic and identify unusual behavior that may indicate a threat. By analyzing logs using ML algorithms, organizations can focus their threat hunting efforts on areas of the network that are most likely to be targeted by attackers.

Step 2: Using IOC or Forming Hypothesis

Once the relevant logs have been collected, the next step is to use Indicators of Compromise (IOCs) or form hypothesis for threat hunting. IOCs are pieces of evidence that indicate a system has been compromised or is under attack. They can be used to search for known threats in an organization’s network. Alternatively, organizations can use hypothesis to guide their threat hunting efforts.

Here are a few sample hypotheses that organizations can use for threat hunting:

1. Prolonged Connections: Outbound/inbound connections that are prolonged may sometimes resemble suspicious activity. Further validation needs to be conducted to exclude safe connections. Probably this connectivity might be related to APT.
   
   
2. Beacon: Any inbound/outbound traffic with consistent interval or volume will be termed as beacon activity.
   
   
3. Known Bad Actor’s TTP: This hypothesis involves looking for activities that match the tactics, techniques, and procedures (TTPs) of known threat actors.
   
   
4. Unexpected Protocol Usage: This hypothesis involves looking for unusual use of protocols, such as HTTP traffic on non-standard ports.
   
   
5. Unusual DNS Query: This hypothesis involves looking for unusual DNS queries, such as DNS queries for domains that do not exist.
   
   
6. Unusual SSL Activity: This hypothesis involves looking for unusual SSL activity, such as SSL traffic on non-standard ports.
   
   
7. Security Features Disabled: This hypothesis involves looking for events related to disabling of security features, like Antivirus service, event viewer service, GPO service etc.
   
   
8. Unusual Application/Port: This hypothesis involves looking for applications that run from unusual ports.
   
   
9. MITRE ATT&CK Tactics: Selective attack tactics as defined in MITRE ATT&CK model.

Step 3: Executing the Hunt

The final step in the threat hunting process is executing the hunt. This involves searching for signs of malicious activity using the IOCs or hypotheses identified in Step 2. By using ML algorithms, organizations can automate much of the hunt process, allowing them to identify threats more quickly and efficiently.

If a threat is identified during the hunt, an incident should be raised for containment and remediation. This involves isolating the affected system from the network to prevent further damage and conducting an internal forensics investigation to determine the scope of the attack.

Conclusion

In the ever-evolving landscape of cyber threats, organizations must take a proactive stance to protect their sensitive information. Cyber threat hunting using ML algorithms provides an effective approach to detect and respond to threats in real-time. By following the 3-step process outlined in this blog post, organizations can strengthen their defenses, minimize potential damage, and stay ahead of cybercriminals.