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

As the digital landscape becomes increasingly complex, the frequency and sophistication of cyber-attacks continue to rise. In response, organizations are seeking innovative approaches to not only detect but proactively respond to security threats. One such approach gaining prominence is cyber threat hunting, a proactive technique that involves actively searching for signs of malicious activity within an organization’s network. This method differs from traditional cybersecurity measures, which primarily rely on reactive defenses. Advanced threat hunting aims to identify and neutralize threats before they can inflict substantial damage, providing a crucial advantage in the ongoing battle against cyber threats.

Despite its significance, cyber threat hunting can be a challenging endeavor, especially for organizations lacking the necessary expertise and resources. This is where the integration of Machine Learning (ML) algorithms becomes invaluable, automating and streamlining the threat hunting process to enhance its effectiveness and efficiency.

Step 1: Collecting the Right Logs

The initial phase of threat hunting involves collecting relevant logs from diverse sources such as Endpoint Detection and Response (EDR), Proxy, Domain Name System (DNS) logs, and Firewall telemetry. While these logs offer a wealth of information, the sheer volume of data generated daily poses a challenge in identifying pertinent details. ML algorithms play a crucial role here. By training these algorithms on historical data, organizations can discern patterns and anomalies indicative of malicious activity.

ML algorithms excel at analyzing logs to identify suspicious IP addresses, domains, and user accounts. Furthermore, they can scrutinize network traffic to pinpoint unusual behavior, enabling organizations to focus their threat hunting efforts on the areas most likely to be targeted by cyber attackers.

Step 2: Using IOCs or Forming Hypotheses

Once the relevant logs are in place, the next step involves utilizing Indicators of Compromise (IOCs) or forming hypotheses to guide the threat hunting process. IOCs are crucial pieces of evidence indicating a compromised system or an ongoing cyber-attack. Organizations can use them to search for known threats within their networks. Alternatively, forming hypotheses provides a strategic approach to threat hunting.

Sample hypotheses for threat hunting include:

• Prolonged Connections: Identifying prolonged outbound/inbound connections resembling suspicious activity, potentially related to Advanced Persistent Threats (APTs).
• Beacon Activity: Recognizing consistent inbound/outbound traffic with regular intervals or volumes, indicative of beacon activity.
• Known Bad Actor’s TTP: Looking for activities aligning with the tactics, techniques, and procedures (TTPs) of known threat actors.
• Unexpected Protocol Usage: Detecting unusual usage of protocols, such as HTTP traffic on non-standard ports.

These hypotheses guide organizations in their proactive search for potential threats within their network infrastructure.

Step 3: Executing the Hunt

The final step involves executing the hunt based on the identified IOCs or hypotheses. ML algorithms come into play by automating much of the hunt process, facilitating quicker and more efficient threat identification. If a threat is confirmed during the hunt, organizations can promptly raise an incident for containment and remediation.

Containment involves isolating the affected system from the network to prevent further damage. Simultaneously, conducting an internal forensics investigation helps determine the scope of the attack and informs the organization’s response strategy.

Conclusion

In the ever-evolving landscape of cyber threats, adopting a proactive stance is imperative for organizations seeking to protect their sensitive information. Cyber threat hunting, enhanced by the power of ML algorithms, provides an effective approach to detecting and responding to threats in real-time. By following the three-step process outlined above, organizations can strengthen their defenses, minimize potential damage, and stay ahead of cybercriminals in the ongoing battle for digital security.