In the ever-evolving landscape of cybersecurity, detecting and responding to threats has become more complex. One of the more advanced techniques gaining traction is implied cyber threat hunting. Unlike traditional threat hunting, which often involves reacting to known threats and signature-based detection, implied threat hunting focuses on uncovering hidden or yet-to-be-identified threats based on contextual clues, anomalous behaviors, and patterns in data. This approach helps organizations identify potential risks before they manifest into active attacks.
Here are some effective tactics for undertaking implied cyber threat hunting:
1. Leverage Behavioral Analytics
One of the cornerstones of implied threat hunting is understanding normal behavior across your network. By establishing a baseline of what constitutes “normal” activity within your system, hunters can look for deviations that might signal a potential threat.
Anomaly Detection: Behavioral analytics tools can monitor network traffic, user behaviors, and application usage to identify abnormal patterns. For instance, if a user accesses sensitive data outside of their usual operating hours or from an unusual location, it could be a sign of malicious activity.
Machine Learning Algorithms: These can process vast amounts of data to detect subtle behavioral anomalies that may not immediately raise alarms. Over time, machine learning models become better at distinguishing between benign and malicious anomalies.
2. Focus on the Indicators of Compromise (IoCs) and Indicators of Attack (IoAs)
Although implied threat hunting focuses on emerging threats that might not yet have signatures, it’s still essential to identify subtle indicators that an attack might be underway.
IoCs: These are pieces of evidence that suggest an attack has already occurred or is in progress. Examples include unfamiliar IP addresses, unusual file hashes, or newly created user accounts.
IoAs: These are more behavioral-focused clues, showing the actions or tactics used by threat actors to initiate an attack. For example, a significant increase in failed login attempts across multiple accounts could signal an attempted brute force attack.
By hunting for IoCs and IoAs, you can uncover suspicious activity before it evolves into a full-scale breach.
3. Use Threat Intelligence Feeds to Predict Emerging Threats
Implied threat hunting requires staying ahead of potential attackers, which is where threat intelligence comes into play. By subscribing to threat intelligence feeds and continuously analyzing current cyber threats, you can detect patterns in attack methodologies.
Open-source Intelligence (OSINT): OSINT provides valuable data from a variety of online sources, such as forums, social media, or even dark web marketplaces, which can offer clues about emerging threats.
Collaborative Intelligence Sharing: Sharing threat intelligence within trusted networks, such as industry-specific Information Sharing and Analysis Centers (ISACs), can help identify patterns and tactics that could point to new forms of attack.
Predicting cyber threats based on these sources allows you to proactively adapt defenses before threats reach your systems.
4. Implement Threat Intelligence Correlation
Merely collecting vast amounts of raw threat data isn’t enough to detect implied threats. Threat intelligence correlation helps put that data into context by analyzing it across different sources.
Cross-referencing Logs: Cross-reference network logs, endpoint logs, and email security logs to identify patterns that might otherwise be missed. For example, you may detect a set of IP addresses that appear in both inbound and outbound traffic patterns, potentially indicating an exfiltration attempt.
Contextualization: Correlating threat intelligence with contextual information—such as recent organizational changes or employee behavior—can help pinpoint areas of vulnerability. A recent merger or acquisition, for instance, could create new entry points for attackers, especially if integration isn’t secure.
By connecting the dots between various data sources, you can spot emerging threats that don’t yet have clear indicators of compromise.
5. Investigate External Attack Surface Risks
One tactic often overlooked in implied cyber threat hunting is investigating the external attack surface—the entire digital perimeter of an organization that could be exposed to cyber threats. With the rise of cloud services, remote workforces, and third-party vendors, organizations may have unknown vulnerabilities.
Continuous Scanning: Use external attack surface management tools to scan for exposed assets, open ports, misconfigured cloud services, and unused accounts that could be exploited.
Third-party Risk Management: Monitor and assess the cybersecurity posture of external vendors, service providers, or partners.
Cybercriminals often use the “soft underbelly” of less-secure partners to infiltrate larger, more heavily fortified organizations.
By hunting for weak links in the external attack surface, you can prevent threat actors from gaining unauthorized access through overlooked points of entry.
6. Red Team and Purple Team Collaboration
Simulated attacks conducted by Red and Purple Teams offer a proactive method for implied threat hunting.
Red Teams: These ethical hackers perform simulated attacks to find gaps in your defenses. They attempt to mimic real-world attackers and can identify vulnerabilities that might not be apparent during traditional vulnerability scanning.
Purple Teams: Collaboration between Blue Teams (defenders) and Red Teams creates a more integrated approach to cybersecurity. Purple Team activities can enhance the ability to detect and respond to emerging threats that evade traditional signature-based tools.
These collaborative exercises offer an opportunity to simulate threats and explore how your organization might respond to new tactics or attack methods that haven’t been encountered yet.
7. Focus on User and Entity Behavior Analytics (UEBA)
User and Entity Behavior Analytics (UEBA) is a powerful tool for implied threat hunting, especially when it comes to detecting insider threats. UEBA focuses on monitoring and analyzing the behavior of users and other entities (such as devices, applications, or systems) within an organization.
Identifying Suspicious Users: UEBA can flag anomalous behaviors like privilege escalation, data access anomalies, or unusual login times, all of which could indicate an insider threat or compromised user credentials.
Detecting Lateral Movement: UEBA also helps identify patterns of lateral movement across the network, which is often a precursor to full exploitation of a system.
By leveraging UEBA tools, security teams can proactively hunt for malicious insiders or compromised accounts before they can do significant damage.
Conclusion
Implied cyber threat hunting is a forward-thinking approach that emphasizes the identification of latent risks before they evolve into active threats. By integrating behavioral analytics, threat intelligence, external attack surface management, and advanced detection techniques like UEBA and anomaly detection, security teams can stay one step ahead of cybercriminals. As the threat landscape becomes more complex, adopting these proactive strategies can significantly enhance an organization’s defense against emerging cyber threats.
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