Proactive Threat Hunting: Steps, Tools, and Best Practices

What is proactive threat hunting?

In cybersecurity, proactive threat hunting (also known as preemptive threat hunting) is when Security Operations Center (SOC) analysts actively search for indications of a threat or attack, typically before it has caused business disruption. Instead of waiting for visible signs of damage and harm—or looking for signs of a previously seen and known attack (signature-based detection), proactive threat hunting involves researching systems, networks, and logs to uncover new, hidden, or emerging threats. The process can have automated and manual components and may involve components utilizing artificial intelligence (AI).

This intensive method differs from traditional threat hunting, which typically relies on looking for prior known attacks using tools like signature-based detection. Proactive threat hunting can also detect previously seen threats that were missed by traditional methods.

Proactive threat hunting vs. reactive and automated detection

The key difference between proactive threat hunting and reactive threat hunting is that, with reactive threat hunting, there’s already an indicator that a threat has occurred, whether it’s the detection of a matching signature to an existing threat, loss of data, a ransom request from ransomware, or damage to a system. Reactive threat hunting occurs after an alert or damage has occurred, looks for the source of the damage, and attempts to rectify the problem. Reactive threat hunting often includes, or may even heavily rely on, automated detection (for example, automated runs of signature-based detection, or ML-based detection of known IOCs).

Proactive threat hunting: Seek and rectify before any damage occurs

Reactive detection: Control and fix any damage after an attack has occurred

Automated detection: A continuous process that searches for and flags known and previously seen attacks

Core principles of proactive threat hunting

Proactive threat hunting is the process of actively searching for threats before they cause harm, typically new threats that have never been seen before. There are some core principles when engaging in proactive threat hunting. These principles include:

principle	Details
Human-led	Security analysts plan and use their expertise to create hypotheses and to actively threat hunt.
Hypothesis-driven	When looking for new and novel threats, a hypothesis has to be created to determine where to look for the possible attack. These can be based on prior attacks, security analyst intuition and hunches, and/or target suspected avenues of intrusion.
Behavior-focused	To detect a threat, proactive threat hunting often looks for anomalous and unusual behavior in the network, endpoint, and cloud.
Tool-assisted	Because of the volume of data involved, tools and automation are typically required to assist the security analyst in finding and detecting threats.
Goal	Security analysts typically aim to detect evasive, unknown, and stealthy threats, including insider and zero-day exploits that may evade typical security measures.

The proactive hunt cycle (step-by-step)

When making the decision to use proactive threat hunting as a tool of the SOC, there are some standard, typical steps involved in creating the threat hunting plan and activity.

Hypothesis generation: First the security analysts need to determine where and how the proactive threat hunting should begin. This starts with formulating a hypothesis about how the threat is being implemented, and then looks at the sources associated with the hypothesis.
Data collection: The next step involves looking at the data associated with the hypothesis. The data collection can be automated or manual, but automation will generally make proactive threat hunting feasible given the large amounts of data typically involved. Data can be collected from a number of sources, including endpoint, network traffic, cloud logs, authentication records, web proxies, and other sources.
Data analysis: The collected data is used for anomalies, patterns, and other indicators to match the hypothesis. Varying tools can be used for the data analysis, including Network Detection and Response (NDR), SIEM, and other querying and analysis platforms.
Threat detection and validation: Once an anomaly or other alert has been flagged, confirmation of whether detection was the result of a threat is needed. Validation will need to occur through further investigation to confirm an attack is under way and, if available. correlation from other detection methods.
Responses and mitigation: If an attack has been confirmed, incident response needs to be started, which can include actions like isolating the affected device or system, remediating and closing any access points uncovered in the investigation. If possible, create new detection logic or rules for the newly discovered threat.
Feedback and enrichment: The last step in proactive threat hunting is to update the detection systems with information on the threat, any relevant processes, and any other appropriate security tools.

Data sources for proactive threat hunting

Data collection is perhaps one of the key aspects of proactive threat hunting. Having as much data as possible is key for finding threats before they become harmful. Having the right data and the best data is also key, once a threat is found for incident response and mitigation. Data sources can include:

Endpoint data and logs
Network data and logs
Cloud and SaaS logs
Authentication and identity logs
Application and system logs
Threat intelligence feeds
Security tool alerts (for correlation)

Proactive threat hunting techniques & tools

A number of techniques and tools can be used in proactive threat hunting. The techniques include:

Techniques	Details
Hypothesis-driven hunting	Proactive threat hunting is based on a hypothesis, and can also drive the techniques used in how the search is implemented for signs of a threat.
Behavioral analysis	Behavioral analysis is often combined or confused with anomaly detection, but it can also be a completely separate approach. It is based on the search for specific behavior used in prior attacks, and it looks for these behaviors even when signature-based detections don’t indicate a threat.
Anomaly detection	Anomaly detection: Anomaly detection is often machine learning based and looks for anomalous behavior and unusual activity after establishing a baseline of normal activity.
IOC/IOA sweeping	Search for known indicators of compromise (IOCs) and indicators of attack (IOA) in the environment. This is typically more effective for finding known and previously seen attacks, but if any new attack uses some components of a previously seen attack, this may have some success.
TTP-based hunting (MITRE ATT&CK)	Threat hunting using the tactics, techniques and procedures found in the MITRE ATT&CK framework.
Threat intelligence integration	Use intelligence feeds from outside vendors and sources that may contain attack trends and actors to find new threats in addition to the known threat information already supplied by these feeds.
Frequency and rarity analysis	Similar to anomaly detection, this looks for the rare or first-time occurrence of users, connections and/or processes.

These techniques can be found in a number of different tools used in proactive threat hunting. These tools can include:

Tools	Details
Endpoint & telemetry tools	Include EDR, sysmon, and other sources of endpoint data.
Network tools	Zeek (Bro), Suricata, Snort, Wireshark, tcpdump, and commercial NDR platforms.
Log analysis and search	SIEM, query languages, graylog, and other log tools.
Threat intelligence tool	MITRE ATT&CK, MISP, and other threat intelligence tools
Visualization and analysis	Maltego, OSINT, Sierra, and other tools

Proactive threat hunting challenges

There are a number of challenges associated with proactive threat hunting. These include:

Incorrect hypothesis, missing hypothesis: Since proactive threat hunting starts with a hypothesis, an incorrect one or one that neglects areas where threats might exist will both fail to identify hidden threats.
Insufficient, missing data or too much/overwhelming data: Both too much and too little data have challenges. Too little, missing data, and indicators of a threat will be missed. Too much data, and it may be impossible or difficult to process all the data available, making it likely a threat is missed.
Insufficient tools: Due to the amount of data often needed in proactive threat hunting, it’s important to have both the right tools and automated tools to assist in threat hunting.
Lack of experience/knowledge combined with fatigue/lack of resources: The shortage of cybersecurity experts is continuing to get worse, meaning there are more in the field who may lack experience or there are simply positions that go unfilled. This may cause SOC analysts to be overworked, coupled with typically high volumes of alerts from cybersecurity tools, and fatigue becomes a real concern.
Lack of standardization between tools: As threats become more complex and evasive, being able to correlate alerts and data between platforms and tools is increasingly important. If there’s no way to accomplish this due to lack of standardization, it’s likely threats will be missed.
Visibility limitations and evasive threats: Some tools may have limited visibility due to location or placement in the network (one of the reasons having access to data across a variety of sources is key). The latest threats are also hiding and evading locations where common detection tools are located and even using a technique called “living off the land” (LoTL), which uses tools commonly available in the network to cause mischief.

How network-based tools like Corelight Open NDR support proactive threat hunting

NDR, or Network Detection and Response, is a cybersecurity platform that monitors network traffic to identify and respond to cyber threats and unusual network behavior. It uses advanced techniques, including AI and machine learning to detect threats as well as anomalies that might indicate a security breach. NDR solutions also offer incident response capabilities, including automating workflows and integrating with other security tools to trigger broader remediation efforts.

Corelight's Open NDR Platform supports proactive threat hunting by providing comprehensive network visibility and actionable evidence. It leverages open-source technologies like Zeek® and Suricata® to collect and analyze network traffic, transforming traffic logs into usable security insights. This allows security teams to identify, investigate, and disrupt threats more effectively, even evasive threats that are designed to hide within complex environments and evade EDR detection.

Here's a detailed breakdown of some of the benefits provided by Corelight Open NDR when used for threat hunting:

Proactive threat hunting. Open NDR provides a rich source of network telemetry and insights, allowing analysts to search for threats that may have bypassed other security measures. It also provides tools for threat hunting, including behavioral analysis, anomaly detection, threat intelligence integration, detailed search capabilities, and mapping to the MITRE ATT&CK framework.

Enhanced Visibility: Corelight's NDR platform offers broad visibility across networks, including cloud, IoT, and unmanaged devices, which can be crucial for identifying lateral movement and other malicious activities that can be missed by EDR.

Actionable Evidence: By transforming raw network data into actionable evidence, Open NDR helps security teams quickly understand the context of threats, see correlated alerts, and prioritize their response.

Integration with Other Tools: Corelight integrates with various security tools, including CrowdStrike's Falcon platform, Microsoft Defender for IoT, and Sentinel, allowing for a unified threat intelligence approach.

Faster Incident Response: The platform's ability to correlate alerts with comprehensive evidence and endpoint insights, along with AI-assisted triage accelerates incident response times.

Open Source Foundation: Corelight's use of open-source technologies like Zeek and Suricata allows for customization and integration of community-driven improvements, enhancing threat detection capabilities.

Expert Hunting: By providing the right network evidence, Corelight empowers security teams to become expert hunters, discovering undocumented devices, identifying adversaries, and disrupting attacks.

Cloud Security: In addition to hardware and virtual sensors, Corelight offers cloud sensors for platforms like AWS, enabling threat hunting and attack disruption within cloud environments.

Smart PCAP: Enables selective packet captures for in-depth investigation of suspicious activities. With Smart PCAP, Corelight has designed a highly efficient and purpose-built packet capture solution for security teams that can extend their packet lookback window up to 10x with potentially significant cost-savings vs. full packet capture solutions.

Investigator: Corelight's own tool for threat investigation, which offers advanced machine learning capabilities. Investigator helps to accelerate incident response with intelligent alerts and correlated evidence fueled by machine learning and open source communities.

Exporters: Source data is always available, avoiding proprietary formats, with support for popular SIEMs, log management systems, and analysis tools like Splunk, Elastic, and Snowflake.

Frequently asked questions

What does “proactive” mean in threat hunting?

How is proactive threat hunting different from reactive incident response?

With proactive threat hunting, SOC teams have no indication a threat has already occurred. Threat hunting is based on the idea an attack may have begun, and the SOC team is looking for indications that an attack is active in the environment. Proactive threat hunting’s goal is to find an attack before it has caused any damage. Reactive incident response happens after damage has occurred. The incident response team is then deployed to repair the damage and remediate any systems that may still be affected by the attack.

Does proactive hunting replace automated detection tools?

Proactive threat hunting does not replace automated detection tools. Most automated detection tools are looking for signs a known or previously-seen threat is present in the environment. Proactive threat hunting works hand-in-hand with automated tools and searches for new and unknown threats.

What data sources are most useful for a proactive hunt?

To get the widest visibility into the environment, it’s important to look at data sources from a number of locations and devices. These sources can include data from network devices, endpoints, servers, etc. The location of the device is also important as not all locations will be able to see east-west traffic or north-south traffic, hence the need for multiple data sources.

How often should a security team run proactive hunts?

With the availability of automated tools in cybersecurity, security teams should run proactive threat hunts on a continuous basis. While it’s obvious the security team members will not be continuously on the hunt, security tools can continuously search for indications of a possible attack and issue alerts for the team to follow up on as necessary.

How is machine learning used in proactive threat hunting?

Machine learning is a core component of today’s proactive threat hunting capabilities, and is one of the key reasons proactive threat hunting is possible. Machine learning can observe large quantities of data, establish a baseline of normal behavior, and then determine when an anomaly or unusual activity has occurred. These anomalies can then trigger alerts to indicate possible threats in the environment.

Can NDR replace EDR and/or SIEM for proactive threat hunting?

No, NDR works great in tandem with both EDR and SIEM, each covering gaps in the other tools. EDR has great visibility into what’s going on in the systems it protects, NDR has visibility into the network and data traversing the network, and SIEM is great for searches across different logs and sources.

Proactive Threat Hunting: Steps, Tools, and Best Practices

What is proactive threat hunting?

Proactive threat hunting vs. reactive and automated detection

Core principles of proactive threat hunting

The proactive hunt cycle (step-by-step)

Data sources for proactive threat hunting

Proactive threat hunting techniques & tools

Proactive threat hunting challenges

How network-based tools like Corelight Open NDR support proactive threat hunting

Frequently asked questions

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