Cyber Threat Hunting Guide: Techniques, Models, Tools, Benefits
Cybercriminals are growing more clever than ever before, making cyber threat hunting a crucial component of the network, endpoint, and data security measures. If a sophisticated external attacker or insider threat is able to circumvent basic network protection mechanisms, they may go unnoticed for weeks. During this period, they may collect sensitive data, breach private information, or get login credentials that allow them to traverse your network environment laterally.
To minimize the consequences of security breaches, it is crucial to detect them as soon as feasible. No longer can security professionals afford to wait for automated cyber threat detection systems to alert them of an approaching attack.
Cyber threat hunting is a proactive type of cyber defense. It is the proactive and iterative process of scanning via networks to find and isolate sophisticated threats that elude traditional security solutions. In contrast, standard threat management methods, such as firewalls, intrusion detection systems (IDS), sandboxes, and SIEM systems, often entail an assessment of evidence-based data after notification of a possible threat.
Cyber threat hunting helps the early identification of attacks by proactively identifying the behaviors of known and unknown adversaries using high-fidelity telemetry and the most recent threat data. It is an efficient approach for defending your company's IT networks and systems from cyber threats. A survey on the efficacy of threat hunting indicated that,
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74% of responders mentioned fewer attack surfaces
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59 percent reported improved reaction speed and accuracy
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52 percent of organizations discovered previously undiscovered threats in their networks.
In this article, we will cover what cyber threat hunting is, how it works, the importance and benefits of threat hunting, threat hunting models and tools, challenges and best practices of threat hunting, and the required skills to be a competitive threat hunter.
What is Cyber Threat Hunting?
Cyber threat hunting, also known as threat hunting, is a proactive strategy to detect previously undisclosed or existing threats that have not been remedied inside a company's network.
Threat hunting is an aggressive technique based on the "assumption of the breach," which holds that attackers are already inside a company's network and are watching and moving stealthily across it. In practice, attackers may stay within a network for days, weeks, or even months without any automated security recognizing their existence, designing and executing attacks such as advanced persistent threats(APTs). Threat hunting thwarts these assaults by searching for hidden indications of compromise (IOCs) so that they may be reduced prior to achieving their goals.
Specifically, threat hunting responsibilities include:
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Hunting for current dangers inside your business, including anything an attacker may exploit to exfiltrate data or inflict harm.
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Proactively hunting for risks that may emerge anywhere in the globe
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Setting a trap and waiting for potential attackers to find you
The main purpose of threat hunting is to try to find the following signs in the IT environment:
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Host-Based Artifacts: Examine endpoints for malware involvement in the registry, file system, and elsewhere.
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Network-Based Artifacts: Search for malware communication via session recording, packet capture, and network monitoring.
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Indicators of Compromise (IOCs): Factors, such as forensics data and log files, that may assist in the identification of suspected harmful behavior that has already happened.
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Indicators of Attack (IOAs): Although similar to IOCs, IOAs assist you to understand the occurring cyber attacks.
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Adversaries: Based on their understanding of adversary motives and TTPs, threat hunters might seek indicators of adversary presence in an organization's environment.
How Does Threat Hunting Work?
Cyber threat hunting is effective because it combines the human aspect with the huge data processing capability of a software solution. Human threat hunters, whose purpose is to use solutions and intelligence/data to locate adversaries who may evade conventional defenses by employing techniques such as living off the land, rely on data from complex security monitoring and analytics tools to proactively identify and neutralize threats.
The cyber hunting process relies heavily on human intuition, strategic and moral reasoning, and innovative problem-solving. These human attributes allow enterprises to execute threat resolutions with more speed and precision than when using automated threat detection systems alone.
For cyber threat hunting to be effective, threat hunters must first create a baseline of expected or approved occurrences to discover abnormalities more effectively. Using this baseline and the most up-to-date threat intelligence, threat hunters may next examine security data and information gathered by threat detection technologies. These technologies may include security information and event management (SIEM), managed detection and response (MDR), and further security analytics tools.
Once threat hunters are armed with data from several sources, like endpoint, network, and cloud data, they may examine your systems for possible threats, suspicious activity, and triggers that depart from the usual. Threat hunters may create hypotheses and conduct in-depth network investigations if a threat is found or if existing threat information reveals new possible risks. During these investigations, threat hunters strive to determine if a threat is malicious or benign, as well as whether the network is appropriately protected against emerging cyber threats.
A cyber threat hunt consists of the following procedures or processes designed to make the search efficient and effective:
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Hypothesis: The beginning of a threat hunt is a hypothesis, or a statement describing the hunter's beliefs about potential hazards in the environment and how to locate them. A hypothesis might comprise the tactics, techniques, and procedures (TTPs) of a suspected assailant. Threat hunters construct a logical route to detection using threat information, environmental knowledge, and their own expertise and inventiveness.
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Collect and Process Intelligence and Data: Cyber threat hunting needs quality intelligence and data. A strategy is necessary for gathering, centralizing, and processing data. A Securityecurity Information and Event Management (SIEM) tool may give insight and a log of activity in an organization's IT environment.
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Trigger: A hypothesis might serve as a trigger when sophisticated detection technologies direct threat hunters to investigate a particular system or network region.
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Investigation: Investigative technology may seek or search deeply for possibly dangerous abnormalities in a system or network, determining whether they are harmless or malicious in the end. A threat hunter may rely on sophisticated and historical information obtained by threat hunting systems like SIEM, MDR, and User Entity Behavior Analytics (UEBA) throughout an investigation. The inquiry will continue until the hypothesis is verified and anomalies are identified, or until it is determined that the hypothesis is harmless.
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Response/Resolution: Data obtained from malicious behavior that has been validated may be incorporated into automated security technologies to react, resolve, and reduce problems. Actions may include removing malware files, restoring altered or deleted files to their original state, updating firewall / IPS rules, deploying security patches, and modifying system configurations - all while gaining a better understanding of what transpired and how to improve your security against similar attacks in the future.
As hackers are always evolving and inventing new network risks, threat hunting is a never-ending endeavor. Alongside automated threat detection technology and your security team's existing threat identification and response methods, cyber threat hunting should become a daily habit inside your firm.
What are the Threat Hunting Models?
Threat hunters presume that enemies are already present in the system, and they conduct an inquiry to identify anomalous behavior that may signal hostile activity. In threat hunting, this inquiry initiation often falls into one of three categories:
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Intel-based Hunting: Intel-based hunting is a reactive hunting technique that employs indicators of compromise from threat intelligence sources. The search then follows rules given by the SIEM and threat intelligence.
Intelligence-based searches may leverage indicators of compromise, hash values, IP addresses, domain names, networks, or host artifacts supplied by intelligence-sharing systems like computer emergency response teams (CERT). From these systems, an automated alert may be exported and imported into the SIEM as structured threat information expression (STIX) and trustworthy automated exchange of intelligence information (TAXII). Once the SIEM has received the IoC-based warning, the threat hunter may analyze the malicious activities before and following the alert to discover any environmental breach.
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Hypothesis Hunting: Hypothesis hunting is a proactive hunting methodology that employs a threat hunting library. It conforms to the MITRE ATT&CK framework and uses global detection playbooks to identify malware assaults and advanced persistent threat groups.
Hypothesis-based hunting utilizes the IoAs and TTPs of attackers. The hunter detects threat actors based on the domain, environment, and attack behaviors used to generate a MITRE-compliant hypothesis. After identifying a behavior, a threat hunter will monitor activity patterns to discover, identify, and isolate the danger. Thus, the hunter can discover potential threats before they might do harm to an area.
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Custom Hunting: Custom hunting is based on industry-standard hunting methods and situational awareness. It detects irregularities in the SIEM and EDR (Endpoint Detection and Response) tools and is adaptable to client needs.
Customized or situational hunts are undertaken proactively depending on conditions, such as geopolitical crises and targeted assaults, according to the needs of the customer. These hunting activities may use both intelligence- and hypothesis-based hunting models that include IoA and IoC data.
What is Threat Hunting Maturity Model?
The Hunting Maturity Model (HMM) is a basic model developed by security architect David J. Bianco for measuring the threat hunting capacity of an organization. It gives both a "where are we now?" statistic and a plan for program enhancement.
When evaluating an organization's hunting capability, there are three things to consider:
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the quality of the data they gather for hunting
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the tools they give to access and analyze the data
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the capabilities of the analysts who utilize the data and tools to locate security events.
Among these variables, the analysts' abilities are perhaps the most crucial, since they are what enable them to transform data into detections. Therefore, each level of the Hunting Maturity Model (HMM) begins with a description of the normal degree of analytic competence at that level.
The quality of the regularly collected data from an organization's IT infrastructure is also a significant component in defining the HMM level. The greater the quantity and variety of data provided to a skilled hunter, the more results they will locate. The data collection and analysis tools are also components, although they are less significant. Given a high level of analyst talent and a huge quantity of high-quality data, it is feasible to partially compensate for toolset inadequacies. For this reason, each HMM level addresses the quality and quantity of data that is regularly gathered throughout the company but not the analytic toolset.

Figure 1. Hunting Maturity Model stages
The Hunting Maturity Model identifies five degrees of organizational hunting capability, from HMM0 (the least capable) to HMM4 (the most capable) (the most). Let's analyze each stage in depth.
HMM0 - Initial
At HMM0, a business largely uses automated alerting solutions like IDS, SIEM, and antivirus to identify harmful activities throughout the enterprise. . They may integrate signature update feeds or threat intelligence indicator feeds, and they may even generate their own signatures or indicators. However, they are supplied directly into the monitoring systems. Human activity at HMM0 is focused mostly on the resolution of alerts.
In addition, HMM0 firms gather less information from their IT systems beyond what is required to trigger their automatic alerts. Thus, even if they suddenly acquired hunting knowledge (by hiring a consultant or making a strategic hire, for example), their capacity to hunt would be severely constrained.
At HMM0, organizations are not deemed capable of hunting.
HMM1 - Minimal
An enterprise at HMM1 continues to rely mostly on automated alerts to drive their incident response process, but they do at least do regular IT data collection. These firms often want intelligence-driven detection (that is, they base their detection decisions in large part on their available threat intelligence). They often monitor the most recent danger alerts from both open and closed sources.
HMM1 businesses usually gather at least a few kinds of enterprise-wide data, and some may collect a great deal. Thus, when analysts become aware of new dangers, they may extract the important indications from these reports and review historical data to see whether they have been observed in the recent past.
Due to these search capabilities, HMM1 is the first level where hunting occurs, although in a small capacity.
HMM2 - Procedural
If you search the Internet for hunting techniques, you will get many excellent results. Typically, these processes combine an anticipated kind of input data with a specialized analytic methodology to identify a certain type of harmful behavior (e.g., detecting malware by gathering data about which programs are set to automatically start on hosts across the enterprise and using least-frequency analysis to find suspicious binaries). Organizations at the HMM2 maturity level can learn and use procedures produced by others and make modest modifications but are not yet capable of developing whole new processes.
HMM2 organizations implement these practices consistently, if not on a fixed timetable, then at least regularly.
The majority of frequently accessible techniques depend on least-frequency analysis (as of this writing, anyway). This method is only successful when there is data from several hosts. Consequently, HMM2 enterprises often gather a substantial (sometimes extremely large) quantity of enterprise-wide data.
The HMM2 capability level is the most prevalent among corporations with active hunting operations.