Cyber threat hunting

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Cyber threat hunting is an active cyber defence activity. It is "the process of proactively and iteratively searching through networks to detect and isolate advanced threats that evade existing security solutions."[1] This is in contrast to traditional threat management measures, such as firewalls, intrusion detection systems (IDS), malware sandbox (computer security) and SIEM systems, which typically involve an investigation of evidence-based data after there has been a warning of a potential threat.[2][3]


Threat hunting has traditionally been a manual process, in which a security analyst sifts through various data information using their own knowledge and familiarity with the network to create hypotheses about potential threats, such as, but not limited to, Lateral Movement by Threat Actors.[4] To be even more effective and efficient, however, threat hunting can be partially automated, or machine-assisted, as well. In this case, the analyst uses software that leverages machine learning and user and entity behavior analytics (UEBA) to inform the analyst of potential risks. The analyst then investigates these potential risks, tracking suspicious behavior in the network. Thus hunting is an iterative process, meaning that it must be continuously carried out in a loop, beginning with a hypothesis.

  • Analytics-Driven: "Machine-learning and UEBA, used to develop aggregated risk scores that can also serve as hunting hypotheses"
  • Situational-Awareness Driven: "Crown Jewel analysis, enterprise risk assessments, company- or employee-level trends"
  • Intelligence-Driven: "Threat intelligence reports, threat intelligence feeds, malware analysis, vulnerability scans"

The analyst researches their hypothesis by going through vast amounts of data about the network. The results are then stored so that they can be used to improve the automated portion of the detection system and to serve as a foundation for future hypotheses.

The Detection Maturity Level (DML) model [5] expresses threat indicators can be detected at different semantic levels. High semantic indicators such as goal and strategy, or tactics, techniques and procedure (TTP) are more valuable to identify than low semantic indicators such as network artifacts and atomic indicators such as IP addresses.[citation needed] SIEM tools typically only provide indicators at relatively low semantic levels. There is therefore a need to develop SIEM tools that can provide threat indicators at higher semantic levels.[6]


There are two types of indicators:

  1. Indicator of compromise - An indicator of compromise (IOC) tells you that an action has happened and you are in a reactive mode. This type of IOC is done by looking inward at your own data from transaction logs and or SIEM data. Examples of IOC include unusual network traffic, unusual privileged user account activity, login anomalies, increases in database read volumes, suspicious registry or system file changes, unusual DNS requests and Web traffic showing non-human behavior. These types of unusual activities allow security administration teams to spot malicious actors earlier in the cyberattack process.
  2. Indicator of Concern - Using Open-source intelligence (OSINT), data can be collected from publicly available sources to be used for cyberattack detection and threat hunting.

Tactics, Techniques and Procedures (TTPs)[edit]

The SANS Institute identifies a threat hunting maturity model as follows:[7]

  • Initial - At Level 0 maturity, an organization relies primarily on automated reporting and does little or no routine data collection.
  • Minimal - At Level 1 maturity, an organization incorporates threat intelligence indicator searches. It has a moderate or high level of routine data collection.
  • Procedural - At Level 2 maturity, an organization follows analysis procedures created by others. It has a high or very high level of routine data collection.
  • Innovative - At Level 3 maturity, an organization creates new data analysis procedures. It has a high or very high level of routine data collection.
  • Leading - At Level 4 maturity, automates the majority of successful data analysis procedures. It has a high or very high level of routine data collection.

Dwell Time[edit]

Cyberattackers operate undetected for an average of 99 days, but obtain administrator credentials in less than three days, according to the Mandiant M-Trends Report.[8] The study also showed that 53% of attacks are discovered only after notification from an external party.[9]

Mean Time to Detection[edit]

In 2016, it took the average company 170 days to detect an advanced threat, 39 days to mitigate, and 43 days to recover, according to the Ponemon Institute.[10]

Example Reports[edit]

Example Threat Hunting[edit]

See also[edit]


  1. ^ "Cyber threat hunting: How this vulnerability detection strategy gives analysts an edge - TechRepublic". TechRepublic. Retrieved 2016-06-07.
  2. ^ "MITRE Kill Chain". Retrieved 2020-08-27.
  3. ^ "Threat Intelligence Platform on War Against Cybercriminals". Retrieved 2019-02-17.
  4. ^ "Cyber Threat Intelligence (CTI) in a Nutshell". Retrieved 2020-07-27.
  5. ^ Stillions, Ryan (2014). "The DML Model". Ryan Stillions security blog. Ryan Stillions.
  6. ^ Bromander, Siri (2016). "Semantic Cyberthreat Modelling" (PDF). Semantic Technology for Intelligence, Defense and Security (STIDS 2016).
  7. ^ Lee, Robert. "The Who, What, Where, When and How of Effective Threat Hunting". SANS Institute. SANS Institute. Retrieved 29 May 2018.
  8. ^ "Threat Hunting (TH)" (PDF). one esecurity.
  9. ^ "State of Malware Detection and Prevention". Ponemon Institute. Ponemon Institute. Retrieved 29 May 2018.