Blog, Learning Center July 12, 2023

What is Cyber Threat Hunting?

Cyber threat hunting is a proactive security strategy that involves searching for threats within a network before they can cause significant damage. Unlike traditional methods, which are reactive and wait for an alert before taking action, threat hunting seeks to actively identify and mitigate hidden threats that have evaded initial security measures. Threat hunting involves constant monitoring and data analysis to spot suspicious behavior that may indicate a cyber attack.

This blog will explore everything you need to know about threat hunting and how it helps organizations improve their cyber resilience.

Why is threat hunting important?

In today’s complex digital landscape, cyber threats are continually evolving, and traditional defense mechanisms alone are not sufficient. Threat hunting provides an additional layer of security, and here are a few reasons why it’s crucial:

  • Proactive Defense: Instead of waiting for an alarm, threat hunting actively searches for potential threats, allowing for faster detection and response.
  • Advanced Threat Detection: Threat hunting can detect advanced and persistent threats that traditional methods often miss.
  • Risk Reduction: Threat hunting can mitigate damage and reduce risk by identifying threats earlier.
  • Better Understanding of Threats: Threat hunting can help your organization understand the types of threats it faces, improving future defenses and strategies.

What are the types of threat hunting?

Threat hunting can be categorized into two main types: hypothesis-based and analytics-driven hunting.

Hypothesis-based hunting

Hypothesis-based hunting involves creating a hypothesis about a potential threat based on threat intelligence, prior knowledge, or instinct, and then investigating whether the threat exists in the network.

Analytics-driven hunting

Analytics-driven hunting is when security teams use advanced analytical tools to find patterns or anomalies in data that may signify a security threat.

What are threat-hunting examples?

Threat hunting can come in many different shapes and forms. One example of threat hunting might involve a security analyst investigating an unexpected increase in outbound network traffic from a particular server, which could indicate data exfiltration by an attacker.Another example could be an analyst following up on a suspicious login attempt from an unfamiliar IP address, potentially suggesting a brute-force attack on the network.

What are threat-hunting techniques?

Some of the more common threat hunting techniques include anomaly detection, pattern recognition, threat intelligence feed comparisons, and hypothesis creation. Each has its own advantages, and knowing when and where to use them can significantly help your organization.

Anomaly Detection

Anomaly detection is a technique used in data analysis to identify patterns or observations in a dataset that deviate significantly from established norms. These detected anomalies, often referred to as outliers, may indicate critical incidents such as bank fraud, medical problems, or errors in a text. In the context of cybersecurity, anomaly detection is particularly valuable for identifying suspicious activities that could represent threats or intrusions, offering a proactive approach to threat detection and mitigation.

Pattern Recognition

Pattern recognition is a branch of machine learning that focuses on the automatic detection of regularities or repeating elements in data. It involves training a model using a dataset, so that the model can later recognize and classify new data based on the learned patterns. Applications of pattern recognition span across various fields such as image and speech recognition, bioinformatics, data mining, and cybersecurity, where it can be used to identify and respond to threats based on previously observed patterns of malicious activity.

Threat Intelligence Feed Comparison

Threat Intelligence Feed Comparison refers to the process of evaluating and contrasting different sources of threat intelligence data in order to determine their relative value, accuracy, and usefulness for an organization’s security needs. These feeds provide timely and actionable information about known threats such as IP addresses, URLs, or file hashes associated with malicious activities. By comparing various feeds, organizations can assess their coverage, false positive rates, update frequency, and other factors, helping them to select the most suitable threat intelligence sources and optimize their cybersecurity strategy.

Hypothesis Creation

Hypothesis creation is a fundamental step in the scientific method and research processes, where a testable prediction is made based on observation or existing knowledge. It involves formulating a proposed explanation or educated guess for a specific phenomenon, which can then be tested through experimentation or further observation. In the context of data science, AI, or cybersecurity, hypotheses might be created to explain certain trends in data, anticipate user behavior, or predict potential security threats, among other things.

What tools are used for threat hunting?

The tools that you use depend on your organization’s attack surface and how much you are looking to protect. Several tools can assist in cyber threat hunting, such as SIEM systems, EDR systems, AI and machine learning, and threat intelligence platforms.

Security Information and Event Management (SIEM) Systems

Security Information and Event Management (SIEM) systems are integral tools in cybersecurity that provide real-time analysis of security alerts generated within an organization’s network. These systems collect and aggregate log data generated across the network’s hardware and software infrastructure, from host systems and applications to network and security devices. By identifying patterns and anomalies that may suggest a security threat, SIEM tools aid in detecting and responding to incidents, providing valuable data for compliance reporting and improving overall security posture.

Endpoint Detection and Response (EDR) Systems

Endpoint Detection and Response (EDR) systems are cybersecurity tools that monitor and analyze endpoint device activities to identify, prevent, and respond to potential threats. These systems collect and store data from endpoints, employing advanced analytics to detect suspicious behavior or indicators of compromise. In the event of a security incident, EDR systems provide comprehensive insight and response capabilities, allowing security teams to rapidly investigate and mitigate threats.

Artificial Intelligence and Machine Learning Tools

Artificial Intelligence (AI) and Machine Learning (ML) tools are computational technologies used to create systems capable of learning from data, making predictions, and automating decision-making processes. AI is the broader concept of machines performing tasks that would normally require human intelligence, while ML is a subset of AI involving using algorithms to parse data, learn from it, and then make a determination or prediction. These tools are widely used across various industries, from healthcare to finance, enabling advancements in areas like image recognition, natural language processing, and predictive analytics.

Threat Intelligence Platforms

Threat Intelligence Platforms (TIPs) are security tools that help organizations collect, correlate, and analyze threat data from various sources to support defensive actions against cybersecurity threats. They gather data on potential threats such as indicators of compromise (IOCs), tactics, techniques, and procedures (TTPs) used by attackers, and provide actionable insights to proactively defend against future attacks. TIPs aid in real-time threat detection and contribute to strategic decision-making, incident response, and risk management by providing a better understanding of the threat landscape.

What is threat hunting vs detection?

While they both serve to protect against cyber threats, threat hunting and threat detection have different approaches.

Threat detection is a passive process that relies on automated systems to identify threats and generate alerts. In contrast, threat hunting is a proactive process in which security analysts actively search for threats that may have bypassed the initial security measures.

What is cyber threat hunting vs. TTP hunting?

TTP stands for Tactics, Techniques, and Procedures, and it refers to the patterns of behavior exhibited by cyber attackers. TTP hunting is a part of cyber threat hunting, focusing on identifying these behaviors to predict and prevent attacks.

While cyber threat hunting refers to the broader proactive pursuit of threats in a network, TTP hunting is a more specific methodology that focuses on detecting the modus operandi of attackers.

What is the difference between threat hunting and threat intelligence?

Threat intelligence is the process of gathering and analyzing information about potential threats to inform security decisions. It’s about understanding the landscape of threats, including the tactics, techniques, and procedures attackers use.

On the other hand, threat hunting is the proactive search within your network for threats that have evaded your security measures. It takes the insights derived from threat intelligence and uses them to actively search for hidden threats in the network.

In summary, threat hunting is a crucial aspect of an effective cybersecurity strategy, given the sophisticated and evolving nature of modern threats. By employing the right techniques and tools and continually refining your practices, you can better protect your network and your organization’s valuable data.

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In one powerful portal or via API, understand who is targeting you, create automated workflows, and integrate ASI data into your SIEM, ticketing system, and vulnerability management tools.