prevent malware

Attacks to network and data sources have become more complex and harder to detect when already inside the environment. Traditional detection techniques such as security information and event management (SIEM), endpoint detection and response (EDR) become less effective in finding such unknown threats in the network. Thus, security operation centers (SOCs) deploy Threat Hunters to hunt down these hard to detect threats. There are multiples cyber threat hunting tools and techniques that can be utilized to proactively explore and hunt the environment network. Models and Frameworks also got developed to serve as base guidelines to threat hunting, enabling threat hunters to get ahead of the unknowns. Read further into the blog to find out about the different tools and techniques used during threat hunting.

Threat Hunting Tools Models

Intel-based Hunting Model

One of the threat hunting models that can be utilized is the Intel-based Hunting Model. This model is reactive as it allows input from threat intelligence sources, and threat hunters can act based on those inputs. This model follows predefined rules established by the SIEM and threat intelligence. It uses IoCs, hash values, IP addresses, domain names and networks or host artifacts provided by intelligence sharing platforms such as computer emergency response teams. With the exported automated alerts from these platforms into the SIEM, threat hunters can track down suspicious activity to see any compromise in the network and system.

Hypothesis Hunting Model

If Intel-based hunting is a reactive model and dependent on intelligence sources, the Hypothesis Hunting Model is proactive. This model uses global detection playbooks to track down sophisticated and persistent threat groups and prevent malware attacks. Hypothesis-based hunting utilizes the IRAs and TTP of the attackers. Hunters can create hypotheses just from the patterns and behaviors of attacks on the environment or domain. With the constructed hypotheses, the threat hunters then can identify and isolate a possible threat with a similar pattern to those observed previously.

Custom Hunting Model

Custom Hunting Model is dependent on situations and requirements of a particular network. It can be proactive or reactive depending on the anomalies found in the SIEM and EDR tools. It is highly customizable and flexible to use both IoA and IoC to get information on potential attacks.

Structured Hunting Model

This model is similar to the Hypothesis Hunting Model as it also depends on IoA and tactics, techniques and procedures (TTP) of an attacker. Hunters can determine the actions of threat actors based on their TTP alignments. With the Structured Hunting Model, hunters can identify possible threats before the attackers can cause damage to the environment. This model utilizes the MITRE Adversary Tactics Techniques and Common Knowledge (ATT&CK) framework to capture information about the attack.

Unstructured Hunting Model

Unlike the Structured Hunting Model, this model is aligned to the Intel-based Hunting Model, wherein hunters can use the triggers from IoC sources. These triggers can alert threat hunters to start looking for pre-and post-detection patterns. Hunters can search for the threat info as far back as the data retention and previously associated offences.

Threat Hunting Techniques

Technique #1: Test evolving hypotheses across all data

Threat hunters must not miss one single piece of data from all source points to test an evolving hypothesis. A single missing data can become a potential high-profile, expensive breach threat if undetected for too long. Hunters must look out through all real-time and historical data for a comprehensive analysis. This technique requires collecting, storing, and analyzing all security data in one place, regardless of type, source, or time horizon.

Technique #2: Conduct a historical analysis

Conducting a historical analysis is one of the most essential techniques in threat hunting. This technique requires combining live, hot data with historical analytics to accurately establish the threat path, tactics, and impact on the network. Since this technique involves a large quantity of data, a powerful data platform that can collect and store event data as long as necessary is required. Having a data analysis to look back into while threat hunting is advantageous for the iterative hunt for attacks.

Technique #3: Support creativity with agile search

Being agile is another advantage that threat hunters must utilize. The agile search technique prepares threat hunters for unprecedented outcomes. From the gathered data sources of attack patterns, one can become agile by creating and testing multiple hypotheses throughout the discovery process. An agile querying capability to pivot, filter, and iterate on their analyses is needed too. Having an elegant search can allow threat hunters to collect, analyze, and connect various data sets for richer context in real-time.

Technique #4: Integrate threat intelligence

Having an integrated threat intelligence lessens the difficulty of the hunt for threats. High confidence, high fidelity threat intelligence feeds curated by practitioners and IoCs make the hunting campaigns for advanced persistent threats efficient and effective. This third-party and open-source intelligence provides off threats and informs threat hunters of its analyses, which enriches the hunt with relevant results.

Use The Right Tools and Techniques for Threat Hunting

Different organizations require different sets of tools and techniques for threat hunting. The complexity of the threat hunting tools depends on how extensive your network or system is. Thus, clearly defining your network requirements is the first step to using the correct tools for threat hunting. 


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