Investigating Risks Through Threat Hunting Capability Guide

By   Olivia William
Writer , Information Security Buzz | May 22, 2023 02:13 am PST

Threat hunting is the means of exploring and searching for malicious software or unauthorized users on your network. Since a security information and event management (SIEM) system gives insight into network, endpoint, and application behavior that may indicate an attack, it is commonly acknowledged as the method to utilize while doing threat hunting.

Log information from several systems, such as servers, firewalls, security solutions, antivirus (AV), and more, is gathered and stored centrally by SIEM solutions. Security companies can adapt to the growing number of threats by adopting a culture of acceptance and preparation for compromise. As hackers adapt and find new ways to penetrate an organization’s IT infrastructure, the relevance of threat hunting rises.

Most security software can prevent about 80% of attacks but still leaves 20% that get through. The potential for catastrophic damage from the remaining dangers is significantly higher. The importance of automated threat hunting is highlighted here because it drastically cuts down the time between an intrusion and its detection.

A threat-hunting hypothesis is a declaration about a specific method or technique that could affect your company and should be the starting point of any danger hunt. The hypothesis needs to be testable in order to have a true or false result. Once you have a threat hunting hypothesis, you may perform the following seven hunts to find anomalies that could be caused by malicious actors:

Importance Of Proactive Threat Hunting

The following are the importance of proactive threat hunting.

  • Proactive threat hunting allows organizations to take a proactive approach to cybersecurity rather than relying solely on reactive measures.
  • It enables the identification and mitigation of possible threats before they could be an agent of damage or lead to a security breach.
  • Proactive threat hunting helps to uncover threats that may have evaded traditional security controls and gone undetected.
  • It allows organizations to stay ahead of emerging threats and evolving attack techniques, providing a better chance of defending against sophisticated and targeted attacks.
  • By actively hunting for threats, organizations gain valuable insights into their security posture and vulnerabilities, enabling them to make informed decisions to strengthen their defenses.
  • Proactive threat hunting helps to reduce the dwell time of threats within an organization’s environment, minimizing the potential impact and reducing the cost of remediation.
  • It enhances incident response capabilities by identifying threats at an early stage, allowing for faster and more effective response and containment.
  • Proactive threat hunting promotes a security culture within an organization, emphasizing the importance of continuous monitoring, analysis, and proactive defense.
  • It helps to build resilience and trust with stakeholders, including customers, partners, and regulators, by demonstrating a proactive and robust security stance.
  • Proactive threat hunting contributes to regulatory compliance requirements, as organizations can demonstrate their efforts to actively identify and address potential threats.
  • Overall, proactive threat hunting is a proactive and strategic approach to cybersecurity that helps organizations stay one step ahead of adversaries and safeguard their critical assets and sensitive data.

Methodologies and Techniques

A. Collection and Analysis of Threat Intelligence: 

A crucial aspect of threat hunting is collecting and analyzing threat intelligence, which involves gathering information from various sources such as threat feeds, security vendors, open-source intelligence, and dark web monitoring. The collected data is then analyzed and contextualized to identify potential threats and indicators of compromise (IOCs). Threat intelligence platforms and tools are often utilized to automate the aggregation, enrichment, and correlation of threat data, enabling faster and more effective threat identification.

B. Endpoint Detection and Response (EDR) Tools and Techniques:

Endpoint detection and response (EDR) plays a vital role in threat hunting. EDR solutions are deployed on endpoints (e.g., workstations, servers) to collect detailed telemetry and behavioral data. By continuously monitoring and analyzing endpoint activities, EDR tools can identify suspicious or malicious behavior indicative of a potential threat. Advanced detection capabilities, such as memory analysis, process monitoring, and file behavior analysis, are employed to enhance the effectiveness of EDR solutions in threat hunting.

C. Network Monitoring and Analysis: 

Network monitoring and analysis are essential for detecting and responding to network-based threats. Organizations implement network monitoring tools to capture and analyze network traffic, identifying potential security incidents. Intrusion detection systems (IDS) and intrusion prevention systems (IPS) are used to detect and block malicious activities. Packet-level analysis and log analysis are conducted to identify anomalies, suspicious patterns, and network-based attacks, enabling threat hunters to proactively hunt for threats within the network.

D. Behavioral Analytics and Anomaly Detection: 

Behavioral analytics and anomaly detection involve developing baseline profiles of normal behavior for systems, users, and network traffic. By analyzing patterns and deviations from these baselines, threat hunters can identify potentially malicious activities. Behavioral analytics techniques use machine learning algorithms to detect anomalies and flag suspicious behavior that may indicate a threat. This approach enables proactive threat hunting by identifying activities that may go unnoticed by traditional security measures.

E. User and Entity Behavior Analytics (UEBA): 

This focuses on analyzing the behavior of users and entities (e.g., applications, devices) within an organization’s environment. UEBA solutions collect and correlate data from multiple sources, including logs, network traffic, and endpoints, to create profiles of normal behavior. By applying machine learning algorithms, UEBA can identify deviations from normal behavior, such as unusual access patterns, privilege escalations, or data exfiltration, indicating potential insider threats or compromised accounts.

F. Data Correlation and Hunting in Large Datasets: 

Threat hunting often involves working with large volumes of diverse data. Data correlation techniques are employed to identify relationships and patterns within the data that may indicate malicious activities. Threat hunters analyze and hunt for threats by connecting the dots across different data sources, such as logs, network traffic, endpoint telemetry, and threat intelligence feeds. This approach helps uncover hidden threats and provides a comprehensive understanding of the attack landscape.

G. Incorporating Machine Learning and Artificial Intelligence: 

Machine learning (ML) and artificial intelligence (AI) are increasingly utilized in threat hunting to enhance detection capabilities and automate processes. ML algorithms can compute vast amounts of data, including threat intelligence and behavioral patterns, to identify anomalies, detect previously unseen threats, and improve the accuracy of threat hunting. AI-powered techniques, such as natural language processing and image recognition, enable advanced analysis and identification of threats in various forms, including malware, phishing attacks, and social engineering attempts.

7 Ways To Reduce The Risk Of Threat Hunting

  1. Identifying Suspicious Programs

Attackers deploy locally installed malware for a variety of purposes, such as command and control, persistence, automation, and data exfiltration. Malware can only be used by an attacker if it is running as a process on the endpoint. Therefore, you can use endpoints as a way to search for suspicious programs.

Both the process name and the process hash can be used to detect malicious software. More suspicious programs can be found if you provide log data from your endpoint detection and response (EDR) solution to your security information and event management (SIEM) system.

Monitoring processes or hashes gives IT a simplistic picture of activity on a certain endpoint. When more information is added to the monitoring process, such as whether or not a process is normal for a given user or which parent process gave rise to a possibly malicious process, the focus shifts from the endpoint itself to the behavior of the user.

Security logs, Sysmon, and your endpoint detection and response (EDR) solution are all good places to look for information on who started a new process and why. These permutations provide the context for deciding whether or not to conduct more research.

2. Abusive Scripting

Attackers that are trying to stay under the radar typically avoid doing anything that would alert IT to their presence. Instead, they rely on the endpoint’s native scripting support, specifically Windows Scripting Host and PowerShell.

Monitoring for scripting engine executions is the most elementary form of threat detection. CScript, WScript, and PowerShell are processes that signal a script has been launched. This visibility is likely to require additional logging for Sysmon, PowerShell operating logs, and command line parameter logging.

3. Antivirus Follow-Up 

Using antivirus data on a company-wide scale can help you determine if and where malware is spreading. Post-threat intelligence gleaned from antivirus log data could prove useful in revealing problems with elevated privileges or network segmentation. (Also See: How Shareholders and Board Members Are Affected by Cyberattacks)

4. Continuity

Once an attacker has acquired access to an endpoint, they want to maintain that access even after a reboot, user logoff, or the termination of a malicious process. Using common methods for launching applications, attackers ensure that the malicious code that establishes their control executes at system boot or login.

While often changing users, processes, and registry keys could serve as a baseline for monitoring, it’s important to also keep a watch on the relevant keys and provide as much detail as possible about relevant changes.

5. Sideways Motion

The hacker then needs to move from node to node throughout the network in search of the target system where the sensitive information is stored.

Abnormal login patterns and connections across computers can be a sign showing that a threat actor is attempting to traverse the network laterally. It’s crucial to keep an eye out for any suspicious activity with privileged accounts or signs that they’ve been breached.

6. DNS Abuse

An endpoint should only send DNS requests of the correct size to the registered DNS servers. Monitoring for changes to the hosts file or the DNS configuration, excessive DNS traffic from a single endpoint (which implies data being smuggled through port 53), and DNS rebinding requests are only some of the approaches available for detecting DNS abuse.

7. Lure the Criminal In

You can use honeypots to identify threats without putting your production environment at risk by creating a “bait” account, file, share, system, or network. In theory, you would select the aspects of the environment you wish to mimic, build a virtual one to serve as the honeypot, and then make it accessible by leaving open ports that are vulnerable to attack, using weak passwords, and so on.

Future Trends in Threat Hunting

A. Advancements in Automation and Orchestration:

  • Automation and orchestration technologies will play a significant role in the future of threat hunting.
  • Advancements in automation will enable the efficient handling of repetitive and time-consuming tasks, allowing threat hunters to focus on higher-value activities.
  • Orchestration capabilities will streamline the coordination and integration of various security tools, workflows, and processes, improving the overall effectiveness and efficiency of threat-hunting operations.

B. Integration of Threat Hunting with Other Security Operations:

  • The future of threat hunting lies in its integration with other security operations, such as incident response, vulnerability management, and security analytics.
  • By sharing data, insights, and intelligence across these functions, organizations can establish a holistic security approach that maximizes threat detection and response capabilities.
  • Integrating threat hunting with other security operations will lead to better contextualization of threats, faster incident resolution, and improved overall security posture.

C. The Role of Artificial Intelligence and Machine Learning:

  • Artificial intelligence (AI) and machine learning (ML) will continue to revolutionize threat hunting.
  • AI and ML algorithms can analyze vast amounts of data, detect patterns, and identify anomalies, enabling more accurate and timely threat detection.
  • Machine learning models can adapt and learn from new threats, improving the effectiveness of threat hunting over time.
  • Natural language processing and image recognition will enhance the analysis and identification of threats in various forms, including sophisticated malware and social engineering attacks.

D. Emerging Threat Landscape and Proactive Hunting Strategies:

  • The threat landscape is constantly evolving, with new attack techniques and vectors emerging regularly.
  • Threat hunting must adapt to these changes by continuously evolving its strategies and techniques.
  • Proactive hunting strategies will focus on staying ahead of emerging threats, leveraging threat intelligence, and actively hunting for threats that may bypass traditional security controls.
  • Threat hunters will need to develop expertise in new areas, such as cloud security, Internet of Things (IoT) security, and emerging technologies, to effectively detect and respond to evolving threats.


Not every business can afford to implement a multi-layered security system that uses several technologies to protect against modern threats. Instead of waiting for automated detection, businesses may use log data and an appropriate cybersecurity solution to actively search for hazards. By allowing security teams to observe both leading and active indicators of assaults, threat hunting expedites the detection of threats. Threat hunting helps businesses reduce their attack surface by revealing weak spots in their defenses and providing insight into how attacks are carried out and how to patch them.

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