Intro to threat hunting

In the intricate tapestry of cybersecurity, threat hunting emerges as a proactive countermeasure against the stealthy and persistent adversaries that evade conventional detection mechanisms. The dynamic nature of cyber threats necessitates an adaptive and continuous approach to security, transcending passive defense mechanisms. This article unfolds the systematic process of threat hunting, elucidating its critical role in pre-emptive defense strategies.

Conceptualizing Threat Hunting

Threat hunting is a proactive and iterative approach in cybersecurity that involves searching through networks, endpoints, and datasets to detect and isolate advanced threats that evade existing security solutions. Unlike automated threat detection systems, threat hunting presupposes a human element, combining the intuition and experience of security professionals with the data analysis capabilities of various tools.

The conceptual framework for threat hunting can be outlined by several key principles and methodologies, among which the “Diamond Model of Intrusion Analysis” is foundational. Here’s a discussion on the conceptual framework of threat hunting with references to foundational work:

Conceptual Framework

1. Hypothesis Generation: Threat hunting begins with the formulation of a hypothesis based on threat intelligence, anomalies, or known indicators of compromise (IoCs). This hypothesis guides the hunter on where to look and what behavior might indicate a compromise.
2. Data Collection and Processing: A vast amount of data is collected from various sources within the IT environment, including logs, network traffic, and endpoint data. This data must be processed and normalized to be usable for analysis.
3. Data Analysis: Hunters analyze the data using various techniques, such as behavioral analysis, statistical analysis, and machine learning, to identify patterns that suggest malicious activity.
4. Iterative Approach: Threat hunting is not a one-off task; it is an iterative process. Each cycle of hunting can refine the initial hypothesis or generate new ones, leading to continuous improvement in threat detection.
5. Utilization of Threat Intelligence: Effective threat hunting is informed by robust threat intelligence that provides context, tactics, techniques, and procedures (TTPs) of known threat actors, which helps in anticipating and identifying similar patterns in the organization’s data.

The Diamond Model of Intrusion Analysis

The Diamond Model by Caltagirone et al. (2013) provides a structured method for analyzing cyber intrusions. It offers a way to document and connect the various elements of an intrusion:

• Adversary: The individual or group responsible for the intrusion.
• Capability: The tools and techniques used by the adversary.
• Infrastructure: The physical and digital means by which the adversary projects capability.
• Victim: The target of the adversary.

The Diamond Model enables analysts to make connections between these elements, understanding how an adversary operates and adapts over time. It’s particularly useful in threat hunting for mapping out the activities of sophisticated attackers and understanding complex threat landscapes.

Preparing for the Hunt

Hypothesis development is a critical component of threat hunting, as it steers the direction of the investigation and focuses the search for malicious activity within a network. The process is informed by understanding adversaries’ tactics, techniques, and procedures (TTPs), and is greatly enhanced by knowledge of historical campaigns and intrusions. Let’s delve into how hypotheses are formed, drawing inspiration from the seminal work by Hutchins et al. in “Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains”.

Hypothesis Development Framework

1. Understanding Adversary Behaviors: Utilize knowledge of how adversaries have previously operated, including the tools they use, the sequences of actions they take, and their targets.
2. Kill Chain Integration: The concept of the “kill chain” provides a framework for understanding the stages of an intrusion, allowing threat hunters to anticipate an adversary’s next steps and tailor their hypotheses to intercept or identify activities at any stage of the kill chain.
3. Indicators of Compromise (IoCs): Use IoCs from past incidents to formulate hypotheses about potential future attacks. These indicators can include IP addresses, domain names, file hashes, network signatures, and unusual patterns of behavior.
4. TTP Analysis: Tactics, techniques, and procedures employed by attackers can provide a blueprint for what a new attack might look like. Analysts develop hypotheses by comparing current network events to known TTPs.
5. Anomaly Detection: Identify deviations from the norm within the network environment. These anomalies could suggest the presence of an adversary, even if the specific TTPs don’t match previously documented ones.
6. Threat Intelligence: Integrate threat intelligence feeds and reports into the hypothesis development process to stay current with emerging threats and incorporate this knowledge into the hunt.

Incorporating “Intelligence-Driven Computer Network Defense”

Drawing on Hutchins et al.’s work, the development of a hypothesis can be strengthened through an intelligence-driven approach. Their paper emphasizes the importance of understanding the adversary’s campaign—an orchestrated sequence of malicious activities aimed at achieving specific objectives. Here’s how their insights can be applied:

• Campaign Analysis: By analyzing campaigns, threat hunters can predict potential targets and methods of attack, forming hypotheses based on attackers’ objectives and known behaviors.
• Adaptive Learning: Incorporating feedback from ongoing defense efforts and previous intrusions can refine hypotheses, making them more precise and tailored to detect nuanced adversary behaviors.
• Strategic Threat Modeling: Develop strategic models of adversary behavior to anticipate their actions and create proactive defenses. This involves a deep dive into the motives, resources, and constraints of potential attackers.

Applying Literature Insights to Hypothesis Development

The insights from the referenced literature can lead to a multi-faceted approach to hypothesis development in threat hunting:

• Behavioral Modeling: Create models of adversary behavior that account for both technical and human factors. This can involve psychological profiling of likely attackers based on past incidents and motives.
• Scenario Planning: Develop scenarios based on potential adversary actions, using these as the basis for hypotheses. Each scenario plays out a different attack vector or method, providing a comprehensive range of hypotheses for testing.
• Red Team Exercises: Use insights from red team exercises to inform hypothesis development. These exercises simulate attacks on the network, providing valuable data on how real-world adversaries might operate.

By integrating these aspects into the hypothesis development phase of threat hunting, cybersecurity teams can leverage a rich understanding of adversary behavior, enabling them to detect and respond to threats more effectively.

The Mechanics of Active Hunting

In the domain of cybersecurity, anomaly detection is a pivotal strategy for identifying unusual patterns that may signify a security incident. Both statistical methods and artificial intelligence (AI) play a significant role in this area. Let’s dissect each approach and how it contributes to establishing baselines and detecting anomalies, with references to “Applied Network Security Monitoring: Collection, Detection, and Analysis” (Sanders & Smith, 2013).

Anomaly Detection Framework

1. Statistical Methods:
   • Baseline Establishment: Statistical methods begin by establishing what is normal within a network. This involves calculating the mean, median, mode, and standard deviation of network traffic and performance metrics over time.
   • Control Limits: Techniques such as control charts from the field of Statistical Process Control (SPC) can set thresholds (control limits) for expected behavior. Anything that falls outside these control limits can be flagged for investigation.
   • Statistical Significance: The calculation of statistical significance (e.g., using z-scores) allows analysts to determine if observed anomalies are due to chance or indicate a potential security event.
2. AI Methodologies:
   • Machine Learning: Algorithms, especially unsupervised learning ones such as k-means clustering or neural networks, can learn from data to detect outliers or patterns that deviate from the established baseline.
   • Behavioral Analytics: AI can model user or entity behavior to create a dynamic baseline that evolves over time, thereby recognizing anomalies that are contextually significant.
   • Predictive Analytics: AI systems can predict future states based on historical data, identifying anomalies when actual states deviate significantly from predicted ones.

Sanders & Smith emphasize the importance of collecting the right data and applying the correct analytical methods to effectively detect anomalies. Their work provides a foundational understanding of network security monitoring, from data collection through to the analysis phase where anomalies are identified.

Indicator-Based Approaches

Indicator-Based Approaches are crucial for identifying and responding to cyber threats. Indicators of Compromise (IoCs) are forensic data that suggest a network intrusion or malicious activity. Let’s look into how IoCs are employed, with insights from “Indicator’s of Compromise (IoCs) and Their Role in Attack Defence” by the European Union Agency for Cybersecurity (ENISA).

• IoC Types: IoCs can range from simple data points like IP addresses and domain names to complex behavioral patterns that indicate the presence of advanced persistent threats (APTs).
• Automated IoC Sharing: Tools and frameworks such as STIX/TAXII allow for the automated sharing of IoCs across organizations, enhancing the collective response to new threats.
• Signature-Based Detection: Signature-based systems use IoCs to match known threat signatures with observed events, facilitating rapid identification of known threats.
• Contextual Relevance: The relevance of an IoC depends on its context. ENISA notes that IoCs must be timely and relevant to the current threat landscape to be effective.
• IoC Lifecycle Management: IoCs have a lifecycle, from creation to retirement. This process is vital to ensuring that defense mechanisms are not cluttered with outdated or irrelevant IoCs, which can lead to inefficiencies and blind spots.

ENISA’s publication underlines the necessity of a well-managed and contextually aware use of IoCs in defense strategies, highlighting their role in not just detecting but also in preventing cyber attacks. Integrating IoCs with both traditional statistical methods and modern AI techniques can result in a robust security posture that is both reactive and proactive, capable of detecting known threats and predicting potential new ones.

Investigative Techniques

In the sphere of cyber threat hunting, investigative techniques are integral to the identification and mitigation of security threats. These techniques are rooted in a systematic approach to data analysis and the application of advanced analytical tools.

Data Analysis:

1. Data Normalization and Correlation:
   • Effective threat hunting necessitates the normalization of data from disparate sources to enable correlation and pattern recognition.
   • Peer-reviewed studies often discuss the benefits of data fusion, where different data types are integrated to provide a more comprehensive picture of potential security incidents.
2. Temporal and Spatial Analysis:
   • The timing and sequence of events are crucial in understanding a cyber threat. Analyzing the temporal aspects can reveal patterns associated with malicious activity.
   • Spatial analysis considers the source and destination of network traffic to identify potential threat zones within the infrastructure.
3. Qualitative and Quantitative Analysis:
   • Qualitative methods focus on the content and context of the data, such as the intricacies of malware communication protocols.
   • Quantitative analysis applies mathematical and statistical techniques to ascertain the likelihood of malicious activity.
4. Visual Analytics:
   • The human ability to recognize patterns can be enhanced with visual representations of data.
   • Peer-reviewed studies emphasize the role of visualization tools in the identification of outliers and patterns that automated tools may miss.

Advanced Analytical Tools:

1. Machine Learning and AI:
   • The application of machine learning algorithms in threat hunting enables the identification of complex patterns and anomalies that human analysts may overlook.
   • AI can provide predictive insights, not only detecting current threats but also anticipating future vulnerabilities and attacks.
2. Behavioral Analytics:
   • Understanding the baseline of normal behavior for users and entities allows for the detection of deviations that signal potential threats.
   • Academic sources outline methodologies for profiling ‘normal’ behavior and detecting anomalies indicative of security incidents.
3. Automation and Orchestration:
   • The integration of advanced analytics with automated response actions can significantly reduce the time between threat detection and response.
   • White papers from organizations like IBM Research discuss the benefits of security automation in handling the vast amounts of data involved in threat hunting.
4. Forensic Analysis Tools:
   • Digital forensics tools are essential for conducting in-depth investigations into security breaches, allowing for the recovery and analysis of artifacts that can provide insights into the tactics, techniques, and procedures (TTPs) of adversaries.
   • Advanced tools are designed to sift through massive data sets, identifying hidden relationships and evidence that can inform mitigation strategies.

The intersection of advanced analytical tools and investigative techniques in cyber threat hunting empowers organizations to detect, understand, and respond to threats more effectively. Organizations are advised to stay informed of the latest developments in analytical methodologies, ensuring that their threat hunting teams are equipped with the knowledge and tools necessary to protect against sophisticated cyber threats.

Response Strategies

Containment and Mitigation, as well as Post-Incident Analysis, are critical phases in the incident response process, which are essential for minimizing the impact of security incidents and reinforcing an organization’s defenses.

Containment and Mitigation

Containment Strategies:

1. Short-Term Containment:
   • This strategy may involve isolating a network segment, disconnecting affected systems, or blocking malicious network traffic.
   • The “Computer Security Incident Handling Guide” by NIST SP 800-61 provides detailed procedures for initial containment efforts, ensuring that an incident does not spread or cause additional damage.
2. Long-Term Containment:
   • Longer-term solutions include system patches, strengthening firewall rules, and applying security updates to prevent exploitation of known vulnerabilities.
   • The guide also suggests strategies for system and network hardening that can serve as long-term containment measures.
3. Eradication Measures:
   • After containing the threat, it’s necessary to remove it from the environment. This could involve deleting malware, disabling breached user accounts, or removing compromised files.
   • Eradication measures are outlined in the NIST guide to ensure thorough cleaning of the affected systems.
4. Recovery Planning:
   • Developing a plan for safely restoring systems and operations is crucial. This may involve restoring systems from backups, rebuilding systems from scratch, and conducting extensive testing before bringing systems back online.
   • Recovery must be done in a controlled manner to prevent the reoccurrence of the incident.

Mitigation Techniques:

1. Traffic Filtering and Sinkholing:
   • Techniques such as IP sinkholing can be used to redirect malicious traffic away from the network, effectively neutralizing its impact.
2. Rate Limiting and Access Control Lists (ACLs):
   • Implementing rate limiting and ACLs can prevent systems from being overwhelmed by attack traffic, a critical mitigation step described in the NIST guide.

Post-Incident Analysis

Learning from Incidents:

1. Root Cause Analysis:
   • Identifying the underlying cause of the incident is essential for preventing future occurrences.
   • Methodologies for conducting root cause analysis are integral to frameworks such as “A Framework for Cybersecurity Incident Recovery” by the IT Governance Institute.
2. Lessons Learned:
   • It’s vital to analyze the incident response process to identify what was successful and what needs improvement.
   • Documenting lessons learned helps refine incident response plans and may be required for compliance purposes.
3. Improving Security Posture:
   • Based on the insights gained from the incident, organizations can make informed decisions to enhance their security measures.
   • The post-incident phase is an opportunity to update policies, improve security protocols, and conduct additional staff training as recommended by the IT Governance Institute.
4. Reporting and Documentation:
   • Comprehensive documentation and reporting of the incident, its impact, and the response actions taken are critical for internal record-keeping, legal compliance, and communication with stakeholders.
   • The frameworks advise on the creation of a formal incident report that can be used to brief senior management and, if necessary, external parties.

Incorporating these strategies and insights into the containment, mitigation, and post-incident analysis phases not only reduces the immediate risks associated with cybersecurity incidents but also strengthens the organization’s overall resilience against future threats. The guidelines and best practices from NIST and the IT Governance Institute serve as foundational resources for organizations aiming to enhance their incident response capabilities.

Conclusion

This article consolidates the multifaceted approach to threat hunting, advancing the conversation from operational tactics to strategic imperatives within the cybersecurity domain. It underscores the indispensable nature of threat hunting in the perpetual cycle of cybersecurity defense and promotes a culture of vigilance and resilience.

References

• Caltagirone, S., Pendergast, A., & Betz, C. (2013). The Diamond Model of Intrusion Analysis. Center for Cyber Intelligence Analysis and Threat Research. Retrieved from https://apps.dtic.mil/sti/citations/ADA586960.
• Hutchins, E. M., Cloppert, M. J., & Amin, R. M. (2011). Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains. Leading Issues in Information Warfare & Security Research, 1(1), 80-106. Retrieved from https://www.lockheedmartin.com/content/dam/lockheed-martin/rms/documents/cyber/LM-White-Paper-Intel-Driven-Defense.pdf.
• Sanders, C., & Smith, J. (2013). Applied Network Security Monitoring: Collection, Detection, and Analysis. Syngress. ISBN: 978-0-12-417208-1. Retrieved from https://www.elsevier.com/books/applied-network-security-monitoring/sanders/978-0-12-417208-1.
• European Union Agency for Cybersecurity (ENISA). (n.d.). Indicators of Compromise (IoCs) and Their Role in Attack Defence. Retrieved from https://www.enisa.europa.eu/publications/indicators-of-compromise-iocs-and-their-role-in-attack-defence.
• MITRE. (n.d.). MITRE ATT&CK. Retrieved from https://attack.mitre.org/.
• Cichonski, P., Millar, T., Grance, T., & Scarfone, K. (2012). Computer Security Incident Handling Guide. NIST Special Publication 800-61 Revision 2. Retrieved from https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r2.pdf.
• IT Governance Institute. (2017). A Framework for Cybersecurity Incident Recovery. Retrieved from https://www.isaca.org/resources/isaca-journal/issues/2017/volume-4/a-framework-for-cybersecurity-incident-recovery.
• IBM Research. (n.d.). Cybersecurity. Retrieved from https://www.research.ibm.com/.
• SANS Institute. (n.d.). Threat Hunting: Maturity Model and Best Practices. Retrieved from https://www.sans.org/reading-room/whitepapers/bestprac/threat-hunting-maturity-model-37062.