Week 12 Discussion Board Chapter 9 Correlation this Post Shou ✓ Solved

Given the vast amount of known threat indicators and level of network activity today, automation has become a necessity. It’s often difficult and time-consuming for human analysts to efficiently manage large amounts of granular data and a wide range of cognitive biases. Therefore, manual threat correlation is often too slow to keep up with the amount of data generated, resulting in a high number of false negatives and positives, and outputs that are not always reproducible. However, performing manual threat correlation processes will remain crucial.

The human brain’s ability to leverage well-formed biases and perform higher-order reasoning is essential for assessing the validity and value being provided by whatever solutions your organization uses as well as building your cyber threat management team’s knowledge base. Thus, even when automated methods are employed, the final tier of analysis typically uses these human abilities for sense-making before any actions.

Conduct your own research and discuss with the group the following:

• Field Techniques of Comparison?
• Rules for Based Matching?
• What is Fuzzy Matching?
• How threat actors can evade detection via threat correlation?

Paper For Above Instructions

In today's rapidly evolving cybersecurity landscape, automation plays a critical role in managing threat detection and correlation effectively. The complexity and volume of data generated by potential and existing threats make it nearly impossible for human analysts to perform tasks manually without succumbing to cognitive biases. This paper explores key aspects of threat correlation, including field techniques of comparison, rules for based matching, fuzzy matching, and methods by which threat actors may evade detection through correlation techniques.

Field Techniques of Comparison

Field techniques of comparison involve the systematic evaluation of data points, seeking to recognize patterns that may indicate malicious activities. Two primary methods used within this context are static and dynamic analysis. Static analysis involves examining code or binary data without executing it, while dynamic analysis necessitates executing the code in a controlled environment to observe its behavior. Field comparisons may also incorporate various statistical methods to ascertain the likelihood of an event being a threat based on similar historical data (Simeonov & Bakker, 2021).

Rules for Based Matching

Rules-based matching is a fundamental technique used in threat detection processes wherein predetermined rules guide the identification of potential threats. This technique employs a set of conditions under which certain behaviors or patterns are flagged for further investigation. For example, a rule might dictate that any file transfer exceeding a specified size after business hours triggers an alert. This method can be quite effective, but it also has limitations, such as its dependence on the quality and comprehensiveness of the rules established by the cybersecurity team (Scarfone & Mell, 2020).

What is Fuzzy Matching?

Fuzzy matching refers to techniques used to identify similarities between two data sets that may not be identical but are close enough to be considered related. This method is particularly useful in addressing the variance in data entry, where slight discrepancies may lead to missed correlations. Fuzzy matching uses algorithms that can identify patterns based on string similarity or a set of characteristics, allowing for a more flexible approach than exact matching. As data sets grow and evolve, fuzzy matching becomes essential for accurate threat correlation as it significantly reduces false positives while enhancing detection capabilities (Bilal et al., 2022).

Threat Actors and Evasion Techniques

Threat actors continuously devise advanced techniques to evade detection through correlation strategies employed by cybersecurity systems. One prevalent tactic is the use of polymorphic and metamorphic malware, which changes its code structure with each iteration, making it challenging for signature-based detection methods to recognize the threat. Additionally, threat actors may leverage advanced evasion techniques such as encryption, obfuscation, or using legitimate credentials to infiltrate networks without raising alarms (Murray et al., 2021). Furthermore, attackers often practice lateral movement within networks, allowing them to alter their methods and take advantage of misconfigurations and vulnerabilities.

The Importance of Human Oversight

While automation significantly improves the efficiency of threat detection, human oversight remains critical in the decision-making process. Automated solutions excel at filtering vast quantities of data and identifying potential anomalies, but the final analysis often requires human intuition and logic. This is particularly evident in cases where correlation results yield ambiguous outputs that necessitate context-specific understanding. Cybersecurity teams must ensure ongoing training and knowledge-sharing among their members to hydrate their capabilities and adapt to new threat landscapes effectively (Bishop et al., 2020).

Conclusion

In conclusion, the integration of automation in threat correlation is not just beneficial but essential in today's digital environment. Traditional manual methods are insufficient due to the pace and volume of data generated by threat actors. By employing field techniques of comparison, rules-based matching, fuzzy matching, and human oversight, organizations can enhance their threat detection and correlation strategies. However, as cyber threats continue to evolve, organizations must remain vigilant to ensure that their threat correlation processes adapt to facilitate effective detection and response to evolving threats.

References

• Bilal, A., Tariq, N., & Munir, M. (2022). Fuzzy matching in cybersecurity: A review. Journal of Cybersecurity and Privacy, 5(1), 1-14.
• Bishop, M., He, J., & Hölbl, M. (2020). Cybersecurity knowledge-sharing practices among team members. Computers & Security, 95, 101866.
• Murray, T., Johnson, R., & Decker, C. (2021). Evasion techniques adopted by cybercriminals. Information Systems Journal, 31(3), 321-345.
• Scarfone, K., & Mell, P. (2020). Guide to Malware Incident Prevention and Handling. NIST Special Publication, 800-83.
• Simeonov, S., & Bakker, E. (2021). The Role of Static and Dynamic Analysis in Malware Detection. International Journal of Information Security, 20, 659-672.