How can you avoid false positives when hunting for threats?

One way to avoid false positives is to identify corroborating evidence. This is one of the primary activities of the threat hunter and SOC operator. Another approach is to develop advanced YARA rules based on malicious code reuse, to increase the malware detection accuracy, reduce false positives, and improve your threat hunting. False positives can also be reduced by using a Threat Hunting tool that includes clear signs of both malicious activity and legitimate traffic. The ideal signature will have the right balance: broad enough to identify many variants of the malware but specific enough to avoid false positives.

When hunting for threats to avoid false positives, defining clear objectives is crucial - clearly articulating what we're searching for and establishing specific criteria for identifying malicious activity. This involves understanding the normal behaviour of our systems, networks, and users. By establishing a baseline of legitimate activity, we can tailor our threat-hunting techniques to focus on anomalies that are more likely to be genuine threats.

Avoiding flase positive is crucial in threat hunting to ensure that security teams focus on legitimate threats and prevent unnecessary distraction. Some strategies to reduce false positive are, a) Tune Detection Mechanisms, b) COntextual Analysis, c) Use Multiple Data Sources, d) Implement Machine Learning and AL, e) Continuous Monitorning and Validation.

Clearly outlining objectives helps in focusing efforts effectively. It involves specifying what kind of threats you're seeking, whether it's malware, suspicious behaviors, or potential vulnerabilities. Specific objectives guide the collection and analysis of data, preventing aimless searches that might yield false leads.

It is a good practice to use different tools that perform same functions for one objective, to proof what you got on your first approach. This will help you identify and proof the result you got earlier. This will consequently reduce the errors in your final report.

To avoid false positives when hunting for threats, we need to validate our sources rigorously - relying on reputable threat intelligence feeds, cross-verifying information, and regularly assessing the quality of data to enhance detection accuracy.

Avoiding false positives in threat hunting is like navigating a maze armed with a reliable map. The key lies in the quality of your data sources, akin to having trustworthy scouts. Verify log sources like a meticulous watchmaker ensuring accurate timekeeping. Regular updates to threat feeds act as real-time intelligence on the battlefield, while cross-referencing is like consulting multiple maps for a comprehensive view. Indicators of compromise are beacons in the labyrinth, much like selecting the right key for a specific lock. Aligning these puzzle pieces creates a symphony, minimizing false notes and fortifying your defenses in the cybersecurity landscape.

Utilize ferramentas de Threat Intelligence com base em histórico e análises em tempo real. De modo geral essas ferramentas se apresentam como banco de dados dos principais ofensores e contém dados como IP, domínios, URLs, hash de arquivos e outros indicadores de comprometimento (IoC). Esses serviços permitem a importação da base de dados para seu SIEM e com isso poderá cruzar os logs com os IoCs para identificar as entidades maliciosas, mesmo quando o EDR e XDR não tenham detectado a atividade suspeita. Da mesma forma essas bases de IoC o ajudarão a identificar falsos positivos quando as entidades não constam nestas bases ativas. Algumas ferramentas conhecidas são o Defender for Threat Intelligence da Microsoft (antigo RiskIQ) e o Shodam.

Validating data sources is crucial. Ensure the sources are reputable and reliable. False or unverified information can easily trigger false positives. It's essential to vet the sources for accuracy and credibility to reduce the chances of false information leading to alerts.

Effectively harnessing threat hunting tools, including Security Information and Event Management (SIEM) or Endpoint Detection and Response (EDR) solutions, requires a strategic approach to handle the influx of alerts and minimize noise. Tuning these tools becomes paramount to curbing false positives, enabling a concentrated focus on the most critical alerts. This involves fine-tuning alert thresholds, rules, and filters aligned with your risk appetite, baseline, and feedback loop. Regular reviews and updates to your tools ensure they remain adaptive and responsive to evolving threat landscapes, enhancing the overall efficacy of your threat hunting capabilities.

Em muitas áreas usamos a regra de pareto (20% das atividades representam 80% do resultado). Porém em segurança esta pode ser uma armadilha. Alguns tipos de ataque são direcionados para seu ambiente, colaboradores ou clientes. Casos recentes de "deface" de sites bancários, lojas online e aplicações permitiram que atacantes fossem assertivos por enganarem totalmente os colaboradores e usuários. Estar atento a curvas de atendimento (SAC) e atividades fora do baseline, como por exemplo uma alta de chamados ou tráfego pode ser indício de início de um ataque direcionado. Esteja atento e cruze fontes de dados da organização para alimentar seus indicadores de comprometimento com base no negócio e não apenas IoCs tradicionais.

Having a process in place where the people using the SIEM tools (Likely the SOC Team) can bring forward suggestions for potential tuning opportunities will greatly help with the process of tuning out any false positives. With this process, it allows the SOC team to focus on more high fidelity alerts without all of the noise created by false positives. However, it is equally as important to ensure you do not over-tune the alerts, as this could create an issue where you are potentially missing malicious activity.

Cette application est aussi en cohérence avec la norme ISO 27005 qui décrit les grandes lignes d’une gestion des risques dans un contexte précis : définition du contexte d’analyse, identification et évaluation des risques encourus, possibilités de traitement ou d’acceptation de ces derniers. De mon expérience, une analyse de risque devrait même permettre de peaufiner cette réflexion autour du contexte.

To avoid false positives when hunting for threats, it's crucial to use our effective communication strategy. Maintain open channels between our threat-hunting team and relevant stakeholders, ensuring clear documentation of methodology, findings, and uncertainties in the analysis process. Regularly update and educate all involved parties on our evolving threat landscape and the rationale behind detection mechanisms. This approach fosters a shared understanding, reducing the risk of misinterpretations and contributing to a more accurate threat assessment, ultimately minimizing false positives.

Effective communication in threat hunting is crucial not just for personal understanding but also for stakeholders, including management, peers, and customers. To avoid false positives and false negatives, provide clear evidence, thorough explanations, and actionable recommendations for your findings. Utilize visualizations and dashboards as practical tools to illustrate key points without ambiguity. Maintain simplicity and clarity in language and terminology to ensure that your communication is easily comprehensible, fostering a shared understanding among diverse stakeholders.

Sharing findings and their context within your team or organization fosters collaboration and better decision-making. Providing context behind alerts and explaining why certain activities were flagged reduces confusion and helps in making informed judgments about potential threats.

You should review previous incidents for any "Lessons Learned" which could help to improve the threat hunting / investigation process going forward. Having a review process with the wider team after an incident takes place will help you to upskill everyone & will open up opportunities for deeper discussions / different ideas for the future.

Regularly review past incidents of false positives, analyze the root causes, and adjust detection criteria accordingly. Implement a feedback loop to continuously improve the effectiveness of threat-hunting activities. By identifying and correcting errors in detection methods, one can refine the approach and enhance the accuracy of threat identification, minimizing the recurrence of false positives over time.

To avoid false positives when hunting for threats: 1. Fine-tune detection rules and classify them into (Application's related, Device Miss-configurations, False-Positive, and actual security incident). 2. Contextual analysis for anomalies. 3. Use whitelisting for trusted entities and reduce the operational noise. 4. Correlate and enrich alerts. 5. Establish a baseline of normal behavior. 6. Implement UEBA for behavior analysis. 7. Automate initial investigations using your developed and updated playbooks. 8. Train your security and non-security teams. 9. Maintain a feedback loop. 10. Continuously monitor and adjust.

Analyzing past false positives is a learning opportunity. Identifying why they occurred, whether due to misconfigurations, outdated rules, or other factors, allows for process improvements. Adjusting procedures based on these insights helps in preventing similar incidents in the future.

Nous parlons ici d'un sujet technique, mais les processus peuvent être d'une aide précieuse. L’idéal est de permettre aux analystes d’améliorer de façon continue leurs résultats et pour y répondre, ils disposent d'un ensemble de moyens techniques, mais souvent pas stratégiques ou de gouvernance. Mettre à disposition des processus octroie, entre autres, aux analystes de définir une méthode pour évoluer positivement.