Full Report
Learn more about the framework Talos IR uses to conduct proactive threat hunts, and how we can help you stay one step ahead of emerging threats.
Analysis Summary
This article focuses on a structured methodology for proactive threat hunting rather than specific malware or attack tools. Therefore, the summary will focus on the described framework and associated techniques.
# Tool/Technique: PEAK Threat Hunting Framework
## Overview
The PEAK Threat Hunting Framework (Prepare, Execute, and Act with Knowledge) is a structured methodology developed by Splunk SURGe and utilized by Cisco Talos Incident Response (Talos IR) to conduct comprehensive and proactive threat hunts. Its purpose is to align hunts with an organization's specific threat landscape and needs, enabling early detection and mitigation of potential risks.
## Technical Details
- Type: Framework/Methodology
- Platform: General (Applies to environment data sources like endpoints, networks, and logs)
- Capabilities: Provides a structured approach to proactive threat detection through baseline analysis, hypothesis testing, and machine learning integration.
- First Seen: Information not explicitly provided in the text.
## MITRE ATT&CK Mapping
Since this is a framework for hunting rather than a specific malicious tool, the mappings below reflect the general *goals* of the activities described within the framework, which often map to Detection and Response tactics:
- **TA0001 - Initial Access** (Hunting for mechanisms related to initial access)
- **TA0003 - Persistence** (Hunting for persistence mechanisms)
- **TA0008 - Lateral Movement** (Hunting for evidence of internal movement)
- **TA0011 - Command and Control** (Hunting for anomalous network communication)
- **TA0012 - Detection** (The overall goal of the activities described)
- T1082 - System Information Discovery
- T1016 - System Network Configuration Discovery
- **TA0013 - Exfiltration** (Hunting for data staging or exfiltration)
## Functionality
### Core Capabilities
The framework is built upon three core hunting types:
1. **Baseline Hunts:** Establishing normal operating parameters (user activity, network traffic, system processes) for anomaly detection.
2. **Hypothesis-Driven Hunts:** Proactively testing specific educated guesses about adversary behaviors based on threat intelligence.
3. **Model-Assisted Threat Hunts (M-ATHs):** Leveraging machine learning and advanced statistical models to analyze vast datasets for subtle, hidden threat patterns.
### Advanced Features
- **Dynamic Adaptation:** Hypothesis-driven hunts adapt in real-time by evolving hypotheses, adjusting scope, or pivoting focus based on unearthed evidence.
- **ML Integration (M-ATHs):** Uses machine learning models trained on normal/malicious behavior to identify sophisticated, previously undetected anomalies without relying solely on static signatures.
- **Integration with Incident Response:** Seamless escalation path from a proactive hunt discovery to Talos IR’s 24/7 Incident Response team for containment and eradication.
## Indicators of Compromise
This framework does not generate IoCs; it is designed to *discover* IoCs related to threats blending into normal operations.
- File Hashes: N/A (Generated during discovery)
- File Names: N/A (Generated during discovery)
- Registry Keys: N/A (Generated during discovery)
- Network Indicators: N/A (Generated during discovery, C2 communication might be identified as anomalous traffic)
- Behavioral Indicators: Deviations from established baselines, unusual login patterns, attempts to collect/exfiltrate data.
## Associated Threat Actors
The framework is designed to detect threats from **Insider Threats**, **Advanced Persistent Threats (APTs)**, and actors employing **novel attack techniques**.
## Detection Methods
Detection is primarily achieved via advanced analytical methods rather than traditional signatures.
- Signature-based detection: Not the focus; threats blend in with legitimate activity.
- Behavioral detection: Highly reliant on detecting deviations from the established baseline, anomalous user activity, and application of ML models.
- YARA rules if available: N/A (Framework focuses on proactive searching across data sources).
## Mitigation Strategies
- **Continuous Improvement:** Refining the hunt baseline and models over time to strengthen the security posture.
- **Early Detection:** Identifying abnormal activities before they spread.
- **Actionable Insights:** Using hunt results to strengthen defenses against current and emerging threat trends.
## Related Tools/Techniques
- Utilizing **Talos Threat Intelligence** to enrich and guide hunts.
- **Splunk** (as the platform supporting the data analysis).
- General concepts related to **Hypothesis Generation** and **Anomaly Detection**.