Skill UI

This skill parses various cyber threat intelligence (CTI) reports (Mandiant, CrowdStrike, etc.) using NLP and specialized libraries (stix2, mitreattack-python, spaCy). It systematically extracts adversary behaviors, maps them to MITRE ATT&CK IDs, and constructs a searchable STIX 2.1 Attack Pattern library. This is essential for threat-informed detection engineering, generating detection rules (Sigma, YARA), and validating security controls.

This skill provides a comprehensive workflow for building vendor-agnostic detection rules using the Sigma format. It empowers security operations center (SOC) engineers to translate raw threat intelligence and MITRE ATT&CK techniques into standardized, portable detection logic. The process guides users through rule validation and conversion into specific queries for major SIEM platforms, including Splunk, Elastic, and Microsoft Sentinel, ensuring robust detection coverage.

This skill provides a framework for proactively detecting service account abuse, which often occurs through anomalous interactive logons, privilege escalation, or lateral movement. It leverages EDR/SIEM telemetry (e.g., CrowdStrike, Splunk) and advanced threat intelligence rules (Sigma) to identify compromised accounts performing unexpected activities. Ideal for incident response, threat hunting, and security posture assessment.

A comprehensive guide and toolkit for extracting, parsing, and analyzing Windows Event Logs (EVTX). This method utilizes industry-leading tools like Chainsaw and Hayabusa, along with Sigma rules, to perform deep digital forensics. It is specifically designed to detect critical security indicators such as lateral movement, persistence mechanisms, privilege escalation, and unauthorized access attempts on Windows systems.

This guide focuses on developing multi-event correlation rules using Splunk SPL and Sigma format. It teaches how to detect Advanced Persistent Threats (APT) and lateral movement by chaining multiple Windows authentication, process execution, and network connection events across hosts within specified time windows. Use this to surface complex, multi-stage attack sequences that single-event detection rules typically miss.

Hayabusa is a specialized threat-hunting and fast-forensics tool designed to analyze Windows EVTX event logs. It applies a comprehensive library of Sigma detection rules to raw log data, generating a single, prioritized, chronological timeline. The output includes severity levels, MITRE ATT&CK mappings, and detailed metrics, enabling analysts to perform rapid, SIEM-free incident triage and export results for collaborative analysis.

Chainsaw is a fast, Rust-based forensic tool designed for first-response threat hunting. It analyzes collected Windows EVTX files using the comprehensive Sigma rule corpus, built-in detection rules, and high-speed keyword/regex searching. Furthermore, it uniquely analyzes critical artifacts such as shimcache, SRUM, and event-log gaps, providing robust, offline detection capabilities without needing a SIEM pipeline. It is essential for digital forensic triage and hypothesis confirmation.

This guide details how to mature and operationalize a MISP instance, transforming it from a simple repository into an active threat detection engine. Users learn to enable and cache curated threat feeds, apply warninglists to minimize false positives, query indicators using PyMISP, and automate the export of matching attributes into industry-standard detection rules like Suricata, Sigma, and Wazuh.

Provides a comprehensive framework for blue-team threat hunting and detection engineering. Users learn hypothesis-driven hunting techniques, optimize SIEM queries, and build robust detection rules using industry standards like Sigma and YARA. Essential for minimizing false positives and validating security controls in a defensive posture.

Proactive threat hunting and detection engineering using Sigma/YARA rules, SIEM queries, and ATT&CK mapping for blue-team operations. Includes hypothesis-driven workflows, rule validation, and toolchain guidance.