Threat intelligence and malware analysis essentials

Threat intelligence helps teams understand who and what poses risk, while malware analysis reveals how threats operate in practice. Together, they form a practical cycle that improves detection, response, and decision making. This cycle helps teams prioritize alerts, choose the right tools, and measure defense over time.

Start with data. Good intelligence comes from reliable sources and careful context. In malware work, you collect both samples and telemetry to confirm what works against your environment. A clear data plan keeps work focused and repeatable.

Data sources

  • Malware samples from incidents, sandboxes, or labs
  • Network telemetry: IDS alerts, DNS logs, HTTP logs
  • Open source intelligence and threat feeds
  • Incident reports and forensic notes
  • Public risk intelligence and MITRE ATT&CK mappings
  • Hashes, domains, IPs, and file metadata

Analysis methods

  • Static analysis: examine file headers, strings, packers
  • Dynamic analysis: observe behavior in a sandbox
  • Behavioral profiling and reverse engineering
  • YARA rules and IOC generation
  • Cross-sample correlation to link campaigns

Enrichment and sharing

  • Add context: campaign names, technique mapping, risk scores
  • Use machine-readable formats (STIX/TAXII) when possible
  • Share with security teams, partners, and vendors

Practical workflow

  • Define scope and collect indicators
  • Map to ATT&CK, enrich with context
  • Run analysis on samples and validate findings
  • Publish indicators and update defense
  • Review and adjust defense plans after each incident

Example: After receiving an unknown binary, analysts note a distinctive DNS pattern and a unique string. Static analysis reveals a simple packer. In a sandbox, it downloads a second stage and attempts data exfiltration. The team creates IOCs and a YARA rule to block similar files and shares the report with incident responders.

Key Takeaways

  • Build a practical workflow that links data collection, analysis, and sharing.
  • Use static and dynamic analysis to create reliable indicators.
  • Map findings to MITRE ATT&CK for better detection and response.