Code, strings, signatures, and other identifying characteristics of a malicious payload stored within a malware repository. It includes both static (file-based) and dynamic (behavioral or execution-based) components that can be analyzed for threat intelligence, detection, and prevention purposes. Examples: - Static Analysis: - Executable Code: Analyze binary data to identify unique patterns, obfuscated code, or embedded resources. - Strings Extraction: Use tools like strings or YARA rules to identify hardcoded URLs, IPs, filenames, or suspicious function calls. - Signatures: Extract cryptographic hashes (MD5, SHA256) of files to track known malware variants or detect previously unseen samples. - Dynamic Analysis: - Behavioral Observations: Monitor execution traces to capture API calls, registry modifications, or network traffic patterns indicative of malicious behavior. - Memory Analysis: Examine memory dumps to uncover injected code or runtime-decrypted payloads. - Artifacts: Record file system changes, process creation events, and command-line arguments. - Threat Intelligence Integration: - Campaign Attribution: Associate observed code snippets or signatures with known APT campaigns or ransomware families. - Indicator Sharing: Share identified Indicators of Compromise (IOCs) with threat intelligence platforms (e.g., MISP, OpenCTI). - Examples of Malware Content: - Embedded C2 domains (e.g., malicious-domain.com hardcoded in the payload). - Fileless malware indicators, such as PowerShell scripts invoking Invoke-Mimikatz. - Malware-specific signatures, such as unique PE header values for a particular strain. *Data Collection Measures:* - Collection from Public Malware Repositories: - VirusTotal: Obtain samples for static analysis. - Hybrid Analysis: Gather execution data from sandbox analysis. - Any.Run: Access interactive malware execution traces. - MalwareBazaar: Download malware samples for research and signature generation. - Automate data extraction using repository APIs (e.g., VirusTotal API for hash lookups or sample retrieval). - Internal Malware Labs: - Sandbox Environments: Use dynamic malware analysis tools such as Cuckoo Sandbox or Joe Sandbox to execute and monitor malware in a controlled environment. Capture runtime behavior logs, memory dumps, and file system changes. - Reverse Engineering: Disassemble binaries with tools like IDA Pro, Ghidra, or Radare2 to identify malicious functionality and extract code patterns. - EDR/Endpoint Telemetry: - Collect samples of malicious binaries or scripts from infected endpoints using tools like CrowdStrike, Carbon Black, or SentinelOne. - Extract memory-resident payloads from live systems for analysis. - Threat Intelligence Platforms: - Gather contextual metadata for identified malware using tools like OpenCTI, Recorded Future, or ThreatConnect. Participate in intelligence-sharing groups such as ISACs (e.g., FS-ISAC, IT-ISAC). - Custom Data Collection Pipelines: Use open-source tools like malwoverview or Maltrail to automate sample downloads, hash extraction, and IOC generation.
Monitor for contextual data about a malicious payload, such as compilation times, file hashes, as well as watermarks or other identifiable configuration information. Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on post-compromise phases of the adversary lifecycle. Consider analyzing malware for features that may be associated with malware providers, such as compiler used, debugging artifacts, code similarities, or even group identifiers associated with specific MaaS offerings. Malware repositories can also be used to identify additional samples associated with the developers and the adversary utilizing their services. Identifying overlaps in malware use by different adversaries may indicate malware was obtained by the adversary rather than developed by them. In some cases, identifying overlapping characteristics in malware used by different adversaries may point to a shared quartermaster.(Citation: FireEyeSupplyChain)
Consider use of services that may aid in the tracking of newly issued certificates and/or certificates in use on sites across the Internet. In some cases it may be possible to pivot on known pieces of certificate information to uncover other adversary infrastructure.(Citation: Splunk Kovar Certificates 2017) Some server-side components of adversary tools may have default values set for SSL/TLS certificates.(Citation: Recorded Future Beacon Certificates) Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on related stages of the adversary lifecycle, such as during Defense Evasion or Command and Control. Monitor for contextual data about a malicious payload, such as compilation times, file hashes, as well as watermarks or other identifiable configuration information. Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on related stages of the adversary lifecycle, such as during Defense Evasion or Command and Control. Monitor for logged network traffic in response to a scan showing both protocol header and body values that may buy and/or steal capabilities that can be used during targeting. Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on related stages of the adversary lifecycle, such as during Defense Evasion or Command and Control. Consider analyzing malware for features that may be associated with malware providers, such as compiler used, debugging artifacts, code similarities, or even group identifiers associated with specific Malware-as-a-Service (MaaS) offerings. Malware repositories can also be used to identify additional samples associated with the developers and the adversary utilizing their services. Identifying overlaps in malware use by different adversaries may indicate malware was obtained by the adversary rather than developed by them. In some cases, identifying overlapping characteristics in malware used by different adversaries may point to a shared quartermaster.(Citation: FireEyeSupplyChain) Malware repositories can also be used to identify features of tool use associated with an adversary, such as watermarks in Cobalt Strike payloads.(Citation: Analyzing CS Dec 2020)
Consider analyzing malware for features that may be associated with the adversary and/or their developers, such as compiler used, debugging artifacts, or code similarities. Malware repositories can also be used to identify additional samples associated with the adversary and identify development patterns over time. Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on related stages of the adversary lifecycle, such as during Defense Evasion or Command and Control. Monitor for contextual data about a malicious payload, such as compilation times, file hashes, as well as watermarks or other identifiable configuration information. Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on related stages of the adversary lifecycle, such as during Defense Evasion or Command and Control. Consider use of services that may aid in the tracking of capabilities, such as certificates, in use on sites across the Internet. In some cases it may be possible to pivot on known pieces of information to uncover other adversary infrastructure.(Citation: Splunk Kovar Certificates 2017) Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on related stages of the adversary lifecycle, such as during Defense Evasion or Command and Control.
Consider analyzing malware for features that may be associated with the adversary and/or their developers, such as compiler used, debugging artifacts, or code similarities. Malware repositories can also be used to identify additional samples associated with the adversary and identify development patterns over time. Monitor for contextual data about a malicious payload, such as compilation times, file hashes, as well as watermarks or other identifiable configuration information. Much of this activity will take place outside the visibility of the target organization, making detection of this behavior difficult. Detection efforts may be focused on post-compromise phases of the adversary lifecycle.