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Systematically evaluates cyber-campaign evidence to attribute a cyber operation to a specific threat actor. This skill utilizes advanced analytical frameworks like the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh multiple indicators, including infrastructure overlaps, TTP consistency, malware similarity, and operational patterns, providing confidence-weighted attribution assessments for incident response.

This tool detects abnormal access patterns across major cloud storage services (AWS S3, GCS, Azure Blob Storage). By analyzing audit logs like CloudTrail, it uses statistical baselines and time-series anomaly detection to identify suspicious activities such as after-hours bulk downloads, access from new IPs, or unusual API call spikes. It is crucial for investigating potential data exfiltration incidents or building robust cloud security detection rules.

A specialized tool designed for incident responders and threat hunters. It extracts critical configuration data—such as C2 server addresses, malleable profiles, watermarks, and communication settings—from Cobalt Strike beacon payloads found in PE files or memory dumps. This analysis is crucial for mapping attacker infrastructure and improving defensive detection rules.

This tool parses and analyzes Cobalt Strike Malleable C2 profiles, extracting critical network indicators such as HTTP/DNS transforms, URIs, headers, jitter, and sleep cycles. It is essential for reverse-engineering captured profiles or building robust network detection signatures for threat hunting and incident response.

A comprehensive skill for performing deep security audits on Ethereum smart contracts. Utilizes both static analysis (Slither) and symbolic execution (Mythril) to detect critical vulnerabilities such as reentrancy, integer overflows, and access control issues. This process is crucial for ensuring the security and immutability of financial assets before mainnet deployment.

This skill details the process of reverse engineering Go-compiled malware using Ghidra. It involves parsing Go-specific metadata structures like buildinfo and pclntab to recover stripped or obfuscated function names. Users can also extract embedded module paths and dependency strings, providing critical insight into the malware's capabilities and third-party dependencies.

This tool parses Kubernetes API server audit logs (JSON lines) to detect critical security events. It specifically targets unauthorized access, secret enumeration, RBAC escalation, privileged pod creation, and shell access (exec/attach). Use it for incident response, threat hunting, or building SIEM detection rules.

This guide details the comprehensive process of using the Linux Audit framework (auditd) to detect advanced security threats. It covers deploying specific rules to monitor critical system files, detecting unauthorized privilege escalation, process injection, and suspicious system calls. Users can then leverage ausearch and aureport to efficiently query, reconstruct timelines, and summarize findings for forensic analysis and compliance auditing.

This skill provides a structured approach to detecting kernel-level rootkits in Linux environments. It utilizes memory forensics with Volatility3 (checking syscalls, hidden modules) and compares system views (/proc vs /sys) to identify tampered kernel structures, hooked syscalls, and concealed processes. It also integrates live scanning tools like rkhunter for comprehensive security incident investigation.

A comprehensive guide for digital forensics analysts to investigate compromised Linux systems. This workflow details the collection and analysis of critical system artifacts, including authentication logs, shell histories, SSH keys, and system configurations. Use this methodology during incident response to detect rootkits, unauthorized persistence mechanisms, and reconstruct full attacker activity.

This skill details the forensic analysis of Windows LNK shortcut files and Jump Lists. These artifacts record critical user activity, including file access and program execution, even after the original source files have been deleted. It covers using specialized tools (LECmd, JLECmd) and Python parsing techniques to reconstruct user timelines, making it essential for incident response and deep digital forensics examinations.

This skill enables deep analysis of malware families using the Malpedia API. It allows users to retrieve canonical aliases, associated threat actors, and YARA detection rules, and map complex relationships like loader-payload chains. Ideal for threat intelligence research, incident response, and building automated detection signatures.