Skill UI

This skill detects covert data exfiltration occurring over DNS tunnels. It analyzes DNS query names by computing Shannon entropy, examining query length distributions, and identifying high subdomain cardinality. It is essential for security operations, incident response, and proactive threat hunting when suspicious or encoded DNS activity is suspected.

This guide details how to perform coverage-guided fuzzing on compiled binaries using AFL++ (American Fuzzy Lop Plus Plus). It covers the entire workflow: instrumenting targets, managing input corpora, running parallel fuzzing campaigns, and triaging complex crashes using tools like CASR or GDB. Ideal for security assessment, crash discovery, and validating application security controls.

This skill detects advanced lateral movement techniques used by attackers after initial compromise, mapping them to MITRE ATT&CK TA0008. It correlates multiple data sources, including Windows event logs (4624, 4769), Sysmon telemetry, network flow data, and endpoint logs, to identify suspicious activities like Pass-the-Hash, PsExec, WMI exploitation, RDP pivoting, and SMB spreading.

This skill automates deep packet capture (PCAP) analysis using tshark and pyshark. It is invaluable for security assessments and incident response, enabling the extraction of protocol statistics, identification of suspicious network flows (e.g., port scanning, beaconing), and comprehensive extraction of Indicators of Compromise (IOCs), including IPs, domains, and URLs. It also specializes in detecting complex anomalies like DNS tunneling.

A comprehensive guide for performing static analysis of Windows Portable Executable (PE) files using tools like PEStudio and scripting libraries. This technique examines file headers, calculates section entropy, identifies suspicious API calls (e.g., process injection, persistence), and extracts obfuscated strings and Indicators of Compromise (IoCs) without executing the sample, making it a crucial step in malware triage and reverse engineering.

A comprehensive guide and workflow demonstrating how to use YARA rules for proactive threat hunting. This method helps detect unknown malware, suspicious patterns, and Indicators of Compromise (IOCs) across filesystems, memory dumps, and network artifacts, making it essential for incident response and security analysis.

This skill performs User and Entity Behavior Analytics (UEBA) to identify anomalous user activities such as impossible travel, unusual access patterns, and privilege abuse. It establishes behavioral baselines using SIEM logs and statistical analysis, helping SOC teams detect insider threats and compromised accounts by flagging deviations from normal user behavior. Use when monitoring security hygiene and suspicious activity is critical.

A comprehensive guide and Python tool for reverse engineering Rust-compiled malware. It addresses unique Rust binary challenges, such as non-null-terminated strings and monomorphization. The script extracts crate dependencies, identifies suspicious capabilities (like networking or encryption), and filters for malware-related strings, assisting security researchers in deep binary analysis.

Provides a structured workflow for performing initial triage of security incidents. This process uses established frameworks (NIST SP 800-61r3 and SANS PICERL) to classify the incident type, assign severity based on business impact, and determine the necessary containment and escalation steps. Ideal for analyzing SIEM/EDR alerts, suspicious user reports, or matching threat intelligence.

This tool monitors Certificate Transparency (CT) logs to detect unauthorized certificate issuance for owned domains, discover hidden subdomains, and track suspicious certificate activity. By querying public CT logs and APIs (like crt.sh), it helps build continuous security pipelines, identify potential phishing infrastructure, and map the external attack surface for comprehensive threat intelligence.

This system detects ransomware by combining multiple advanced techniques: real-time entropy analysis, file system I/O monitoring, and behavioral heuristics. It identifies abnormal mass file modification patterns, sudden spikes in data entropy (indicating encryption), and suspicious process activities like shadow copy deletion. Ideal for building advanced EDR detection layers to catch zero-day and unknown ransomware variants.

Accelerates CPU-bound Python workflows by translating NumPy, pandas, ML, graph, image, simulation, and IO code into NVIDIA GPU kernels using CuPy, Numba CUDA, Warp, cuDF/cuML/cuGraph, KvikIO, cuSpatial, and RAFT for dramatic speedups.