Skill UI

Tailscale provides a WireGuard-based, zero-trust mesh VPN solution. It establishes end-to-end encrypted, peer-to-peer connections using identity-aware controls and granular Access Control Lists (ACLs). Ideal for modernizing network security, replacing traditional VPNs, and ensuring compliant, identity-gated access across diverse environments (Cloud, On-Prem).

This skill deploys advanced anomaly detection systems for Operational Technology (OT) and Industrial Control Systems (ICS). It builds multi-dimensional baselines by analyzing SCADA communications, network traffic (Modbus, DNP3, OPC UA), and timing patterns. It detects deviations, rogue devices, and potential intrusions by profiling normal behavior, complementing signature-based IDS.

A comprehensive guide for security professionals on detecting and preventing ARP spoofing and poisoning attacks. The skill covers multiple layers of defense, including implementing Dynamic ARP Inspection (DAI) on managed switches, utilizing ARPWatch for continuous monitoring, performing deep packet inspection with Wireshark, and developing custom Python scripts for real-time anomaly detection. Essential for mitigating Man-in-the-Middle (MitM) attacks at Layer 2.

This skill monitors critical OT historian servers (such as OSIsoft PI and Ignition) located at the IT/OT boundary. It is designed to detect sophisticated cyber threats, including unauthorized access attempts, data manipulation, suspicious query patterns, and indicators of lateral movement between enterprise and control networks. Use it to validate data integrity and respond to potential ICS security incidents.

This skill provides methods and tools for detecting sophisticated cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS). It focuses on detecting anomalies like unauthorized command injection, Man-in-the-Middle attacks on industrial protocols (Modbus, DNP3), and data manipulation, which traditional IT security tools often miss. Usage involves establishing deterministic communication baselines and monitoring network traffic for deviations.

This script performs statistical analysis on Zeek connection logs (conn.log) to detect Command and Control (C2) beaconing patterns. It leverages the ZAT library to load data into Pandas DataFrames, calculates inter-arrival time statistics, and flags connections with low standard deviation relative to the mean. This technique is crucial for network threat hunting and security incident investigation when identifying malicious, periodic callbacks.

Detect unauthorized crypto-mining in AWS, Azure, and GCP by correlating GuardDuty/Defender/SCC findings, compute anomalies, network flows, and container/serverless behaviors to validate threats and guide response.

This skill detects unauthorized modifications (container drift) to running containers by monitoring for deviations from the original image state. It monitors binary execution drift, file system changes, and configuration deviations using tools like Falco and Kubernetes security contexts. Essential for SOC analysts and security teams investigating runtime compromises and enforcing immutable infrastructure.

A comprehensive guide for security teams on detecting and responding to unauthorized crypto mining operations in cloud environments (AWS, Azure). It details a multi-layered approach, utilizing cost anomaly detection, compute utilization monitoring, GuardDuty findings, and network flow log analysis (VPC/KQL) to identify resource hijacking attempts and suspicious activity across EC2, ECS, and EKS workloads.

A specialized tool for detecting anomalies within DNP3 (Distributed Network Protocol 3) communications used in critical SCADA and energy sector infrastructure. It employs deep packet inspection and baseline comparison to monitor for unauthorized control commands, protocol violations, suspicious firmware update attempts, and deviations from established operational traffic patterns.

This skill implements User and Entity Behavior Analytics (UEBA) to detect sophisticated insider threats. It builds behavioral baselines from diverse log sources (authentication, file access, network) using Elasticsearch/OpenSearch. It calculates anomaly scores and performs peer group analysis to identify deviations indicative of data exfiltration, privilege abuse, or unauthorized access patterns.

A comprehensive guide and framework for detecting attacker lateral movement within enterprise networks. It details the use of network flow analysis (Zeek), authentication log review (Windows Event Logs, Kerberos), and dedicated SIEM correlation rules (Splunk, Elastic) to identify techniques like Pass-the-Hash and RDP hopping. Ideal for threat hunters and security engineers building detection pipelines.