Skill UI

A comprehensive guide and workflow for detecting Mimikatz and similar credential-dumping activities. It utilizes command-line pattern matching, LSASS memory access signatures, binary indicators, and in-memory analysis. Ideal for threat hunting, scoping compromises during incident response, or validating detection coverage in purple team exercises using EDR/SIEM data.

This skill provides comprehensive guidance on detecting network reconnaissance and port scanning activities using industry-standard IDS/IPS systems like Suricata and Snort. It covers various scanning techniques (e.g., SYN, FIN, NULL, Xmas scans, UDP sweeps) and advanced detection methods, including signature matching, threshold-based alerting, and traffic anomaly analysis to identify tools like Nmap and Masscan.

WizTelemetry Notification is the core notification component of the KubeSphere observability platform. It ingests alerts, cloud events, and audit logs in a multi-tenant Kubernetes environment. It intelligently routes and distributes notifications to various channels (Email, Slack, DingTalk, etc.) based on sophisticated label matching and predefined routing rules, ensuring reliable and targeted communication in complex cloud deployments.

This guide details how to mature and operationalize a MISP instance, transforming it from a simple repository into an active threat detection engine. Users learn to enable and cache curated threat feeds, apply warninglists to minimize false positives, query indicators using PyMISP, and automate the export of matching attributes into industry-standard detection rules like Suricata, Sigma, and Wazuh.

A comprehensive guide for designing and auditing empirical experiments in signal processing, audio, and speech domains. It emphasizes matching standard metrics (e.g., WER, SI-SDR, PSNR) to specific tasks, establishing current strong baselines, and systematically sweeping operating conditions (like SNR or reverberation time) to ensure robust, reliable, and publication-ready results.

A comprehensive guide to modeling the specific peer review cycle of MLSys submissions. It details the OpenReview workflow, the challenges posed by the mixed ML and systems reviewer pool, and provides strategic advice on addressing concerns related to workload representativeness, baseline strength, and evidence scope matching for high-stakes technical papers.

A comprehensive guide for authors submitting to high-impact scientific journals. It outlines the essential principles for structuring experiments and presenting evidence, emphasizing claims-evidence matching, fair benchmarking, differentiating result sources (simulation, hardware, theory), and ensuring statistical rigor. Covers best practices across computer science, control, communication, and microelectronics.