Results for “depth-estimation”

Detects DNS-based data exfiltration, tunneling, and DGA communication by analyzing query logs with entropy analysis, volume anomalies, and subdomain length detection in SIEM platforms.

Conducts digital forensics investigations on compromised endpoints, including memory acquisition, disk imaging, artifact analysis, and timeline reconstruction for incident response and evidence collection.

Performs privilege escalation assessments on compromised Linux and Windows systems to identify paths from low-privilege access to root or SYSTEM-level control.

Extracts GraphQL API schemas through introspection attacks, identifies sensitive fields and mutations, and tests for query depth and complexity vulnerabilities.

Provides a structured framework for analyzing cyber intrusions by examining four core features: Adversary, Capability, Infrastructure, and Victim. Covers implementing the Diamond Model programmatically to classify and correlate intrusion events, build activity threads, and generate pivot-ready intelligence.

Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.

Investigates insider threat incidents involving employees, contractors, or trusted partners who misuse authorized access to steal data, sabotage systems, or violate security policies. Combines digital forensics, user behavior analytics, and HR/legal coordination to build an evidence-based case.

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

Analyze network traffic, logs, and data flows to detect potential data exfiltration via DNS tunneling, cloud storage uploads, encrypted channels, and other indicators of compromise.

Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality using scapy for packet capture analysis.

Surface every assumption embedded in a plan, strategy, or document, assess how critical and how validated each one is, and identify which ones to test first. Load when the user asks to map assumptions, surface hidden beliefs, find what must be true for this to work, run an assumption audit, or when deep-thinking diagnoses an assumption frame. Also triggers on "what are we assuming", "what must be true for this to work", or "find the untested beliefs". Based on David Bland and Alex Osterwalder's assumption mapping method from Testing Business Ideas.