iNTERCEPT is a comprehensive web-based signal intelligence and RF monitoring platform built with Flask and a vanilla JavaScript frontend. It integrates over 20 distinct signal decoding modes (ADS‑B, AIS, ACARS, APRS, POCSAG/FLEX pager, SSTV, weather satellites, Bluetooth, WiFi, TSCM, drone detection, and more) into a single unified dashboard with real‑time streaming via SSE and WebSockets. The backend provides an SDR hardware abstraction layer (RTL‑SDR, HackRF, Airspy, LimeSDR, etc.) with device claiming, subprocess management, and a command‑builder pattern. The frontend features mode‑specific JavaScript modules with IIFE lifecycle methods, Leaflet map overlays, and reusable UI components (signal waveform, proximity radar, channel chart). While the primary character is an application, it also incorporates data‑research elements such as a bundled signal identification database, TLE‑based satellite pass prediction, and a heuristic signal‑guessing engine. The repository shows strong engineering with comprehensive test coverage, active maintenance, and several isolated reusable patterns like the SSE fanout system, DataStore cleanup manager, and validation utilities.
OpenCTI is an open-source cyber threat intelligence platform that ingests, manages, and analyzes threat data using the STIX 2.0/2.1 standard. The system is a monorepo containing a Node.js/TypeScript GraphQL backend (opencti-graphql), a React/Relay frontend (opencti-front), a Python client library (client-python), and a Python worker service (opencti-worker). The backend uses Elasticsearch/OpenSearch as its primary data store, Redis for caching and pub/sub, RabbitMQ for connector queues, and PostgreSQL for migrations. The frontend provides full CRUD UIs, dashboards, workspaces, playbook automation, and workflow management. Key entry points include the GraphQL API (resolvers per entity type), the frontend React app with Relay data fetching, and the Python client for programmatic access. Data flows involve ingestion via CSV/JSON/TAXII/RSS feeds, manual creation, connector enrichment, and inference rules that automatically derive relationships. External dependencies include Elasticsearch, Redis, RabbitMQ, MinIO/S3 for file storage, and optional AI services. The platform supports enterprise features (playbooks, PIR, XTM Hub) gated by license checks.
Yeti is a threat intelligence platform that ingests, enriches, correlates, and stores cyber threat observables, entities, indicators (YARA, Sigma, Suricata, regex, forensic artifacts), and DFIQ investigative questions. It provides a FastAPI-based REST API for CRUD operations, graph traversal, semantic search (via ChromaDB), and RBAC. The system uses ArangoDB as its primary graph/document database, Redis for message brokering, and Celery for background task scheduling. It includes a plugin architecture for feeds (30+ public sources like MITRE ATT&CK, CISA KEV, MISP, AbuseIPDB) and analytics (Shodan, Censys, VirusTotal, etc.). The codebase is well-structured with clean abstractions (ArangoYetiConnector, event system, polymorphic schemas, migration framework) but contains deprecated plugins and some configuration drift. It is primarily an application, not a data research project.
iKy (I Know You) is a modular OSINT (Open Source Intelligence) aggregation platform built with FastAPI, Celery, Redis, and an Angular frontend. The system ingests a username or email and concurrently executes 26+ parallel tasks across social media, data breaches, search engines, and dark web sources, returning a structured JSON contract (module, param, validation, raw, graphic, profile, timeline, tasks) consumed by a visual graph/timeline UI. Key architectural patterns include a dynamic module registry that maps route names to Celery task paths, a decorator-based task wrapper (@iky_task) that centralizes dev-mode golden-file bypass, error classification, and performance logging, and a pluggable search provider orchestration layer. The codebase demonstrates a strong separation of concerns between API routing, task execution, and data enrichment, but suffers from fragile scraping logic (LinkedIn RSC parsing, TikTok SSR regex), heavy reliance on browser session cookies, and several deprecated external APIs (Twitter v1.1, FullContact). The project is actively maintained with an extensive test suite (~4K lines) and Docker Compose deployment, though many tests are heavily mocked and may not detect real API drift.