# LOGAR: Edge-Thin Log Analysis & Temporal Verification System **LOGAR** is an enterprise log aggregation, verification, and anomaly detection architecture designed for heterogeneous server fleets (Windows & Linux). It combines lightweight zero-state edge forwarders with a centralized cloud hub that applies OpenPGP encryption, authenticated TCP streaming, temporal persistence tracking across 12-hour evaluation windows, and an automated 4-run rule to filter out transient infrastructure blips before reporting verified anomalies to **Hermes**. --- ## Table of Contents 1. [Core Philosophy](#core-philosophy) 2. [Architecture & Data Flow](#architecture--data-flow) 3. [Security & Cryptographic Model](#security--cryptographic-model) 4. [Cloud-Side Temporal Persistence & 4-Run Rule](#cloud-side-temporal-persistence--4-run-rule) 5. [Agentic Hermes Integration](#agentic-hermes-integration) 6. [Dynamic Machine & Domain Identification](#dynamic-machine--domain-identification) 7. [Repository & Shippables Structure](#repository--shippables-structure) 8. [Getting Started & Installation](#getting-started--installation) 9. [Running Tests](#running-tests) 10. [Automated Releases via Gitea Actions](#automated-releases-via-gitea-actions) --- ## Core Philosophy ### 1. Edge Thinness & Zero State Site agents running on Windows and Linux act strictly as lightweight forwarders: - **No Local Database**: Clients maintain zero state and no local SQLite or cache files. - **Source-Level Filtering**: Agents upload candidate entries spanning from informational events up to errors (`INFO`, `WARNING`, `ERROR`, `CRITICAL`), while stripping verbose debugging noise (`DEBUG`, audit entries) and skipping any entries older than 24 hours. - **End-to-End Encryption**: Logs are encrypted using the server's OpenPGP public key before leaving the edge node. - **Secure TCP Sockets**: Ingestion occurs over low-overhead authenticated TCP sockets rather than bulky HTTP/HTTPS endpoints. ### 2. Cloud-Side Temporal Persistence The central Python/TCP hub handles the heavy lifting: - State tracking is managed centrally in SQLite (`logar_state.db`). - Candidate issues are evaluated over a **12-hour temporal evaluation window**. - An issue must persist across **at least 4 consecutive runs / cycles** to be confirmed as a genuine system anomaly. Transient blips and sporadic spikes are filtered out automatically. ### 3. Agentic Integration with Hermes Instead of human engineers manually diving through noisy logs, **Hermes** ingests pre-filtered, 4-run validated anomalies directly from the cloud hub (`GET /api/hermes/report`), treating them as verified system artifacts to trigger precise team notifications. --- ## Architecture & Data Flow ```mermaid graph TB subgraph Edge Nodes [State-Tracking Edge Forwarders] W[Win_Client.py / Win_Client.exe / Win_Client.pyz
Windows Event Log Application] L[Linux_Client.py / Linux_Client.bin
systemd journalctl -p info] end subgraph Security Layer [Security & Framing] E[OpenPGP Payload Encryption
Server Public Key & Fingerprint] S[Length-Prefixed Framing
4-byte Big-Endian + Auth Envelope] end subgraph Cloud Hub [LOGAR Central Server Hub] TCP[Authenticated TCP Listener
Port 9443] DEC[OpenPGP Decryption
Server Private Key] DB[(SQLite Persistence
active_issues & ingest_runs)] RULE{12h Window &
4-Run Rule} end subgraph Agentic Reporting [Downstream Integration] API[FastAPI / Uvicorn Reporting
Port 8443] HERMES[Hermes Agent
GET /api/hermes/report] end W --> E L --> E E --> S S -->|TCP Stream| TCP TCP --> DEC DEC --> RULE RULE --> DB DB --> API API --> HERMES ``` --- ## Security & Cryptographic Model ### Pure-Python OpenPGP (RFC 4880) - **Zero OS Binary Dependency**: Utilizes `pgpy` and `cryptography` in pure Python. **No native GnuPG or `gpg` binary installation is required** on the server, Windows nodes, or Linux nodes. - **First-Run Automatic Key Generation**: On the first launch, if `server_config.json` is missing, `Server.py` automatically generates: - An OpenPGP RSA 2048 keypair with encryption-only usage flags. - An armored private key (`private_key`) and public key (`public_key`). - A SHA-256 public encryption fingerprint (`server_fingerprint`). - A cryptographically random authentication secret token (`auth_token`). - **Client Configuration Exporter**: ```bash python Server.py --create-client-config --server-host 127.0.0.1 --server-port 9443 --client-out client_config.json ``` Produces an anonymous client config containing only the server socket coordinates, authentication token, and the encryption-only public key & fingerprint. - **Socket Protocol Framing**: - `[4 bytes big-endian unsigned int]` : Total envelope length. - `[JSON Envelope]` : ```json { "auth_token": "", "timestamp": "2026-09-03T...", "encrypted_payload": "-----BEGIN PGP MESSAGE-----\n..." } ``` - Unauthorized clients or invalid authentication tokens are rejected immediately. --- ## Cloud-Side Temporal Persistence & 4-Run Rule Incoming candidate logs are tracked in SQLite table `active_issues`: - **Issue Fingerprint**: Formatted as `{site_name}:{server}:{signature}`. - **12-Hour Evaluation Window**: - When an issue is observed, the hub compares `(now - last_seen)`. - If more than 12 hours have passed since the issue was last recorded, the previous window is expired and the cycle resets to `run_count = 1` with status `TRANSIENT`. - **4-Run Rule**: - For each distinct run batch, `run_count` increments. - Issues with `run_count < 4` are marked as `TRANSIENT` and ignored by downstream reporting. - When `run_count >= 4` within the active 12-hour window, the status transitions to `VERIFIED`. --- ## Agentic Hermes Integration The server hub serves a REST reporting API (default port `8443`): ### `GET /api/hermes/report` Returns exclusively **verified anomalies** that have satisfied the 4-run rule within the active 12-hour evaluation window: ```json [ { "fingerprint": "corp.internal:web-app-01.corp.internal:NginxWorkerCrash", "site": "corp.internal", "server": "web-app-01.corp.internal", "signature": "NginxWorkerCrash", "severity": "ERROR", "message": "Worker process 4120 terminated with signal 11", "os_type": "linux", "first_seen": "2026-09-03T09:00:00+00:00", "last_seen": "2026-09-03T21:00:00+00:00", "consecutive_runs": 4, "evaluation_window": "12h", "verified": true, "status": "VERIFIED" } ] ``` ### `GET /api/hermes/all` Diagnostic endpoint listing all active issues (both `TRANSIENT` candidate blips and `VERIFIED` anomalies). ### `GET /health` Returns hub health, encryption fingerprint, and listener ports. --- ## Dynamic Machine & Domain Identification Client configurations intentionally contain **no machine name or site name**. Both forwarders dynamically identify their host and domain at runtime via `get_machine_identifier()`: 1. **Fully Qualified Domain Name (FQDN)**: Checked via `socket.getfqdn()`. 2. **OS-Specific Domain Discovery**: - **Windows**: Checks Active Directory environment variable `USERDNSDOMAIN` / `USERDOMAIN`. - **Linux**: Parses `/etc/resolv.conf` `domain` and `search` directives. 3. **Reverse DNS Lookup**: Resolves canonical hostname via `socket.gethostbyaddr`. 4. **Fallback**: Local hostname `socket.gethostname()`. The server automatically infers site attribution from domain qualifiers (e.g. `node01.corp.internal` $\rightarrow$ site `corp.internal`). --- ## Repository & Shippables Structure ``` LOGAR/ ├── .gitea/ │ └── workflows/ │ ├── ci.yml # Continuous Integration automated test suite (runs on every push) │ └── release.yml # Automated standalone binary release workflow (runs on tag v*) ├── .gitignore # Ignore venv, caches, DBs, and private keys ├── requirements.txt # Unified dependencies ├── README.md # Comprehensive documentation ├── Server.py # Central TCP server and Hermes API ├── Win_Client.py # Windows edge forwarder ├── Linux_Client.py # Linux edge forwarder ├── server_config.sample.json # Central server sample configuration ├── package_dist.py # Multi-platform standalone binary packaging script ├── upload_release.py # Direct Gitea REST API release asset publisher ├── test_pipeline.py # End-to-end integration test ├── tests/ # Unified unit test suites │ ├── test_server.py # Server unit tests │ ├── test_win_client.py # Windows client unit tests │ └── test_linux_client.py # Linux client unit tests └── out/ # Edge forwarder deployment packages ├── win_client/ │ ├── client_config.sample.json # Reference client configuration │ └── README.md # Windows service installation & configuration guide └── linux_client/ ├── client_config.sample.json # Reference client configuration └── README.md # Linux service installation & configuration guide ``` --- ## Getting Started & Installation ### 1. Central Server Hub 1. **Install dependencies**: ```bash pip install -r requirements.txt ``` 2. **Start the server** (generates `server_config.json` and keypair on first run): ```bash python Server.py ``` 3. **Export a client configuration**: ```bash python Server.py --create-client-config --server-host --server-port 9443 --client-out client_config.json ``` ### 2. Windows Client Deployment #### Option A: Precompiled Standalone Executable (Recommended) 1. Download `Win_Client.exe` (or `Win_Client.pyz`) from the repository releases. 2. Place `client_config.json` (exported from the server) in the same directory. 3. Run manually or schedule via Task Scheduler (every 3 hours): ```powershell .\Win_Client.exe --hours 24 ``` #### Option B: Python Source Execution 1. Copy `Win_Client.py`, `requirements.txt`, and `client_config.json` to the target machine. 2. Install client dependencies: ```powershell python -m pip install -r requirements.txt ``` 3. Run manually or schedule via Task Scheduler: ```powershell python Win_Client.py --hours 24 ``` ### 3. Linux Client Deployment #### Option A: Precompiled Standalone Binary (Recommended) 1. Download `Linux_Client.bin` from the repository releases. 2. Place `Linux_Client.bin` and `client_config.json` into `/opt/logar/` and make it executable: ```bash chmod +x /opt/logar/Linux_Client.bin ``` 3. Run via cron or systemd timer: ```bash 0 */3 * * * /opt/logar/Linux_Client.bin --hours 24 ``` #### Option B: Python Source Execution 1. Copy `Linux_Client.py`, `requirements.txt`, and `client_config.json` to `/opt/logar/`. 2. Install client dependencies: ```bash python3 -m pip install -r requirements.txt ``` 3. (Optional) Run `out/linux_client/build_bin.sh` to compile a standalone ELF binary locally if desired. 4. Run via cron or systemd timer: ```bash 0 */3 * * * python3 /opt/logar/Linux_Client.py --hours 24 ``` --- ## Running Tests ### 1. Component-Specific Unit Tests The test suite is located in `tests/` and exercises all components: ```bash # Run all unit tests python -m unittest discover -s tests # Or run component tests individually: python -m unittest tests/test_server.py python -m unittest tests/test_win_client.py python -m unittest tests/test_linux_client.py ``` ### 2. End-to-End Pipeline Integration Test The pipeline test exercises invalid token rejection, encrypted socket streaming, database persistence, status promotion upon the 4th run, and the Hermes API report output. 1. **Start the server** in Shell 1 (creates `server_config.json` on first run): ```bash python Server.py ``` 2. **Export client configuration** in Shell 2 (required for testing): ```bash python Server.py --create-client-config --server-host 127.0.0.1 --server-port 9443 --client-out client_config.json ``` 3. **Execute the integration test** in Shell 2: ```bash python test_pipeline.py ``` ## Continuous Integration via Gitea Actions Continuous integration is automated via [`.gitea/workflows/ci.yml`](.gitea/workflows/ci.yml) and triggers automatically on **every push** and pull request: 1. **Syntax Compilation**: Validates all Python scripts (`Server.py`, `Win_Client.py`, `Linux_Client.py`, `package_dist.py`, `upload_release.py`, `test_pipeline.py`, and test suites). 2. **Component Unit Tests**: Discovers and runs all unit tests in `tests/` (`test_server.py`, `test_win_client.py`, `test_linux_client.py`). 3. **End-to-End Pipeline Verification**: Automatically spins up the LOGAR server hub, generates test configs, runs `test_pipeline.py` (testing socket authentication, 4-run rule persistence, Hermes API report, and client integrations), and shuts down the test instance. --- ## Automated Releases via Gitea Actions Release builds are automated via [`.gitea/workflows/release.yml`](.gitea/workflows/release.yml) using your Gitea action runner: ### Publishing a Release Whenever you want to release a new version with compiled standalone binaries: ```bash git tag v1.0.1 git push origin v1.0.1 ``` ### What Gitea Actions Does Automatically: 1. Gitea runner executes the workflow on tag push. 2. Installs Python, system build tools (`binutils`, `zip`), PyInstaller, and project dependencies via `apt-get` and `pip3`. 3. Runs `package_dist.py` to compile standalone binaries: - `Linux_Client.bin` (native ELF binary compiled with PyInstaller) - `Server.bin` (native server ELF binary compiled with PyInstaller) - `Win_Client.pyz` (standalone executable zipapp) - `SHA256SUMS.txt` (SHA-256 cryptographic checksums) 4. Publishes the Gitea release directly via Python (`python3 upload_release.py --skip-build`) using the Gitea REST API to attach the compiled binary assets (avoiding runner Node runtime limitations). ### Building & Publishing Windows Executables (`.exe`) Locally Because the Linux Gitea runner compiles ELF binaries, native Windows PE executables (`Win_Client.exe`, `Server.exe`) can be built and published directly from a Windows workstation: ```powershell # Compiles Win_Client.exe, Server.exe, Linux_Client.bin, and uploads to Gitea python upload_release.py --tag v1.0.0 --token ``` *(Environment variables `GITEA_TOKEN`, `GITEA_SERVER_URL`, `GITEA_REPOSITORY`, and `GITEA_REF_NAME` are also supported automatically).*