Metadata-Version: 2.4
Name: logsignal
Version: 0.2.0
Summary: Lightweight rule-based and statistical log anomaly detector for Python
Project-URL: Homepage, https://github.com/cpprhtn/logsignal
Project-URL: Repository, https://github.com/cpprhtn/logsignal
Project-URL: Issues, https://github.com/cpprhtn/logsignal/issues
Author: Junwon Lee
License: MIT License
        
        Copyright (c) 2025 Junwon Lee
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        SOFTWARE.
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Security
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: System :: Logging
Requires-Python: >=3.12
Description-Content-Type: text/markdown

# LogSignal

> Lightweight rule-based and statistical log anomaly detection for Python backends

**LogSignal** is a lightweight library for detecting security signals and anomalies
in **any Python backend that produces logs** — including FastAPI, Flask, Django,
batch jobs, CLI tools, and long-running daemons.

It is designed to be **simple, fast, and dependency-free**, making it suitable
for both personal projects and production systems.

---

## Why LogSignal?

Most existing logging or security solutions fall into one of these categories:

* Too heavy (SIEMs, Elastic stacks, LLM-based systems)
* Too complex (regex-heavy configuration, steep learning curves)
* Overkill for small teams or personal projects

**LogSignal has a clear goal:**

> *“If you have logs, you should be able to detect anomalies in under 5 minutes.”*

---

## Key Characteristics

* ❌ No SIEM required
* ❌ No complex configuration
* ❌ No LLMs or external APIs
* ✅ Rule-based detection
* ✅ Statistical detection
* ✅ Native Python `logging` integration

---

## Key Features

### 1️⃣ Rule-Based Detection (Default)

Deterministic and immediate detection based on explicit rules.

* Keyword matching
* Log level matching
* Frequency / spike detection within a time window

```python
from logsignal import Rule

Rule.contains("ERROR")
Rule.level("CRITICAL")
Rule.spike(threshold=5, window=10)
```

Rule-based detectors work **immediately** and are ideal for hard signals
such as errors, crashes, or known failure patterns.

---

### 2️⃣ Statistical Detection (Optional)

Adaptive detectors based on lightweight statistical methods.

* Moving averages
* Z-score based spike detection
* Automatic baseline learning

```python
from logsignal import StatRule

StatRule.error_rate_zscore(z=3.0)
```

> ❗ This is **math/statistics-based**, not ML
> → fast, explainable, and operationally safe

Statistical detectors are designed to **complement**, not replace, rule-based detection.

---

### 3️⃣ Zero Vendor Lock-in

* Built on Python’s standard `logging` module
* No external services, APIs, or agents
* Works anywhere Python runs

---

## Installation

Using `pip`:

```bash
pip install logsignal
```

Using `uv`:

```bash
uv add logsignal
```

---

## Quick Start

### 1️⃣ Create a LogWatcher

```python
from logsignal import LogWatcher

watcher = LogWatcher(
    rules=[
        "ERROR",
        "CRITICAL",
    ]
)
```

---

### 2️⃣ Process Logs

```python
watcher.process("ERROR database connection failed")
```

Example output:

```text
[LogSignal] RuleTriggered: keyword='ERROR'
```

---

### 3️⃣ Spike Detection Example

```python
from logsignal import LogWatcher, Rule
import time

watcher = LogWatcher(
    rules=[
        Rule.spike(threshold=3, window=5)
    ]
)

for _ in range(5):
    watcher.process("ERROR something bad happened")
    time.sleep(0.5)
```

---

## Integration with `logging`

LogSignal integrates directly with Python’s standard logging system.

```python
import logging
from logsignal import LogSignalHandler

logger = logging.getLogger("app")
logger.setLevel(logging.INFO)

handler = LogSignalHandler()
logger.addHandler(handler)

logger.error("DB connection timeout")
```

---

## Entropy-Based Detection (Experimental)

LogSignal provides an **experimental entropy-based detector** that identifies
**sudden increases in log randomness**, which may indicate suspicious or automated activity.

Typical signals include:

* Injection-like payloads
* Obfuscated requests
* Unexpected input patterns

Entropy-based detection does **not rely on signatures or predefined rules**.

---

### Example

```python
import random
from logsignal import LogWatcher
from logsignal.stats.entropy import EntropySpike

watcher = LogWatcher()
watcher.add_stat(EntropySpike(window=10, threshold=2.5))

normal_logs = [
    "INFO ok",
    "INFO request processed",
    "INFO heartbeat",
    "INFO user active",
]

for _ in range(30):
    watcher.process({"message": random.choice(normal_logs)})

attack = "GET /login.php?id=1' OR '1'='1' UNION SELECT password FROM users"

for _ in range(5):
    watcher.process({"message": attack})
```

Example output:

```text
[LogSignal] entropy_spike detected (z=2.93)
```

---

### Notes

* Entropy detectors require a **warm-up period** to learn a baseline
* Detection is **relative to historical behavior**, not absolute thresholds
* Best used alongside rule-based detectors

---

### Status

* Experimental API
* Enabled via `add_stat()`
* No external dependencies

---

### Design Philosophy

* No regex
* No signatures
* No ML models
* Fully explainable
* Lightweight enough for personal projects

> Entropy detection in LogSignal is intentionally simple, transparent,
> and designed to complement rule-based alerts—not replace them.

---

## Status

* Experimental (API may change)
* Enabled via `add_stat()`
* No external dependencies

---

## Design Philosophy

* **Rule-first**
* **Statistics second**
* **ML optional**
* **Explainable over fancy**
* **Personal-project friendly**

LogSignal favors clarity, predictability, and operational safety over black-box intelligence.

---

## Statistical Detectors Are Relative

Statistical detectors operate on **deviation from a learned baseline**.

They assume:

* Historical data represents normal behavior
* Anomalies are statistically significant deviations from that behavior

They are **not** intended for absolute thresholding.

For immediate and deterministic alerts, use **rule-based detectors** alongside
statistical ones.

---

## Roadmap

### v0.1.x

* [x] Keyword rules
* [x] Spike rules
* [x] Standard logging handler

### v0.2.x

* [ ] Statistical baseline learning improvements
* [ ] Z-score / EWMA detectors
* [ ] JSON / structured log support

### v0.3.x

* [ ] ML-based anomaly plugins
* [ ] FastAPI middleware
* [ ] Prometheus exporter

---

## Use Cases

* Security monitoring for personal projects
* FastAPI / Flask backend monitoring
* Batch job failure detection
* CI log anomaly detection
* Lightweight security signal triggering

---

## License

MIT License

---

## Contributing & Support

* GitHub Issues
* Pull requests are welcome

---

