---
language: en
license: apache-2.0
tags:
- cybersecurity
- log-analysis
- threat-detection
- roberta
---

# cyber_threat_log_classifier

## Overview
This model is a fine-tuned RoBERTa-base classifier designed to analyze raw HTTP server logs and system audit trails for malicious patterns. It identifies common web-based attacks such as SQL Injection and Cross-Site Scripting (XSS) with high precision, enabling real-time security orchestration.

## Model Architecture
The model utilizes a Transformer-based encoder architecture (RoBERTa).



- **Encoder:** 12-layer Transformer with 768 hidden units and 12 attention heads.
- **Input:** Tokenized raw log strings (up to 512 tokens).
- **Classification Head:** Linear layer on top of the `[CLS]` (or equivalent `<s>`) token pooling to map hidden states to 5 threat categories.

## Intended Use
- **SIEM Integration:** Automated labeling of incoming logs in Security Information and Event Management systems.
- **Incident Response:** Prioritizing security alerts based on the classified threat type.
- **Log Cleaning:** Filtering out high-volume benign noise from security dashboards.

## Limitations
- **Obfuscated Payloads:** Highly encoded or polymorphic attack payloads may bypass detection if not represented in the training distribution.
- **Context Window:** Extremely long request bodies or multi-line log events exceeding 512 tokens will be truncated.
- **Adversarial Examples:** Sophisticated attackers may craft "log-injection" payloads specifically designed to mislead the classifier.