Create README.md

README.md

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+---
+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.