README.md

---
library_name: peft
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- base_model:adapter:distilbert-base-uncased
- lora
- transformers
model-index:
- name: malicious-url-detector
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# malicious-url-detector

malicious-url-detector is a fine-tuned version of distilbert-base-uncased designed to classify URLs as malicious or benign using natural language and pattern-based representations.
It leverages LoRA (Low-Rank Adaptation) via the PEFT library for lightweight, efficient fine-tuning.

## Model description
This model learns to identify potentially harmful URLs based on patterns commonly found in phishing, malware delivery, and command-and-control links.
It was fine-tuned on a curated dataset of labeled URLs containing both malicious and safe samples.

## Intended uses & limitations
Intended uses:
- Integrate into threat detection systems or browser security tools
- Use for phishing URL classification or malware link filtering
- Educational and research purposes in cybersecurity automation

Limitations:
- Should not be solely relied upon for production-grade URL blocking
- May misclassify newly obfuscated or encrypted URLs
- Requires regular retraining with updated datasets to maintain accuracy

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Framework versions

- PEFT 0.17.1
- Transformers 4.57.1
- Pytorch 2.8.0
- Datasets 4.2.0
- Tokenizers 0.22.1