Yeico
/

RansenEO

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 ---
 base_model: distilbert-base-uncased
 library_name: peft
 ---
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-- PEFT 0.14.0

 ---
 base_model: distilbert-base-uncased
 library_name: peft
+tags:
+  - ransomware
+  - IoT
+  - cybersecurity
+  - LoRA
+  - peft
+  - transformers
+  - distilbert
 ---
+# RansenEO: An Encoder-Only SLM with Fine-Tuning for ransomware detection in IoT
+This model is a lightweight LoRA adapter trained on network traffic data from IoT environments to detect the presence of ransomware. It performs binary classification—distinguishing between normal and ransomware-related network flows—using a DistilBERT-based architecture and is optimized for deployment in resource-constrained IoT devices.
+---
+## 🧠 Model Details
+- **Base Model:** [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased)
+- **Adapter Type:** LoRA (via [PEFT](https://github.com/huggingface/peft))
+- **Target Modules:** `q_lin`, `v_lin`
+- **LoRA Rank (r):** 8
+- **Epochs Trained:** 5
+- **Model Type:** Binary  Classifier (Ransomware Detection)
+- **Intended Use:** Detection of ransomware in IoT environment
+---
+## 📚 Training Data
+- **Dataset:** Forge-IIoT (IoTForge Pro)
+- **Reference:**
+  _Umar P, Mullick S, Das R, Nandi A, Banerjee I. IoTForge Pro. IEEE Internet of Things Journal 2024. doi: 10.21227/c4z1-yc52_
+- The dataset contains la comprehensive security testbed designed to generate a diverse and extensive intrusion dataset for IIoT environments. The testbed simulates various IIoT scenarios, incorporating network topologies and communication protocols to create realistic attack vectors and normal traffic patterns. The generated dataset, named ForgeIIOT, includes various attack types, such as denial-of-service, man-in-the-middle, ransomware, wildcard abuse and malware-based intrusions
+---
+## 📈 Evaluation
+The model was evaluated on a held-out test set using the following metrics:
+- **Accuracy**
+- **F1-Score**
+- **Matthews Correlation Coefficient (MCC)**
+- **Inference Time**
+- **Model Size (MB)**
+- **RAM Usage (MB)**
+These metrics ensure the model is not only accurate but also efficient for use in IoT scenarios.
+---
+## 🚀 Usage
+To use the adapter with the base model in your project:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel, PeftConfig
+peft_model_id = "yeico/RansenEO"
+# Load PEFT config
+config = PeftConfig.from_pretrained(peft_model_id)
+# Load base model
+base_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)