Malware Classifier LIME Model Card 🤖🔒📝
Model Details 🛠🔍
Model name: malware_classifier_lime.h5
Model architecture: Convolutional Neural Network (CNN)
Training dataset: Spectrum-Dataset
Code repository: nileshkhetrapal/spectrum
Input: 200x200 images 🖼️
Output: Malware classification among 119 classes 🦠
Model Architecture
- 3 Convolutional Layers (Conv2D) with ReLU activation 🌐
- MaxPooling2D layers after each Conv2D layer ⛲
- Flatten layer to connect with Dense layers 🥞
- 2 Dense layers with Dropout and ReLU activation 🔗
- Output layer with Softmax activation 🎯
Intended Use 💻🔧
This model is intended to be used for classifying malware based on input images. It is designed to help with the detection and prevention of malware in order to improve computer and network security. 🛡️💻🌐
Model Performance 📊📈
The model achieved the following results during training:
Loss: 0.2642
Accuracy: 0.9627
💡 Please note that these results may not reflect the model's performance in real-world scenarios. It is always recommended to test the model on a specific dataset or use case to ensure its effectiveness.
Usage Instructions 📚🖥️
🎯 Training Instructions 🎯
1️⃣ Download dataset: https://huggingface.co./datasets/nilekhet/Spectrum-Dataset 🌐
2️⃣ Clone rust code: https://github.com/nileshkhetrapal/spectrum 🦀
3️⃣ Use the provided Python code to train the model 🐍
4️⃣ Set parameters (batch_size, epochs, image_size) 🔧
5️⃣ Train model using ImageDataGenerator, train_generator, and validation_generator 🚀
6️⃣ Save the trained model as malware_classifier_lime.h5 💾
🔮 Making Predictions 🔮
1️⃣ Load the malware_classifier_lime.h5 model 📦
2️⃣ Use LIME to explain instances 🍋
3️⃣ Display the original image and LIME explanation 🖼️
4️⃣ Make a prediction using the model 🧠
5️⃣ Output the predicted class and class name 📝
Limitations ⚠️🚧
- The model is trained on a specific dataset and might not generalize well to all types of malware or new malware families. Regularly updating the training data is necessary to maintain its effectiveness.
- The model may produce false positives or false negatives, leading to potential misclassification of benign software as malware or vice versa.
- The model's performance is dependent on the quality and diversity of the training dataset. Low-quality or biased data may lead to suboptimal performance.
Responsible AI Considerations 🌐💡🧠
While this model is designed to improve computer and network security, it is important to consider the potential ethical implications and unintended consequences of its use:
- Privacy: Ensure that the data used for training and making predictions does not contain sensitive or personally identifiable information (PII). Follow data protection regulations and best practices for handling data.
- Transparency: Be transparent about the model's performance, limitations, and potential biases. This will help users make informed decisions about whether the model is suitable for their specific use case.
- Accountability: Establish clear lines of responsibility for the use and potential misuse of the model. Make sure users understand the risks associated with using the model and have the necessary resources to address potential issues.
- Bias: Be aware of potential biases in the training data, as they may affect the model's performance and fairness. Monitor and address any biases that may arise during the model's deployment.
Remember to always use AI responsibly and ethically! 🌍💚🤝