IoT Vulnerability Prediction & Analysis Framework (Python | Jupyter Notebook)
Budget: $30 – $250 USD
IoT Vulnerability Prediction & Analysis Framework (Python | Jupyter Notebook)
I am developing a graduation project focused on building an Intelligent IoT Vulnerability Prediction and Analysis Framework. The objective is to identify and predict security vulnerabilities in IoT networks using machine learning and data mining techniques.
I’m seeking a Python developer with experience in Jupyter Notebook to implement the technical components of this project, working with the IoT-23 dataset and other related data sources such as Shodan and CVE.
Project Overview:
Analyze IoT network traffic
Detect and predict malicious behavior
Apply Association Rule Mining (Apriori, FP-Growth, ECLAT)
Implement machine learning models (Decision Tree, Random Forest, KNN)
Key Tasks:
Data Preprocessing
Clean and normalize the IoT-23 dataset
Perform feature selection and transformation
Association Rule Mining
Implement Apriori, FP-Growth, and ECLAT algorithms
Extract and analyze patterns in the traffic data
Machine Learning Models
Train/test classification models: Decision Tree, Random Forest, and KNN
Evaluate models using accuracy, precision, recall, and F1-score
Visualization and Reporting
Generate visual outputs (confusion matrix, ROC curves, etc.)
Include brief explanations and structured documentation within the notebook
Notebook Organization
Organize the work in a clean and well-documented Jupyter Notebook
Suggested sections: Introduction, Preprocessing, ARM, ML Models, Evaluation, Conclusion
Required Skills:
Python programming with libraries such as Pandas, Scikit-learn, Mlxtend, and Matplotlib
Experience working with cybersecurity or IoT-related data
Knowledge of data mining and ML classification techniques
Ability to structure and document academic projects using Jupyter Notebook
Deliverables:
A complete, organized Jupyter Notebook (.ipynb) with working code and explanations
Performance visualizations and comparison of different models
Clear analysis and summary of results
Please include any relevant project examples, your estimated timeline, and availability to get started.
I am developing a graduation project focused on building an Intelligent IoT Vulnerability Prediction and Analysis Framework. The objective is to identify and predict security vulnerabilities in IoT networks using machine learning and data mining techniques.
I’m seeking a Python developer with experience in Jupyter Notebook to implement the technical components of this project, working with the IoT-23 dataset and other related data sources such as Shodan and CVE.
Project Overview:
Analyze IoT network traffic
Detect and predict malicious behavior
Apply Association Rule Mining (Apriori, FP-Growth, ECLAT)
Implement machine learning models (Decision Tree, Random Forest, KNN)
Key Tasks:
Data Preprocessing
Clean and normalize the IoT-23 dataset
Perform feature selection and transformation
Association Rule Mining
Implement Apriori, FP-Growth, and ECLAT algorithms
Extract and analyze patterns in the traffic data
Machine Learning Models
Train/test classification models: Decision Tree, Random Forest, and KNN
Evaluate models using accuracy, precision, recall, and F1-score
Visualization and Reporting
Generate visual outputs (confusion matrix, ROC curves, etc.)
Include brief explanations and structured documentation within the notebook
Notebook Organization
Organize the work in a clean and well-documented Jupyter Notebook
Suggested sections: Introduction, Preprocessing, ARM, ML Models, Evaluation, Conclusion
Required Skills:
Python programming with libraries such as Pandas, Scikit-learn, Mlxtend, and Matplotlib
Experience working with cybersecurity or IoT-related data
Knowledge of data mining and ML classification techniques
Ability to structure and document academic projects using Jupyter Notebook
Deliverables:
A complete, organized Jupyter Notebook (.ipynb) with working code and explanations
Performance visualizations and comparison of different models
Clear analysis and summary of results
Please include any relevant project examples, your estimated timeline, and availability to get started.