Secure Smart Grid Industrial Control Systems

Cryptographic encryption, anomaly detection, and classification for Smart Grid ICS — code accompanying a journal paper. The notebooks cover a hybrid cryptographic scheme for securing smart-grid communications, cryptanalysis against it, and ML pipelines for anomaly classification and zero-day attack detection on industrial control traffic.

Notebooks

Notebook	Contents
ENCRYPTION AND DECRYPTION.ipynb	Hybrid encryption scheme built on ECC + HKDF (elliptic-curve key agreement → HKDF-derived keys → symmetric cipher) for smart-grid payloads
HKPE COMPARISION.ipynb	Benchmarks of the proposed hybrid scheme against baseline ciphers
CRYPTANALYSIS.ipynb	Security analysis / attack evaluation of the proposed scheme
ANOMALY DETECTION.ipynb	Unsupervised/ML anomaly detection on smart-grid ICS traffic
CLASSIFICATION.ipynb	Supervised attack classification (Random Forest pipeline with ROC/AUC evaluation)
DNP3 CLASSIFICATION.ipynb	Classification of DNP3 protocol traffic
STATISTICAL TESTS.ipynb	Statistical validation of results
ZERODAY.ipynb	Zero-day / unseen-attack scenario evaluation

The notebooks ship inside JOURNAL CODES.zip — extract it to run them:

unzip "JOURNAL CODES.zip"

Requirements

• Python 3.8+
• cryptography (ECC, HKDF, symmetric ciphers)
• pandas, numpy, scikit-learn, matplotlib, seaborn, tqdm

pip install cryptography pandas numpy scikit-learn matplotlib seaborn tqdm

Usage

Each notebook is self-contained: extract the zip, launch Jupyter, and run the notebooks in the order listed above. Classification/anomaly notebooks expect the paper's smart-grid ICS dataset.

Citation

This code accompanies a journal paper — if you use it, please cite the associated publication.