Design of an Improved Model Integrating Blockchain, Machine Learning, and Differential Privacy for Cybersecurity in Smart Farming

There is a critical need for improved cybersecurity and privacy in smart farming; the adoption of advance technologies opens vulnerabilities to cyber threats and data breaches.Most of the solutions developed so far fail to provide the required scalability, robustness, and privacy measures toward completely securing smart farming environments.This paper contributes to solving these limitations by integrating state-of-the-art cybersecurity and privacy using a multifaceted approach.In this paper, we present a holistic framework that uses blockchain, machine learning, deep learning, and data science techniques to protect smart farming systems.First, we use Hyperledger Fabric with smart contracts to assure integrity and privacy in data samples.Being a permissioned blockchain, Hyperledger Fabric offers high scalability and supports smart contracts that help in rigorous access control and immutable transaction records.Preliminary results indicate 100% integrity and 98% privacy compliance of the data samples.We also implement isolation forest, an unsupervised machine learning algorithm, for threat detection by identifying anomalies in network traffic and sensor data samples.This approach presents an efficient way of treating high-dimensional data and provides a 95% detection rate with a 2% false positive rate.Further, we use Long Short-Term Memory (LSTM) networks in order to detect advanced and time-evolving threats hidden in sequential data samples.Since LSTMs are good at recognizing temporal patterns, threat detection accuracy is reportedly as high as 97% with a precision of 96% and a recall of 95%.Differential privacy is further combined with federated learning to ensure better privacy.Federated learning enables model training on decentralized devices and maintains data locality, protecting individual data contributions through differential privacy via the addition of noise.This approach provides high privacy, =1.0, while maintaining model accuracy at 92%, thus avoiding loss in privacy.The model presented herein alleviates the inadequacy of the different existing solutions by providing a scalable, robust, and privacy-preserving framework for smart farming.In this paper, we present a holistic inclination toward enhancing the security and privacy of smart farming systems against cyber threats and protecting sensitive data samples.Our work is well plated on the integration of new technologies in threat detection, integrity of data, and setting a new standard of privacypreserving best practice in the agricultural domain.