Abstract:
An efficient and productive IoT application may ease some real-time tasks however,
it is at risk of cyber-attack. Intrusion Detection Systems (IDS) are of significant
importance for the security measures of IoT applications. Anomaly-based intrusion detection systems perform more efficaciously than other methods. IoT/IIoT
devices that deal with large data volumes are at risk of malicious attacks and
as a result, anomaly-based IDS are developed. But, the question that arises is
whether the performance of models meets the required standards and accuracy.
For research the Telemetry data of IoT/IIoT services from the ToN_IoT dataset
collected at UNSW Canberra Cyber IoT lab, SEIT (Australia), is used. It includes data about seven IoT/IIoT sensors. Federated Learning based on Deep
auto-encoder is adopted to efficiently identifying attacks while solvingthe issue of
data leakage and the privacy of users . Federated models handle the non-IID data
efficiently. Hybrid models use Machines and Deep Learning algorithms for efficient
model design with increased detection rates. The algorithms used for the Hybrid
model are Random Forest, Decision Tree and XGBoost. The XGBoost algorithm
improves the accuracy of the Hybrid model with better predictions. Both Federated and Hybrid model ensures efficient pre-processing and feature selection. The
results of the Federated model are dependent on device datasets while the Hybrid
model outperforms on the same data.
