  Generally, the risks associated with malicious threats are increasing for the
IIoT and its related applications due to dependency on the Internet and the
minimal resource availability of IoT devices. Thus, anomaly-based intrusion
detection models for IoT networks are vital. Distinct detection methodologies
need to be developed for the IIoT network as threat detection is a significant
expectation of stakeholders. Machine learning approaches are considered to be
evolving techniques that learn with experience, and such approaches have
resulted in superior performance in various applications, such as pattern
recognition, outlier analysis, and speech recognition. Traditional techniques
and tools are not adequate to secure IIoT networks due to the use of various
protocols in industrial systems and restricted possibilities of upgradation. In
this paper, the objective is to develop a two-phase anomaly detection model to
enhance the reliability of an IIoT network. In the first phase, SVM and Naive
Bayes are integrated using an ensemble blending technique. K-fold
cross-validation is performed while training the data with different training
and testing ratios to obtain optimized training and test sets. Ensemble
blending uses a random forest technique to predict class labels. An Artificial
Neural Network (ANN) classifier that uses the Adam optimizer to achieve better
accuracy is also used for prediction. In the second phase, both the ANN and
random forest results are fed to the model's classification unit, and the
highest accuracy value is considered the final result. The proposed model is
tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and
Bot_IoT. The highest accuracy obtained is 99%. The results also demonstrate
that the proposed model outperforms traditional techniques and thus improves
the reliability of an IIoT network.
