## 2 Related Work
Furthermore, the authors also claim that the proposed model can be effective for particularly smart factories management systems. [PERSON] et al. [23] recently tried to put together an intrusion detection system for IoT devices; particularly for solving the authentication issues within IoT devices. The authors proposed the use of a supervised learning algorithm in order to detect the intrusion attacks and discard commands classified as threats at the controller level. Furthermore, ANN was used for the identification of intrusions and the UNSW-15 dataset was used for training the model. An average precision of 84% was reported while undergoing 10-fold cross validation. [PERSON] et al. [24] evaluated deep learning-based systems in adversarial environments for network intrusion detection. The authors aimed to analyze the resilience of Network Intrusion Detection (NID) systems based on deep learning in an adversarial environment by proposing four frameworks; Deep Neural Networks (DNN), Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR) using the NSL-KDD dataset. The experimental results proved that the DNN based model is more effective in detecting network intrusions. Also, an intrusion detection method based on artificial neural networks (ANN) and fuzzy clustering were presented by [PERSON] et al. [25]. The FC-ANN method consists of three major modules: fuzzy clustering, ANN, and fuzzy aggregation. The fluffy module for clustering divides a given collection of data into clusters. Every subset's pattern is learned using the ANN module. The fuzzy aggregation module is used to combine the results of various ANNs and decrease detection errors. The FC-ANN method was evaluated on the KDD CUP 1999 dataset and shown to be effective against low-frequency assaults such as R2L and U2R. A CNN based novel network intrusion detection system is presented by [PERSON] et al. [26] to address the issue of malicious network traffic identification. The authors proposed a novel CNN based system for NIDS and trained their deep learning-based CNN model using raw network traffic and extracted features as well. The CICIDS 2017 dataset was used to train the model. Furthermore, the authors claim that the model trained on raw network traffic performs better when compared with the CNN model trained on the previously extracted features. [PERSON] et al. [27] present an ensemble-based technique for the detection of network anomalies in an intrusion detection system. This technique uses an ensemble of learning and prediction mechanisms for the multi-class classification of anomalies. Initially, researchers used ANOVA F-test as the univariate feature selection technique for determining the feature performance and the relationship between class labels and data features [28, 29]. Furthermore, they used an automated machine learning model as a learning mechanism and a Kalman filter as a prediction model. They used a Bayesian optimizer as an optimization function for neural network architecture search (NAS) that selects architecture from a set of architectures list that maximizes the accuracy. This ensemble method uses a weighted voting mechanism that weights the prediction of both algorithms based on average prediction accuracy. They evaluated the performance of the proposed technique on publicly available CICIDS2017 and UNSW-NB15 datasets and achieved an accuracy of 97.02 and 98.801%, respectively. [PERSON] et al. [30] proposed a 1 Dimensional Convolution Neural Network (1D CNN) for the detection of network intrusions. In order to create an invasive Internet traffic model for the IDS, the authors serialize Transmission Control Protocol/Internet Protocol (TCP/IP) packets over a set time period. Normal and abnormal network traffic are then identified and tagged for supervised learning in the 1D-CNN. [PERSON] et al. [31] used a CNN algorithm built on deep learning for IDS. They created the CNN-RNN, CNN-LSTM, and CNN-GRU hybrid network models and tested them on the KDDCup 99 dataset.