## 4 Security of Computer Network Users
### TAI-Based Securing Against Intrusion Attacks
in [154, 155] proposed a B-Stacking Intrusion-Detection System for IoT networks. Their method used the K-Nearest Neighbors (KNN), RF, and XGBoost algorithms to detect intrusions and anomalies in the network. The authors tested their system using publicly available datasets. Thanks to the obtained results, the authors confirmed a high detection rate for less popular cyber attacks with infrequent observations, for example the user to root attack, during which attackers gain access to the root by exploiting the system's vulnerabilities by logging as a regular user. The authors obtained lower false positive and false negative rates than other intrusion-detection methods. [PERSON] et al. in [156] proposed an automatic IDS using a DL approach. They considered a new unsupervised technique connected with SVM classification: Stacked Non-symmetric Deep Auto-Encoder (SNDAE). This connection increased the detection reliability and decreased the computational requirements and the training and computing costs. The authors performed a complex analysis of their approach using publicly available datasets. The authors obtained an overall accuracy of 99.65% and a precision of 99.99%. Both results were better during the comparison with similar techniques. The authors concluded that the following research would consider a system's ability to manage zero-day attacks. In [157], [PERSON] et al. proposed Realguard, an Intrusion-Detection System based on a Deep Neural Network (DNN). The proposed approach can operate in the edge gateway of a network of connected devices to distinguish between normal and attacking network traffic and attacks. The proposed DNN model includes five hidden layers, where each neuron from one layer connects with neurons from the next layer. In addition, the network considers over 34,000 parameters, which translates into a high efficiency of intrusion detection. The network extracts the characteristics of the network traffic. Thus, it detects ten types of attacks, such as MITM or denial of service attacks. The authors tested the operation of the proposed system on a publicly available dataset. The obtained results showed the high precision and accuracy of the system with a simultaneous low rate of false alarms. [PERSON] et al. in [158] proposed a lightweight and effective intrusion-detection method based on cloud-edge collaboration. The authors used an auto-encoder, a temporal convolutional network, and a federated learning framework in this method. It enabled the dimensionality reduction of raw network traffic data's high-dimensional features. In comparing the proposed model with Long Short-Term Memory and the Gated Recurrent Unit, the authors achieved better results (accuracy of 98.62%). The proposed method successfully detected botnets in the network, brute force attacks, or DoS attacks. They concluded that their method could reduce the computation and storage requirements. Also, it deals with the issue of edge device resource limitations. In [159], [PERSON] et al. used a focal loss function to train DL-based models to overcome the data imbalance issue for intrusion detection in the IoT. The authors optimized the model by enabling dynamically scaled gradient updatesand down-weighing easy instances. Also, the focal loss function caused the model to focus on the hard misclassified examples. The authors implemented this function in Feed-Forward Neural Networks and Convolutional Neural Networks. They used well-known datasets to analyze the correctness of the proposed intrusion-detection model. The authors received better results for the model based on Feed-Forward Neural Networks. Thus, in [160], [PERSON] et al. proposed a Graph Neural Network-based (GNN) Intrusion-Detection System. In their method, the network graph processed multiple edges with multi-dimensional edge features. Thanks to this, the network graph can capture the complete exchange of information between any pair of nodes. In this structure, nodes mean the source and destination IP addresses.