## 4 Security of Computer Network Users
### TAI-Based Securing Against Intrusion Attacks  
In this subsection, we present an overview of research connected with Trustworthy Artificial Intelligence methods for the security of computer network users. We considered AI-based Intrusion-Detection Systems and AI-based methods for specific attack detection. Also, we discuss these methods and conclude our insights about the mentioned research. The security of network users is connected with the safety dimension of TAI. In [137], [PERSON] et al. proposed a deep blockchain-based trustworthy privacy-preserving secured framework for a cyber-physical system network called DBTP2 SF. The proposed system was designed to meet the requirements of security, privacy, centralization, trust, and integrity in interconnected device environments. DBTP2 SF has a modular structure. The first module is responsible for credibility and trust management, where observational sensor data are assessed to generate a reputation score of a given network node. The privacy-protection module maintains data integrity and transforms and generates data models. In turn, the anomaly-detection module uses Deep Neural Networks (DNNs) consisting of a inputlayer,  
Figure 2: Typical architecture of a CPS with an AI-based Intrusion-Detection System. output layer, and hidden layer. The implemented network processes the data collected by the sensors and analyzed by the first two modules. The DNN network then evaluates the behavior of the data and divides them into two categories, regular activities and attacks. When an attack is detected, data are sent to the system administrator for him/her to take appropriate action. The authors used publicly available datasets to validate the proposed system. The data contained events related to various attacks that may occur in WSNs (for example, MITM and guessing attacks). The authors measured the performance of the implemented DNN and anomaly-detection system using the following metrics: accuracy, precision, detection rate (recall), and F1-score [134; 138]. While analyzing the efficiency of the intrusion-detection process, the authors obtained an accuracy of 98.97% and a detection rate of 93.87%. [PERSON] et al. concluded that intrusion detection using the proposed DNN ensures the security of network users because it protects them against intrusion into the network. [PERSON] et al. in [139] noticed that data quality also influences the effectiveness of IDS intrusion-detection methods. High-quality training data will increase detection performance and reduce training complexity and response time. They proposed classifying intrusions and regular instances in their solution, which uses the SVM and Naive Bayes (NB) feature embedding. The authors used the NB algorithm to transform the original features into new data to obtain high-quality data. Then, the obtained data trained the SVM classification model. The authors used publicly available datasets for intrusion detection to research and evaluate the proposed model. The authors achieved an accuracy that oscillated between 93.75% and 99.35% for different datasets. [PERSON] et al. summarized that such features characterize their proposed method as having high precision, a high detection rate, a low false alarm rate, and a rapid training speed. [PERSON] et al. in [140] proposed a comprehensive model of network attack detection and classification of network attacks. The authors developed a system containing several modules based on DL. To extract the features of a Recursive Deep Learning model, the authors used Recurrent Neural Networks, Long Short-Term Memory (LSTM) networks, and the Gated Recurrent Unit (GRU) to extract the optimal functions necessary to detect and classify attacks. The authors used Kernel Principal Component Analysis (KPCA) to reduce dimensionality without data loss. The authors then passed the combined features to the collective meta-classifier, which used the Random Forest (RF) and the SVM model for prediction.