## 4 Security of Computer Network Users
### TAI-Based Securing Against Intrusion Attacks
The first is the security-risk filter based on rules, which is responsible for determining whether the observed event of the node deviates from the normal profile. The authors used Density-based Spatial Clustering of Applications with Noise in this stage. In the second step, an adder used the rules of Dempster-Shafer theory [149]. The adder computes an overall belief value by fusing all sources of evidence for each activity. Each activity is recorded in the node's event history database, which considers both the node's good and destructive activities. Saving to the database is the fourth stage of the proposed system. The final step is related to the use of Bayesian Learning. The authors used Bayesian Learning (BL) to update the probability of a belief with new activities. [PERSON] et al. determined the suspicion level of all incoming data based on the degree to which they deviated from the data reported from trustworthy nodes. The authors simulated the system using the Markov Modulated Poisson Process and two Gaussian distribution functions. The results showed that the proposed solution was characterized by high precision and accuracy in detecting intrusion into the VANET. Thus, implementing such a system will protect the network and the devices that compose it against attacks, thus protecting human life. The problem of detecting attacks in vehicle networks was also dealt with by [PERSON] et al. [150,151]. The authors observed that any attack on intelligent vehicle networks could seriously disrupt vehicle communication and become dangerous to human life. [PERSON] et al. proposed a hybrid model of an intruder-detection system based on DL to improve the security of such systems. This model combines LSTM and the GRU. The LSTM processes the input data. Between the LSTM and GRU, a DENSE layer ensures quick system responses. The GRU, on the other hand, generates an exit probability. The results of the tests of the proposed model showed its better performance, including a shorter response time than other models. The authors confirmed that the model could detect different types of cyber attacks. Also, [PERSON] et al. focused on attacks occurring in vehicle networks. In [152], a Federated Learning-based (FL) technique was proposed to detect cyber attacks in the mentioned networks. The model uses the GRU to observe network traffic, considering the temporal relationship between traffic samples, regulating the learning process, and detecting unknown attack patterns. Learning happens asynchronously. Each network node independently performs the training algorithm with its copy of the dataset and shares the weights of the locally learned model with the aggregating instance. The authors tested the proposed system with publicly available test data and obtained promising results. [PERSON] et al. in [153] considered the security and intrusion-detection problems in autonomous vehicle communication using a vehicle-mounted network. The authors proposed a new CNN model based on two-dimensional Mosaic pattern coding to detect intrusion in these networks. The method proposed by [PERSON] et al. consists of two processes. The first is coding based on 2D Mosaic patterns. During this process, one-dimensional data are converted to two-dimensional form and then packed to be joined into a Mosaic pattern. The second process is related to the operation of the CNN, which consists of the input, convolutional, pooling, fully connected, and output layers. The lower layers process the network traffic. However, the output layer contains only two neurons connected to the neurons of the previous layer. This layer classifies incoming network data into normal and attacking network traffic. The authors simulated their method and showed that the proposed method effectively distinguishes attacking traffic from ordinary information flowing in the network of vehicles. Additionally, the technique enables real-time intrusion detection and improves the reliability of system differentiation. [PERSON] et al.