## 4 Security of Computer Network Users
### TAI-Based Securing Against Intrusion Attacks
The last stage of the system's operation was detecting and classifying network attacks, which were performed using logistic regression. The authors tested the proposed method using publicly available test datasets, and better results were obtained during the tests than existing solutions for the same test data. The authors concluded that the proposed solution could be used to monitor network traffic effectively and warn about possible attacks in real-time. [PERSON] et al. in [141] adapted the LightGBM [142] algorithm to an intruder detection system called AI@NTDS. The LightGBM uses the following characteristics: a histogram algorithm, Gradient-based One-Side Sampling, Exclusive Feature Bundling, and Leafwise Tree Growth. Gradient-based One-Side Sampling uses low-gradient random sampling and introduces a constant low-gradient weight. Such an assumption allows for an increase in the efficiency and scalability of the operation of the entire algorithm. The authors compared the proposed method with similar solutions, obtaining better results and demonstrating the effectiveness of the LightGBM algorithm in detecting and classifying attacks by intruders. They concluded that the proposed system effectively detects threats related to remote network connections. Also, [PERSON] et al. in [143; 144] considered the LightGBM algorithm in their intruder detection system. [PERSON] et al. in [145] proposed a lightweight, Dense Random Neural Network (DRNN) for intrusion detection dedicated to IoT solutions. The network model consists of dense clusters that can communicate and perform interactions similar to the interactions between brain cells. The model assumes one input layer, four hidden cluster layers, and one output layer. The communication between hidden layers is based on a Multi-Layer Feed-Forward architecture. The proposed network was tested for effectiveness and efficiency. The resultsshowed that the proposed model correctly classified nine typical IoT attacks with high detection accuracy. [PERSON] et al. in [146] proposed a Hybrid Parallel Deep Learning model for intrusion detection based on Metric Learning. The proposed system consists of several elements. The first is the feature-extraction module, which extracts network traffic features and reduces redundant features. The system then creates two parallel CNN architectures to combine their spatial features further. In the next step, two parallel LSTM architectures analyze the temporal information of the combined features. In the last stage of the system operation, the CosMargin classifier classifies the distinguished space-time features. The authors compared their model with similar solutions and achieved high classification accuracy. In [147], [PERSON] et al. proposed a hybrid-network-based IDS model that uses an enhanced Genetic Algorithm (GA), Particle Swarm Optimization, and Improved Random Forest methods (IRF). In the proposed model, the Particle Swarm Optimization method improves the decision vectors, while the GA is used to reconfigure them using evolutionary operators. In addition, the algorithm selects the best features, thereby minimizing the number of dimensions, increasing the true positive rate and lowering the false positive rate. In turn, the IRF eliminates fewer essential attributes, activates the list of decision trees in each iteration process, and oversees the operation of the RF classifier. The authors achieved high performance of the proposed system by testing its operation using a well-known dataset for IDSs. [PERSON] et al. in [148] considered ensuring safe travel with intelligent vehicles and roads that create the so-called Vehicular Ad Hoc Networks (VANETs). These networks are exposed to attacks in which intercepted nodes modify or reject security messages. The detection of this type of intrusion is important in VANETs because the lack of such protection may affect people's health and lives. The authors divided the intrusion-detection scheme into four stages.