## 4 Security of Computer Network Users
### Discussion and Findings
** \\ \hline
[137] & DNN & & * & complies with security, privacy, centralization, trust, and integrity requirements in connected device environments & IoT & 98.97 & 97.71 \\
[139] & SVM, NB & * & * & users the idea of feature embedding to classify the features of normal and abnormal data effective monitoring of network traffic and alerting about possible attacks in real-time uses KPCA to reduce dimensionality without losing data effectively detects threats related to remote network connections the used dense clusters can communicate and perform interactions similar to the interactions between brain cells tested using two specially prepared datasets a feature classifier may use a subtractive angular margin loss in a cosine space selects the best features, thus minimizing the number of dimensions, increasing the true positive rate lowering the false positive rate ensures safe travel with intelligent vehicles and roads protects the network and the devices against attacks, thus protecting human life detects attacks in vehicle networks reduces training and response times and improves the accuracy of attack detection learning is asynchronous each network node independently executes the training algorithm with its copy of the dataset each node shares the weights of the local learned model with the aggregate instance it effectively distinguishes attack traffic from ordinary information flowing in a network of vehicles enables detection of intrusions in real-time high detection rate of less-popular cyber attacks [158] FL & * &  
example as belonging to the positive class. We included the average values of the accuracy and precision mentioned by the authors in Tables 3 and 4. The authors used publicly available datasets to prove their methods. Only [PERSON] et al. in [146] prepared their datasets. The analyzed research showed a strong need to provide intelligent intrusion-and attack-detection methods. This need is also related to the ever-growing demand for communication via electronic links between users and devices. Users communicate with each other, communicate with devices, as well as their devices communicate with other devices. Users, therefore, require that information transmitted during such communication remains safe and intact by rogue computer network users. Determining which attack is the most-dangerous for users and devices operating on computer networks is impossible. All kinds of inappropriate user activity can devastate users and their data. Hence, trust in AI algorithms is needed to assess network traffic in terms of possible intrusions. IDSs should meet data privacy and security requirements by detecting common and less-popular attacks for a specific solution. Users should feel safe knowing that a trustworthy system will respond quickly and block a bad connection. At this same time, TAI methods in IDSs successfully deal with technical problems related to data quality and efficiency and reduce learning and response times. The prediction of an attack or inappropriate user behavior should be prepared in real-time so that the system and its methods become more trustworthy. Our insights and findings from the described studies are as follows. Firstly, almost all mentioned methods used publicly available datasets to train and verify the proposed systems. These datasets may not imitate real network conditions. The network conditions are constantly changing, and devices connect to and disconnect from the network, while these devices have different software and hardware configurations. Also, we can indicate many types of malware software that are still evolving. Thus, methods for Intrusion-Detection Systems should also be trained and verified using real network environments and devices. Thanks to this, the analysis and evaluations will show the proposed methods' effectiveness. The issue of preparing realistic datasets should be approached in several steps.