## 4 Security of Computer Network Users
### Discussion and Findings
First, it should be determined whether the set is to be prepared for one specific attack, for several, or for general network traffic, which will enable the detection of various irregularities in the network. Each dataset version will be equally valuable due to the possibility of testing IDSs. Then, we need to prepare a controlled test environment against which we can test attacks. Attacks should be conducted based on real scenarios, including \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Refs.** & **AI Techniques** & **Characteristics** & **Application** & **Acc.** & **Prec.** \\ \hline
[156] & DL, SNDAE, SVM & \(\bullet\) & ability to detect zero-day attacks & IoT & 99.65 & 99.99 \\
[157] & DNN & \(\bullet\) & extracts the characteristics of network traffic & IoT & 99.57 & 98.45 \\  & & \(\bullet\) & detects MITM and DoG attacks & & & \\  & & \(\bullet\) & defines network traffic based on the source IP address & & & \\
[161, 162, 163] & SVM, Multilayer Perceptron & \(\bullet\) & identifies host activity using the frequency & CPS & NM & NM \\  & & \(\bullet\) & destruction botnets & & & \\
[164] & FFCNN & \(\bullet\) & detects Low-Rate Denial of Service attacks & IoT & 99 & 99.45 \\  & & \(\bullet\) & protects the privacy and security of patients in & IoT & 94.23 & 93.45 \\
[165] & RF, Grid Search CV & \(\bullet\) & detection of MITM attacks in medical sensor networks & & & \\  & & \(\bullet\) & detects zero-day attacks & & & \\
[166] & RNN & \(\bullet\) & used in IDSs due to the possibility of extracting & & & \\  & & & functions from existing attack patterns and generating & CPS & NM & NM \\  & & & new variants of attacks to predict new and & & \\
[168, 169] & GAN, PSO & \(\bullet\) & adversarial attack detection & CPS & NM & NM \\
[170] & LSTM, RNN & \(\bullet\) & protects against adversarial attacks & IoT & 99.82 & 99.59 \\
[172] & DNN & \(\bullet\) & identifies and detects malware applications on the & Android system, IoT & NM & NM \\
[173] & FL & \(\bullet\) & identifies and detects malware applications in the IoT & IoT & 99.87 & 99.98 \\  & & & connected to WiFi, 5G, or B5G networks & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of overviewed proposed AI techniques for specific attacks and malware detection. various types of network traffic, devices, regular user traffic, and environmental attacks. This ensures that the test data generated will be both realistic and diverse. Operational scenarios should also consider different levels of attack complexity, different network protocols and attack styles, and different times of device connections and activity in the network, such as infinite attacks or data transformation attempts, depending on the assumptions made before preparing the test environment. Secondly, the set of cyber attack types is extensive. Probably, no system will detect all possible attacks and intrusions. So, these detection methods focus mainly on the most-typical attacks on CPSs, and identifying the most-prevalent threats is a good approach. However, detecting the least-popular, niche attacks should also be considered to prevent more-complex attacks using many types of attacks. Some CPSs must be secured against one or two attack types, but most of them must be secured against a broad spectrum of attacks, mainly in the case of users' data. Indicating specific threats or attack vectors that need to be considered is difficult. Both methods of defending against cyber attacks and conducting cyber attacks are constantly evolving. This all depends on the specific environment.