# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 7 Conclusions and Future Work  
The current IDSs are still ineffective, largely due to the susceptibility of the anticipated wireless paradigms, despite having previously adopted various ML tactics to increase their performance. In the research, the CICDDOS2019 dataset is utilized and analyzed using ML models, where the CICDDOS2019 dataset includes a wider range of attacks, such as DDoS, DoS, brute force, XSS, SQL injection, botnets, web attacks, and infiltration. Susceptibility to attacks caused by predictable context (e.g., replay attack) because they rely on the limited entropy of wireless physical context to protect a shared key. This study developed a unique IDS technique to handle imbalanced FS and EL algorithms' preferred hybrid approaches, which are based on high-dimensional traffic with less DR. Using samples from NSL KDD, UNSW NB 2015, CIC_IDS2017, and CICDDOS2019, a hybrid EL approach was used to obtain the best 30 features, 35 features, a subset of function correlation, and 40 features, respectively. The values of the FAR for the datasets UNSW NB2015, NSL KDD, CIC_IDS2017, and CICDDOS2019 were 0.0039, 0.0076, 0.0009, and 0.00113, respectively, with an accuracy of 99.7% for all datasets. The results comparison table includes information on other parametric values. The number of records selected was inversely associated with the percentage of beginning KDDCup'99 dataset records picked for each difficulty degree categorization. In our sample, there were 125.973 occurrences of the KDD_Train set, including 58.630 attacks and 67.343 instances of routine traffic. The majority of current low-key assaults were included in the second dataset (UNSW-NB15), which aimed to replicate current network configurations. It contained 2,540,044 records spread across 4 big-data CSV files, 45 columns (id = 1, features = 44), 175,341 training records, and 82,332 testing records. The system approach performed better than the current classification algorithms. This approach gave the IDS market a large competitive advantage over other tactics. Although \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fih** & **Dataset** & **Number of FS** & **Accuracy \%** & **FAR \%** & **DR \%** \\ \hline
[45] & NSL\_KDD & N/A & 81 & N/A & 82 \\  & NSL\_KDD & 16 & 84 & 4.85 & 83 \\
[46] & NSL\_KDD & 32 & 86 & 15.01 & N/A \\
[47] & NSL\_KDD & 10 & 79 & 1.01 & N/A \\  & NSL\_KDD & 42 & & 5.12 & 96.3 \\  & UNSW\_NB2015 & 13 & 95.2 & N/A & 96.65 \\  & CIC\_IDS2017 & 8 & & N/A & 98.94 \\
[48] & NSL\_KDD & 11 & 98 & N/A & 98.76 \\  & CIC\_IDS2017 & N/A & 98.8 & N/A & N/A \\
[51] & CIC\_IDS2017 & N/A & 99 & N/A & N/A \\  & NSL\_KDD & & 87 & 0.004 & 99 \\  & UNSW\_NB2015 & 30 & 85 & N/A & N/A \\  & NSL\_KDD, & 30 & 99.4 & 0.004 & 99.9 \\
[53] & UNSW\_NB2015 & 35 & 99.8 & 0.008 & 99.6 \\  & CIC\_IDS2017 & 40 & 99.7 & 0.0012 & 99.4 \\  & NSL\_KDD, & 30 & 99.5 & 0.0038 & 99.9 \\ Our System & CUSW\_NB2015 & 35 & 99.9 & 0.0076 & 99.7 \\  & CIC\_IDS2017 & 40 & 99.8 & 0.0009 & 99.5 \\  & CICDDOS2019 & 40 & 99.95426 & 0.00113 & 99.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the reimplemented system’s findings with those from other studies. CFS-RF with ensemble HABBA algorithms has advantages, further work is still needed to strengthen the system's ability to handle potential threats from infrequent future traffic. IDS has applied a connection record for each separately, and by putting the reimplemented NIDS on the private security server firms' systems, the authors think that looking at connections in a stream of data can be useful in identifying undetectable assaults. The provided method is an excellent and trustworthy way to identify network breaches quickly and precisely. In regard to future work, robust machine learning models are required to handle adversarial inputs. In order to build models resilient against hostile inputs, the model should be trained in hostile scenarios. We have examined some machine learning models that use different datasets to identify a threat to 6G security.