## 9 Conclusions  
In cybersecurity, using ML algorithms takes much attention, especially in intrusion detection systems (IDS). Therefore, significant research has been conducted to improve the speed, accuracy, precision, and other essential metrics of ML-based IDS. Moreover,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Defense**} & \multirow{2}{*}{**Attack Type**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**ML/DL Model**} & \multicolumn{2}{c}{**Can Address**} & \multirow{2}{*}{**Defense Category**} & \multirow{2}{*}{**Result**} \\  & & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \multirow{4}{*}{[52]} & \multirow{4}{*}{2019} & \multirow{4}{*}{2K-GanDef} & \multirow{4}{*}{White-box} & - & MNIST & \multirow{4}{*}{NN} & \multirow{4}{*}{Yes} & Changing the training procedure and input data & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline \multirow{4}{*}{[55]} & \multirow{4}{*}{2020} & \multirow{4}{*}{ASD} & \multirow{4}{*}{White-box} & - & NSI-KDD & \multirow{4}{*}{DNN} & \multirow{4}{*}{Not apparent} & Adding an extra network & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline \multirow{4}{*}{[54]} & \multirow{4}{*}{2021} & \multirow{4}{*}{Def-IDS} & \multirow{4}{*}{White-box} & - & CSE-CIC-IDS2018 & \multirow{4}{*}{DNN} & \multirow{4}{*}{Yes} & Adding an extra network & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline \multirow{4}{*}{[56]} & \multirow{4}{*}{2021} & \multirow{4}{*}{APE-GAN++} & \multirow{4}{*}{White-box} & - & MNIST & \multirow{4}{*}{CNN} & \multirow{4}{*}{Yes} & Adding an extra network & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Defense Strategies Using GAN.  
adversarial attacks can have a significant impact on ML algorithms. Hence, the ML-based IDS is vulnerable to adversarial attacks that spark security concerns.  
For example, the IDS classification accuracy is affected by identifying a \"malicious\" input as \"benign\" or vice versa. In this situation, the IDS will be unreliable in defense, posing a severe threat to our networks. Thus, this paper presented a general overview of the ML methods in IDSs to improve their performance. Furthermore, it clarifies the various types of adversarial attacks that can affect the IDS based on ML to evaluate its robustness. In addition, we mentioned the benchmark datasets for IDSs and some state-of-the-art defense strategies that improved IDS accuracy. Finally, we discussed the open issues facing implementing defensive methods to improve ML-based IDSs.  
Conceptualization, M.A.R. and A.A.; methodology, A.A.; software, A.A.; validation, M.A.R. and A.A.; formal analysis, A.A.; investigation, A.A.; resources, A.A.; data curation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, M.A.R.; visualization, A.A.; supervision, M.A.R.; project administration, M.A.R.; funding acquisition, M.A.R. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge Qassim University, represented by the Deanship of \"Scientific Research, on the financial support for this research under the number (COC-2022-1-1-J-24954) during the academic year 1444 AH/2022 AD\".  
Not Applicable.  
The authors declare no conflict of interest.