## 2 Critical Review
ML methods are utilized to identify cyber attacks through training the algorithm to use the KDDCUP99 [43], whereas the KDDCUP99 [44] employs RF, DT, and GB algorithms to develop intrusion detection systems, and NB techniques are used for cyber-protection [45]. The above synthesis table summarizes the critical review which includes the year of papers, problems, approaches, merits, demerits, or research see Table 1: MQTTset is a dataset that uses raw traffic data from the MQTT protocol and is commonly used in IoT contexts. It is crucial to note that public datasets such as KDDCUP99 are available. Rather, MQTTset is distributed as both CSV and PCAP raw data, allowing users to manually handle raw data and generate alternative CSV files as needed. The proposed dataset, inparticular, encompasses both valid and malicious traffic.The following Table 2 describes previous work on MQTset dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref** & **Year** & **Problem(s)** & **Approach** & **Merit** & **D****Emerit or Gap** \\ \hline
[46] & 2022 & An efficient supervised ML intrusion detection system with excellent detection rates for IoT assaults. & The feature significance plots of the XGBoost and SVM models are constructed, as well as the calibration plots of the KNN, QDA, and NB algorithms. & The communication overhead is decreased, and the XGBoost produced excellent results. & The future step will be to use an ensemble model with a unique dataset appropriate for the IoT context, as well as as current analyses utilizing a deep learning model \\ \hline
[47] & 2021 & cyberattacks against network systems & Generative Adversarial Network (GAN) model: CTGAN, TableGAN, and CopulaGAN and discrete values & CTGAN, CopulaGAN, and TableGAN can be used as learning data for various Machine Learning tasks \\ \hline
[48] & 2021 & Classic security methods become less effective against the IoT cyber-attacks and cyber-attacks & The Pearson correlation measure, to highlight the features’ quality and the redundancy in the dataset & Quality analysis is provided in terms of correlation with the class label and redundancy. The selection of features plays an important role in minimizing data dimensionality & - \\ \hline
[49] & 2020 & DoS and probe attacks on IoT networks, notably RPL and 6 LoWPAN networks & XGBoost, DTs, Bagging Trees, and Network, NB, Adaboost RF & Good results are obtained using XGBoost, DTs, Bagging Trees, and RF & Bayes Network, NB and Adaboost need to be improved, and unsupervised testing will be expanded to construct and test the hidden Markov model and the Gaussian mixture model \\ \hline
[50] & 2019 & Detection of botnet and DDoS attacks & SVM, ANN, NB, DT, and Unsupervised Learning (USML) Matthews coefficient yields positive outcomes &
\begin{tabular}{c} Different data sets may be explored to validate the machine learning algorithms’ legitimacy. This article only looked at DDoS attacks \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Summarizes the critical review which includes the year of papers, problems, approaches, merits, demerits or research gap. \begin{table}
\begin{tabular}{c c c c} \hline
**Ref** & **Year** & **Objective** \\ \hline
[51] & 2023 & The author suggested balancing model performance with computational complexity by using a technique that involves training the model on a high-performance computing platform and using the learned model as a detection engine on low-performance platforms (such as the edge node of the edge layer). The suggested security paradigm has been demonstrated to be successful against a variety of internal and external threats. \\ \hline
[52] & 2023 & In this research, a lightweight intrusion detection system (LIDS) IoT model based on deep learning and a MLP network was proposed. The results demonstrated the effectiveness of deep learning in boosting the accuracy of an intrusion detection model. The proposed approaches decreased the number of characteristics by around half, resulting in a LIDS that operates in a limited context.