## 3 Machine Learning Algorithms-Revisited
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Learning Models** & **Datasets** & **Number of Features/Number of Records** & **Cyber-Attacks** \\ \hline \multirow{3}{*}{[20]} & Convolutional neural & \multirow{3}{*}{NSL-KDD/IoTID20/} & 43 Features/140,000 & Norm, DoS, Probe, root to local \\  & network (CNN), long & & 83 Features/450,000 & (R2L), user to root (U2R), / Mirai, \\  & short-term memory (LSTM), & & 83 Features/450,000 & DoS, Scan, MITM \\  & CNN-LSTM & & & \\ \hline \multirow{3}{*}{[21]} & LightGBM, Optimized & \multirow{3}{*}{NSL-KDD/IoTID20/} & 43 Features/140,000 & Norm, DoS, Probe, R2L, U2R \\  & Adaptive and Sliding & & 83 Features/450,000 & /Mírai, DoS, Scan, MITM \\  & Windowing (OASW) & & & \\ \hline \multirow{3}{*}{[22]} & Shallow CNN & NSL-KDD & 43 Features/ & \\  & Shallow CNN & NSL-KDD & 150,000 Records & Norm, DoS, Probe, R2L, U2R \\ \hline \multirow{3}{*}{[23]} & Bagging, J48, KNN, & \multirow{3}{*}{Multilayer Perceptron (MLP), Ensemble} & NSL-KDD/ & 11–60 Features/ & Norm, DoS, Probe, R2L, U2R \\  & Multilayer Perceptron (MLP), & & IoTID20 & 150,00–450,00 & /Mírai, DoS, Scan, MITM \\  & Ensemble. & & & \\ \hline \multirow{3}{*}{[24]} & Adaboost, DT & KDDCUP99, UNSW-NB15, & 43–100 Features/ & \\  & NSL-KDD, CICIDS2017 & 140,000–612,000 & DDoS, flooding, U2R, Jamming \\ \hline \multirow{3}{*}{[25]} & Gradient Boosting Machines, & \multirow{3}{*}{ToN\_IoT} & 7 Features/ & Normal, DoS, DDoS, Injection, \\  & RF, NB, Deep Neural & & 1,300,000 records & MITM, Password, Scan, Cross-site \\  & Networks (DNN) & & & scripting (SSB), Backdoor, Kansome. \\ \hline \multirow{3}{*}{[26]} & SVM, NB, SNN, RF & \multirow{3}{*}{N\_BaIoT, Bot\_IoT} & 114 Features/ & \\  & SVM, NB, SNN, RF & & 612,000 records & Normal, Bashlite, Mirai \\  & & & & \\ \hline \multirow{3}{*}{[27]} & Adaboost, RusBoost, & WUST\_IOT-2018, & 100–114 Features/ & Normal, Bashlite, Mirai, \\  & Bagging, Ensemble & N\_BaIoT, and Bot\_IoT & 100,000–612,000 & Port/Address Scanner. \\ \hline \multirow{3}{*}{[28]} & AdaBoost & CICIDS 2019. & 88 Features/ & DDoS, Heartbleed, SQL Injection, \\  & & & 4,201,795 Records & Botnet. \\ \hline \multirow{3}{*}{[29]} & SNN, SVM, NB, RF, & NSL-KDD, KDDCup99, & 43–100 Features/ & \\  & Self-organizing map & ADFA-LD12, UNSWNB15 & 140,000–612,000 & DDoS, flooding, U2R, Jamming \\ \hline \multirow{3}{*}{[30]} & Ensembles(Boosted DT, & \multirow{3}{*}{Distilled-Kitsune-2018/NSL-KDD} & \multirow{3}{*}{43 Features/ 145,00–150,000} & Mirai, operating system (OS) Scan, \\  & Subspace kNN, RUSBoosted & & & Fuzzing, Video Injection, Address \\  & DIT, SNN, Blawered NN, & 2018/NSL-KDD & 43 Features/ & Resolution Protocol (ARP), Wiretap, \\  & Logistic Regression Kernel & dataset & 145,00–150,000 & simple service discovery protocol (SSDP), Synchronous DoS, secure \\  & & & & \\  & Logistic Regression Kernel & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{[31]} & Beta Mixture Model & BoT-IoT 21 & 12 Features/ & DoS, DDoS, Keylogging, OS and \\  & & & & & Service Scan, and Data calibration \\  & & & & & attacks \\ \hline \multirow{3}{*}{[32]} & AdaBoost DT & \multirow{3}{*}{TON\_IoT\_2020 datasets} & 7 Features/ & DoS, DDoS, Injection Attacks, MITM, \\  & & & & Password Attacks, Scanning, XSS \\ \cline{1-1}  & & & 1,300,000 & Attacks, Backdoor attacks, and \\ \cline{1-1}  & & & & & Rawsome attacks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Decision Trees (DT) are widely used machine learning methods in various fields, such as image processing, pattern recognition, and classification [33]. Figure 4 shows a generic model of the DT, and continuously, the data are divided into subset nodes based on a particular parameter [7]. Bagged Trees (BT) is a machine learning algorithm that can be used as a classifier and solve the variance issue of a dataset with a noisy sample. Figure 5 depicts an overview of the BT process. In the beginning, the dataset is divided into samples to be prepared for  
Figure 4: Decision Trees. Figure 3: Shallow Neural Networks.