# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 6 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Year** & \begin{tabular}{c} **Feature Extraction** \\ **Method Used** \\ \end{tabular} & **Classifier Used** & **Attack Detected** \\ \hline [PERSON] et al. [23] & 2015 & NA & Outlier detection & Network attacks \\ \hline [PERSON] et al. [20] & 2017 & Modified K-means & \begin{tabular}{c} Multi-level Hybrid \\ SVM and ELM \\ \end{tabular} & \begin{tabular}{c} DoS, User to Root (U2R) and \\ Remote to Local (R2L) attacks \\ \end{tabular} \\ \hline [PERSON] et al. [19] & 2018 & Correlation method & \begin{tabular}{c} Ensemble method \\ (Naive Bayes, PART, and Adaptive Boost) \\ \end{tabular} & DoS, Probe, U2R, R2L \\ \hline [PERSON] et al. [32] & 2018 & Deep Belief Network & \begin{tabular}{c} Multi-layer ensemble \\ method SVM \\ \end{tabular} & \begin{tabular}{c} DoS, U2R, R2, Probe, Fuzzers, \\ Analysis, Backdoors, Exploits, \\ Generic, Reconnaissance, \\ Shellcode, Worms, Brute Force \\ FTP, Brute Force SSH, \\ Heartbleed, Web Attack, \\ Infiltration, Botnet and DDoS \\ \end{tabular} \\ \hline [PERSON] et al. [35] & 2018 & \begin{tabular}{c} Non-symmetric Deep \\ Auto-Encoder (NDAE) \\ \end{tabular} & Random Forest & \begin{tabular}{c} DoS (back, land, Neptune), \\ Probe (ipsweep, nmap, \\ portsweep, satan), R2L \\ (ftp\_write, guess\_password, \\ imap, multihop, phf, spy, \\ warezclient, warezmaster), U2R \\ (loadmodule, buffer\_overflow, \\ rootkit, perl) \\ \end{tabular} \\ \hline [PERSON] et al. [36] & 2018 & \begin{tabular}{c} Stacked Sparse \\ Auto-Encoder (SSAE) \\ \end{tabular} & SVM & DoS, Probe, R2L, U2R \\ \hline [PERSON] et al. [39] & 2018 & NA & \begin{tabular}{c} Fast Learning Network \\ improved using PSO \\ \end{tabular} & DoS, U2R, R2L and Probing \\ \hline [PERSON] et al. [28] & 2019 & PCA and Auto-Encoder & CNN & \begin{tabular}{c} DoS, U2R (illegal Access from \\ remote machines), R2L (illegal access to local super user \\ privileges), probe (supervisory and detection). \\ \end{tabular} \\ \hline [PERSON] et al. [29] & 2019 & NA & \begin{tabular}{c} CNN with improved \\ gcForest \\ \end{tabular} & \begin{tabular}{c} DoS, Exploits, Generic, \\ Reconnaissance, Virus and Web \\ attacks \\ \end{tabular} \\ \hline [PERSON] et al. [31] & 2019 & PCA & \
\begin{tabular}{c} Ensemble method (DT, \\ RF, KNN, DNN and \\ MultiTree) \\ \end{tabular} &
\begin{tabular}{c} DoS (SYN flood), Probe (port scanning), R2L (guessing \\ password), U2R (buffer overflow \\ attacks) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of Literature Review (Feature extraction method and Classifier). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Year** & **Feature Extraction Method Used** & **Classifier Used** & **Attack Detected** \\ \hline [PERSON] et al. [41] & 2021 & ICFS and PCA & Naive Bayes and SVM & Employed Double Layered Hybrid Approach (DLHA) for detecting DoS, Probe, R2L and U2R \\ [PERSON] et al. [22] & 2022 & NA & Improved random forest algorithm & Wireless network attacks \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._