## 6 Lightweight IDS for IoT Devices
### Digest on the Techniques Adopted for Lightweight IDS in IoT
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Paper ID** & \begin{tabular}{c} **Publication** \\ **Year** \\ \end{tabular} & **Dataset** & **ML Algorithm** & **Targeted Attack** & **Tool and Libraries** & **Accuracy** & \begin{tabular}{c} **Lighweight** \\ **Technique** \\ \end{tabular} & \
\begin{tabular}{c} **System Specifications** \\ **Mellowsecond** \\ \end{tabular} &
\begin{tabular}{c} **Processing Time** \\ **Mellowsecond** \\ \end{tabular} \\ \cline{3-14}
[134] & 2022 & UNSW-NB16 & \begin{tabular}{c} XCBhost, CatBoost, \\ KNN, SVM, ODA, \\ and NB \\ \end{tabular} & \
\begin{tabular}{c} Nedicose, DoS, \\ Keombens, resource, \\ Shubits, resources, \\ Worm \\ \end{tabular} & - & 99\% &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & - & - & - & 3.17 \\ \hline
[113] & 2022 & \begin{tabular}{c} NSL+CD0, \\ CICDB2017 \\ \end{tabular} & \begin{tabular}{c} B-Stacking \\ Classifiers \\ \end{tabular} & \
\begin{tabular}{c} DDA5 (12, RBL), \\ Prok \\ \end{tabular} & - & 98.5\% &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & 8 GB & 29 GHz & 20 & 280 \\ \hline
[140] & 2022 & \begin{tabular}{c} I/23 Adjer \\ intrusion dataset \\ \end{tabular} & \begin{tabular}{c} Variation \\ Autoencoder MLP \\ \end{tabular} & \begin{tabular}{c} DDA6, Mati, Terri \\ Trip \\ \end{tabular} & \
\begin{tabular}{c} Woka \\ Worm \\ \end{tabular} & 99\% &
\begin{tabular}{c} Collaborative \\ \end{tabular} & - & - & - & - \\ \hline
[120] & 2022 & \begin{tabular}{c} AW/D2 \\ \end{tabular} & \begin{tabular}{c} Efficient-Net \\ BOCANN \\ \end{tabular} & \begin{tabular}{c} improvement, \\ injection, flooding \\ \end{tabular} & \begin{tabular}{c} Python, \\ OpenCV \\ \end{tabular} & 99.9\% & \
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & 32 GB &
\begin{tabular}{c} NVIDIA RTX \\ 2/07 GPU \\ \end{tabular} & - & 3,600,000 \\ \hline
[121] & 2022 & \begin{tabular}{c} AW/D2 \\ \end{tabular} & \begin{tabular}{c} Shallow classifiers \\ \end{tabular} & \begin{tabular}{c} injection, Deaths, \\ Deep Neural \\ \end{tabular} & \begin{tabular}{c} 99.5\% \\ \end{tabular} & \begin{tabular}{c} Python \\ Slamer \\ \end{tabular} & 99.5\% & \
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & 64 GB &
\begin{tabular}{c} AMD B/ram 7 \\ 2/070 GPU \\ \end{tabular} & - & 3,000,000 \\ \hline
[122] & 2022 & \begin{tabular}{c} AW/D3 \\ \end{tabular} & \begin{tabular}{c} MPL Autoencoder, \\ D1, LightSM, and \\ bagging \\ \end{tabular} & \begin{tabular}{c} NSL+D1, \\ SSDP association, \\ Spending \\ \end{tabular} & \begin{tabular}{c} Python \\ Slamer \\ \end{tabular} & \begin{tabular}{c} Peak Accu- \\ v9x9x7\% \\ \end{tabular} & \begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & 64 GB & \
\begin{tabular}{c} AMD B/ram 7 \\ 2/07 GPU \\ \end{tabular} & - &
\begin{tabular}{c} 21,600,000 \\ (Bagging) \\ \end{tabular} \\ \hline
[118] & 2021 & \begin{tabular}{c} I/2020 \\ DT, RS, LDA \\ \end{tabular} & \begin{tabular}{c} SVM, RNN MLP, \\ SPending \\ \end{tabular} & \begin{tabular}{c} Mfa, Dd Scan, \\ Skoam \\ \end{tabular} & 98\% & \
\begin{tabular}{c} Meta, Dd Scan, \\ Skoam \\ \end{tabular} & 98\% &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & 4 GB & - & 80 & - \\ \hline
[119] & 2021 & \begin{tabular}{c} ADFA-LD \\ \end{tabular} & \begin{tabular}{c} MLP \\ \end{tabular} & \
\begin{tabular}{c} Hybrid-RTY, \\ Java-Metepper \\ \end{tabular} & - & 90\% &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & - & 44 GHz & 4.404 & - \\ \hline
[124] & 2020 & \begin{tabular}{c} NSL+XCD0, \\ KDDC01099 \\ \end{tabular} & RF & \begin{tabular}{c} DDA6, ULR \\ RBL, Probe \\ \end{tabular} & \
\begin{tabular}{c} 99\%(kDD) \\ \end{tabular} &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & - & - & 1.78 & 0.28 \\ \hline
[144] & 2020 & \begin{tabular}{c} Self-generated \\ \end{tabular} & \begin{tabular}{c} NB, LR, KNN, RF, \\ DT, ANN \\ \end{tabular} & \begin{tabular}{c} [PERSON], Haglme, \\ Reaper, Reshalle \\ \end{tabular} & - & \begin{tabular}{c} 97\% (device \\ 999\%(Glog \\ server) \\ \end{tabular} & \begin{tabular}{c} \\ \end{tabular} & \begin{tabular}{c} 97\% (device \\ 999\%(Glog \\ server) \\ \end{tabular} & \
\begin{tabular}{c} \\ \end{tabular} &
\begin{tabular}{c} 1\% (device \\ 999\%(Glog \\ server) \\ \end{tabular} & - & - & - & - \\ \hline
[119] & 2020 & \begin{tabular}{c} AW/D2 \\ \end{tabular} & SVM & \
\begin{tabular}{c} Impersonation, \\ flooding, injection \\ \end{tabular} & - & 96.2\% &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & - & - & - & 299.97 \\ \hline
[123] & 2020 & - & DT & Exploit, Dd,s, Probe & SK-learn & 85\% &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & - & - & - & - \\ \hline
[125] & 2020 & \begin{tabular}{c} Self-generated \\ \end{tabular} & \begin{tabular}{c} RF, DT, SVM, and \\ BFNN \\ \end{tabular} & \
\begin{tabular}{c} Miral and \\ BASH/ITE \\ \end{tabular} & Woka & 99.7\% &
\begin{tabular}{c} Feature \\ Selection \\ \end{tabular} & - & - & 30,000 & 113,000 \\ \hline \hline \end{tabular}
\end{table}