## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Author Names** & **Model Used** & **Dataset** & **Accuracy** & \
\begin{tabular}{c} **Computational** \\ **Efficiency** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} \\ \hline
2024 & \
\begin{tabular}{c} [PERSON]; \\ [PERSON]; [PERSON] \\ [PERSON] [5] \\ \end{tabular} & FA with ML & Not mentioned & 95\% &
\begin{tabular}{c} High (Complex \\ Optimization) \\ \end{tabular} & Moderate \\ \hline
2023 &
\begin{tabular}{c} [PERSON]; [PERSON]; [PERSON] [3] \\ \end{tabular} & QNNN & Not mentioned & 91\% & Medium & High \\ \hline
2023 &
\begin{tabular}{c} [PERSON]; [PERSON]; [PERSON] [4] \\ \end{tabular} & BCO & IoT-WSN & 93.5\% & High & Low \\ \hline
2023 & \
\begin{tabular}{c} [PERSON]; [PERSON]; \\ [PERSON] [6] \\ \end{tabular} & DL-HIDS &
\begin{tabular}{c} Satellite \\ Networks \\ \end{tabular} & 98\% & High & Low \\ \hline
2023 & \
\begin{tabular}{c} [PERSON]; [PERSON]. [7] \\ \end{tabular} &
\begin{tabular}{c} CNN with \\ Dimensionality \\ Reduction \\ \end{tabular} & WSN Dataset & 97\% & High & Moderate \\ \hline
2023 & \
\begin{tabular}{c} [PERSON]; [PERSON] [8] \\ \end{tabular} &
\begin{tabular}{c} Hybrid \\ Ensemble \\ Model \\ \end{tabular} & Secure Systems & 92\% & Medium & High \\ \hline
2023 & \
\begin{tabular}{c} [PERSON]; \\ [PERSON] [9] \\ \end{tabular} & Optimized DL &
\begin{tabular}{c} Blockchain \\ Sensor Systems \\ \end{tabular} & 94\% & High & Moderate \\ \hline
2023 &
\begin{tabular}{c} [PERSON]; \\ [PERSON] [10] \\ \end{tabular} & MSO & Not mentioned & 95\% & High & Moderate \\ \hline
2023 & \
\begin{tabular}{c} [PERSON]; [PERSON]; \\ [PERSON] [11] \\ \end{tabular} &
\begin{tabular}{c} ML for \\ Rare-Class \\ Attacks \\ \end{tabular} & Not mentioned & 90\% & Moderate & High \\ \hline
2023 & \
\begin{tabular}{c} [PERSON] [12] \\ \end{tabular} &
\begin{tabular}{c} Deep learning \\ IDS for IoT \\ \end{tabular} & IoT Networks & 92\% & High & Moderate \\ \hline
2023 &
\begin{tabular}{c} [PERSON]; \\ [PERSON] [13] \\ \end{tabular} & BFNN & Sensor Data & 96\% & High & Moderate \\ \hline
2023 &
\begin{tabular}{c} [PERSON]; \\ [PERSON] [14] \\ \end{tabular} & DCNNBiLSTM & WSN & 97\% & High & Moderate \\ \hline
2023 &
\begin{tabular}{c} [PERSON]; \\ [PERSON] [19] \\ \end{tabular} & NN Clustering & WSN & 94\% & Moderate & High \\ \hline
2023 & \
\begin{tabular}{c} [PERSON]; \\ [PERSON] [30] \\ \end{tabular} &
\begin{tabular}{c} DL Techniques \\ for DoS \\ Detection \\ \end{tabular} & WSN & 92\% & High & Moderate \\ \hline
2022 &
\begin{tabular}{c} [PERSON]; \\ [PERSON] [15] \\ \end{tabular} & AutoML-ID & WSN Dataset & 94\% & High & Moderate \\ \hline
2022 & \
\begin{tabular}{c} [PERSON]; \\ [PERSON] [16] \\ \end{tabular} &
\begin{tabular}{c} Hybrid ML \\ Feature Selection \\ \end{tabular} & NIDS & 91\% & Medium & High \\ \hline
2022 & \
\begin{tabular}{c} [PERSON]; [PERSON]. [18] \\ \end{tabular} &
\begin{tabular}{c} Majority Voting \\ and Feature Selection \\ \end{tabular} & Not mentioned & 89\% & Medium & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some recent studies related to intrusion detection in WSNs. The reviewed research articles demonstrate a growing interest in utilizing ML algorithms for ID in WSNs. However, the existing methods often face challenges such as high false alarm rates and limited adaptability to dynamic network environments. By leveraging advanced optimization techniques, such as the LOA, there is a clear opportunity to develop more effective classifiers for WSNs. These optimized classifiers have the capability to enhance the accuracy and efficacy of attack detection significantly, thus addressing the shortcomings of current approaches.