## 2 Related Work
### Deep Learning Approaches for Intrusion Detection in WSNs  
[PERSON] and [PERSON] performed comprehensive study comparing the COA-GS-IDNN model with conventional machine learning classifiers across several publicly available malware benchmark datasets. The findings indicate that DNNs surpass traditional machine learning models in the real-time surveillance of network activity and host-level incidents for intrusion detection and prevention. Experimental findings indicate that the COA-GS-IDNN model surpasses current models across all critical performance parameters, achieving an accuracy of 95%, precision of 94%, recall of 96%, F1-score of 95%, ROC AUC of 98%, detection time of 1.0068754 s, and a latency of 0.8016 ms [16]. The AWID dataset primarily comprises 16 categories of intrusions and IDS are employed to identify malicious network traffic. [PERSON] et al. proposed a completely unsupervised k-means algorithm. Clustering machine learning methodology utilizing 50 features derived from a stacked auto-encoder for the 'impersonation' attack category. This method requires no prior labelling of the dataset during the training phase and achieved a 92% detection rate [17]. In any IDS, feature engineering, feature extraction, and feature selection are critical processes prior to the use of any classification method. [PERSON] et al. conducted feature engineering utilizing a Stacked Autoencoder (SAE) with two hidden layers, layers containing 100 and 50 neurons. Feature selection from SAE, utilized in the deep k-means clustering technique for the classification of 'normal' and 'impersonation' classes with k = 2, achieved an accuracy of 94.81% [18]. [PERSON] et al. proposed deep learning-based methodologies, including Stacked Autoencoders (SAE), a three-layer DNN, and a seven-layer DNN, to enhance the Intrusion Detection System (IDS) for categorization purposes Feature collection including  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Feature Set** & **Approach** & **Classification** \\ \hline [5] & 111, 41, 10 & Random Tree, OneR, Random Forest, AdaBoost, J48 & Multiclass \\ \hline [6] & 20 & C4.8, SVM, Naive Bayes (feature selection), ANN (classification) & Normal vs. Impersonation \\ \hline [7] & 140 & Optimized Maximum Correlation Feature Reduction + Random Forest & Multiclass (AWID-based IDS) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._71 elements generated post-preprocessing. The best accuracy rates attained by the 7-layered DNN for 'normal', 'impersonation', 'injection', and 'flooding' were 98.46%, 99.99%, 98.39%, and 73.12%, respectively [19]. A summary of recent 232 deep learning-based approaches for WSN intrusion detection is presented in Table 2.  
Recent deep learning approaches have demonstrated significant potential in enhancing intrusion detection systems, particularly for WSNs. In [16], a DNN optimized using the Coyote Optimization Algorithm with Global Search (COA-GS) was employed using a 23-feature input, achieving effective results in multi-class classification tasks. Reference [17] proposed an unsupervised learning approach targeting the impersonation class by applying K-Means clustering on 50 features extracted from a stacked autoencoder, eliminating the need for labeled data during training. Expanding on this, Ref. [18] utilized a stacked autoencoder with two hidden layers (100 and 50 neurons) to perform deep K-Means clustering with k = 2, achieving robust binary classification between normal and impersonation traffic. In [19], a series of architectures including a stacked autoencoder, a 3-layer DNN, and a more complex 7-layer DNN were evaluated using a 71-feature input, demonstrating strong performance across multiple attack classes in a multiclass intrusion detection setting. Collectively, these studies highlight the effectiveness of combining deep feature extraction and unsupervised or semi-supervised learning techniques to improve detection accuracy and reduce reliance on labeled data.