## 2 Related Works
(2017) presented a comprehensive literature review examining various routing strategies for low-powered IoT systems. Here, the assessment was carried out based on identification, screening, eligibility, and inclusion. Moreover, their work investigated the strengths and limitations of several security-based routing methodologies used in WSN-IoT networks. [PERSON] et al. [PERSON] et al. (2018) implemented a hybrid deep-learning-based intrusion detection framework for WSN-IoT networks. In this paper, the authors intended to mitigate security attacks by analyzing a network traffic dataset. According to the pre-trained features, the authors' framework categorizes normal and malicious networking traffic in the network. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) constructed a multi-tiered intrusion detection (MDIT) framework for safeguarding WSN-IoT networks. Here, the Spotted Hyena Optimization (SHO) algorithm, integrated with the standard LSTM deep learning algorithm, was used to detect malicious events in cyber-data. Table 1 summarizes the limitations of state-of-the-art systems. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Methodology** & **Results** & **Limitations** \\ \hline [PERSON] et al. [PERSON] et al. (2018) & Investigated security challenges and requirements in WSN-IoT networks. & Identification of potential threats like eavesdropping, DoS, etc. & Low performance in various models. \\ \hline [PERSON] et al. [PERSON] et al. (2018) & Explored machine learning algorithms (classification, regression, clustering) for intrusion detection. & Comparative analysis based on prediction accuracy, energy, etc. & High false positive rate; increased time consumption for attack detection. \\ \hline [PERSON] et al. [PERSON] et al. (2018) & Implemented a lightweight GBM-based cyber-physical system. & Enhanced smart-networking environment. & Low performance in various models. \\ [PERSON]hline [PERSON] et al. [PERSON] et al. (2018) & Cross-layered IDS framework using a linear regression model. & Detection of malicious activities like blackholes, DDoS, etc. & High false positive rate; increased time consumption for attack detection. \\ \hline [PERSON] et al. [PERSON] et al. (2018) & Comprehensive review of machine-learning-based intrusion detection approaches. & Highlighted strengths and weaknesses of various ML algorithms. & Low performance in various models; insufficient memory use during classification. \\ \hline [PERSON] et al. [PERSON] et al. (2018) & Utilized LTINET-2020 dataset for classifying events suggested other datasets. & Identification of normal and intrusive events in WSN-IoT systems. & Inability to handle massive datasets. \\ \hline [PERSON] et al. [PERSON] et al. (2017) & Binary grey-wolf optimization with SVM for intrusion detection, considering feature set reduction. & SVM with reduced feature set achieved efficient intrusion identification. & High curse of dimensionality. \\ \hline [PERSON] et al. [PERSON] et al. (2017) & Anomalous intrusion detection and prevention protocol for WSN-IoT. & Increased network reliability. & Excessive memory use during classification. \\ \hline [PERSON] et al. [PERSON] et al. (2018) & Hybrid SPL and ALO algorithms for an IDS framework; authors focused on energy efficiency with a greedy routing strategy. & Enhanced network efficiency; defence against fraudulent attacks. & Low performance in various models. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table below provides a brief overview of the state of the art. The following are some of the major drawbacks of the current approaches that were identified in the literature review [37; 38]:  
* Low performance in various models;
* Excessive memory use during classification;
* High curse of dimensionality;
* Inability to handle massive datasets. In order to create a highly effective cyber-physical system for WSN-IoT networks, in the proposed work, we make use of innovative optimization and classification approaches.