## 2 Related Works  
One author used deep learning (DL) approaches for the extraction of the latent representation of high dimensions of the gathered dataset for precisely finding malevolent behavior [11]. Specifically, this method rests on a hybridized feature engineering approach of a variational and traditional autoencoder (AE). This approach was utilised to minimize the dimension of data and to extract a good representation of accumulated system activities. Afterwards, the novel feature vector was sent to classifiers that can be framed on batch normalization and deep neural network (DNN) methods. The authors in [12] presented a detection system relevant to the stacked variational AE (VAE) with a fully connected neural network (FC-NN) that learns the latent framework of system activities and exposes the ransomware performance. In addition, the author came up with a data augmentation approach that depends on VAE to produce novel datasets that can be utilised in training an FC network to enhance the generalized capabilities of the presented recognition model.  
[PERSON] et al. [13] modelled a new aimed ransomware detection method devised for the industrial IoT edge mechanism. It leverages DL and Asynchronous Peer-to-Peer Federated Learning (AP2 PFL) methods as targeted ransomware recognition methods. The presented technique contains two modules. The Diagnostic and one Decision Module (DDM) was utilized for finding targeted ransomware and its phases depend on DNN and Batch Normalization (BN). [PERSON] et al. [14] introduced the DL-related new ransomware detection structure in supervisory control and data acquisition-controlled electric vehicle charging station (EVCS) with the performance analysis of three DL methods.  
[PERSON] and [PERSON] [15] devised a fast ransomware identification approach utilizing Memory-based Stochastic-Dynamic-Fixed-Point arithmetic utilizing a four-layer deep belief network (DBN) architecture. The technique stored random bit-streams in storage for producing potential cross-correlation for stochastic computation in Field Programable Gate Arrays (FPGAs). [PERSON] and [PERSON] [16] presented a context-aware ransomware predictive approach that leverages context ontology to derive data features (software updates, connection requests, etc.) and ML techniques to predict ransomware. The presented approaches rely on and focus on the initial detection and prediction of ransomware penetration attempts to resource-limited IoT mechanisms. A weighted minimum Redundancy maximum Relevance (WmRmR) algorithm has been modelled for superior feature impact prediction in datasets captured at the primary stages of a ransomware attack [17]. This presented approach can assess if the feature in the appropriate set was significant or not. It includes a smaller number of evaluations and low-dimensional complexity than the original mRmR approach.  
Several models exist in the literature that perform the ransomware classification process. Although several ML and DL models for ransomware classification are available in the literature, a model is still needed that can enhance the classification performance. Owing to the continual deepening of the model, the number of parameters of DL models also increases quickly, which results in model overfitting. At the same time, different hyperparameters have a significant impact on the efficiency of the CNN model. Particularly, hyperparameters such as epoch count, batch size, and learning rate selection are essential to attain effectual outcomes. Since the trial and error method for hyperparameter tuning is a tedious and erroneous process, metaheuristic algorithms can be applied. Therefore, the HSA algorithm can be applied to the parameter selection of the GCNN model.