## 2 Related Works
### Deep Learning  
Deep learning-based malware classifiers have illustrated a notable performance gain over traditional methods in the past decade. In a recent study, [PERSON] et al. [13] proposed a LeNet-like [42] stacked convolutional neural network (CNN) to classify malware images. They reported a validation accuracy of 99.37% on the nine-class malware dataset. [PERSON] et al. [43] utilized a local binary pattern (LBP) to extract malware features and leverage similar network architecture as [PERSON] et al. [13] to classify the Malimg dataset. In their work, they reported 93.17% classification accuracy on the validation data. [PERSON] et al. [17] also utilized a similar feature extraction architecture to their prior methods and revised the softmax classifier with an SVM classifier in their work to achieve an accuracy of 77.23% in the Malimg dataset. However, in their setup, GRU-SVM outperformed their CNN-SVM structures by a notable margin. Later, [PERSON] et al. [16] also proposed a CNN with four consecutive blocks, combining convolution and max pooling operation to achieve 96.10% on the Malimg dataset. [PERSON] et al. [44] also used a CNN with flow data to reach just over 85% classification on a nine-class dataset. [PERSON] et al. [14] also proposed a deep network called M-CNN and reported an accuracy of 98.52% in the validation phase while using the Malimg dataset. In another work, [PERSON] et al. [12] proposed a deep stacked CNN with 13 convolution layers to achieve an accuracy of 99.26% on a nine-class malware dataset with a 10-fold validation strategy. In addition, [PERSON] et al. [15] compared different network architectures in their study with a 17-class malware dataset and reported an accuracy of 89.55% with a 2D CNN architecture. They found that with pre-trained based Resnet-152 [22], VGG-19 [18] architecture can outperform their 2D CNN [42] with a marginal score. Although several novel works explore different CNN architectures in their respective works, it has been observed that training a CNN without pre-trained weight achieved unsatisfactory performance in malware classification [45]. It is worth noting the existing malware benchmark datasets lack data diversity. Therefore, many recent works utilized Imagenet [46] pre-trained on their respective methods [47]. For instance, [PERSON] et al. [48] proposed to use a VGG-16 [18] with Imagenet pre-trained weights to classify malware images. They achieved a validation accuracy of 90.77% accuracy with a 10-fold validation strategy on a 20-class dataset. Similarly, [PERSON] et al. [49] also used transfer learning on Resnet-18 [22], 34, 50, 101, 152, and GoogleNet [50] to achieve 83%, 86.51%, 86.62%, 85.94%, 87.98%, and 84% validation accuracy. Also, [PERSON] et al. [3] leveraged a VGG-19 architecture with frozen weights and fused it with simple spatial attention. They reported an accuracy of 97.38% with class balancing on the Malimg dataset. Similarly, [PERSON] et al. [11] combined two pre-trained weights (i.e., ALexNet [51] and Resnet0152 [22]) to classify the malware images. They reported 97.18% accuracy on the Malimg dataset. Most of the IoT malware detection studies are based on static analysis [52; 53]. [PERSON] et al. [54] used their OPCODE-based IoT dataset with Recurrent Neural Network (RNN) [55] and achieved an accuracy rate of 98,18%. [PERSON] et al. [56] used the IoTPoT dataset and converted the malware binary files into gray-scale images. They used the CNN-based classifier and achieved an accuracy rate of 94%. [PERSON] et al. [57] created their dataset and compared the characteristics of IoT malware and Android malware, mainly based on Linux, using Control Flow Graphs. Also, they proposed a detection mechanism based on CNN, which achieved a very high accuracy rate of 99.66%. [PERSON] et al. [58] used the IoT dataset and achieved an average accuracy rate of 96.41% with the Fuzzy Pattern Tree algorithm. [PERSON] et al. [59] presented a Cross-Architecture IoT Malware Detection method called MTAEL.