## 2 Related Work  
Machine learning techniques are included and applied in a variety of articles dealing with malware identification and categorization. Existing works use dynamic or static analysis as a way to extract features that use malware detection. For example, [PERSON] et al. introduced two static analysis methods for Android malware [5]. The first strategy relies first on permissions, while the second method relies on parsing the source code aided by the application of machine learning which uses a model that represents a bag of words to describe the data. The F-score for source code-based classification models was 95.1%, while the F-measure for permission-based classification models was 89% [6].  
[PERSON] et al. redirected all malicious processes from the hacked device to a limited virtual machine via installation techniques, so it is a defensive solution for malware. In addition, they returned the results of the malicious behavior to the malware as if it had been performed on the real device [13].  
[PERSON] et al. suggested the classification and protection of vehicles from malware. They also offered a security architecture to keep vehicles safe. They used virtualization to make it less vulnerable to attacks. All vital functions of the vehicle were controlled by a real-time operating system, while at the same time, several additional operating systems were used to control non-critical functions of the vehicle (information and entertainment, information technology, etc.) [14].  
[PERSON] et al. introduced a method that relies on machine learning to detect all the anomalies caused by malware in real-time broadband network traffic. After that, the researchers and scientists presented an effective and essential algorithm which involved identifying malicious actions within the network environment, and they also performed experiments that verify the accuracy of the suggested algorithm and make comparisons with new technologies [9].  
[PERSON] et al. presented a TC-Droid to utilize the on-text categorization algorithm to automatically find malware on Android devices. The suggested system takes the script sequence of AndroPyTool application analysis reports and uses a CNN to analyze all relevant information in the original report, rather than manually engineering features [10].  
[PERSON] et al. extracted features in the source code and apps' manifest files and used them to make eight feature sets [12]. A support vector machine classification algorithm related to malware detection was trained by utilizing this method, and a relatively high rate of malware family detection was obtained when employing this classifier [15].  
For better virus detection on Android IoT devices, [PERSON] et al. developed a novel framework combining machine learning and blockchain technology. Sequential clustering, classification, and blockchain are all used in the suggested technique. Machine learning removes malware data automatically and stores them in the blockchain. Thus, the network can broadcast all malware data stored in the blockchain history, enabling excellent malware detection [16].  
[PERSON] et al. proposed a model in which opcode-level convolutional autoencoders compress long opcode sequences, and dynamic recurrent neural network classifiers execute prediction tasks using the codes generated by the opcode-level convolutional autoencoder [17].