## 1 Introduction  
Today, AI has become a critical technology in information security. AI is used to spot cyber threats and potentially malicious activities, quickly analyze millions of events, and identify many hazards. By using deep algorithms, AI systems are trained to recognize patterns, detect malware, and even detect behaviours of malware or ransomware attacks. Malware or malicious software develops more quickly; it is designed to disable and gain unauthorized access to stolen sensitive information and computers. The primary motive for creating malware is to obtain financial gain.  
Additionally, AV-Test Threat Report logs more than 350,000 new malware programs daily, and in 2021 the malware circulation had increased to 114,530 million. Amazingly, January 2021 alone reported 607 million malware programs [1]. Expert hackers invent sophisticated malware evasion strategies such as polymorphism [2], code obfuscations, metamorphism [3], and others, which outperform many existing anti-malware solutions. The most common malware includes backdoors, passwords, scanning, miners, DDOS, ransomware, etc. [4]. Malware detection techniques are generally categorized into static [5] and dynamic systems [6]. A static system analyzes the disassembled code to extract the opcode sequence, strings, function call graph, API sequences, etc.  
A dynamic system, also known as behavioural analysis, is implemented in a controlled environment (known as the Sandbox) to trace networks or extract system calls. It is effective in identifying subliminal behaviour. This system is powerful but resource-intensive and lazy; moreover, the malware shows restricted behaviour in determining analysis environments. Furthermore, these systems are ineffective in detecting unknown malware or new variants. [7].  
Deep learning (DL) approaches are increasingly used in applications such as fraud detection, malware detection, fragmentation, image analysis, etc., enabling inputs to develop more robust cognitive representations by learning at several levels. Convolutional neural networks (CNNs) have been primarily responsible for advancements in DL. DL methods can quickly train a model with many convolutional layers and millions of parameters by learning complex features. In this study, the knowledge of CNNs was thoroughly trained on a large dataset. CNN techniques have gained importance in time-series classification (TSC) and malware detection [8; 9].  
This study contributes the following:  
* The model is DenseNet121 modified from working on two-dimensional to one-dimensional image shapes.
* To the best of our knowledge, there has been no comprehensive study on the ToN-IoT database. In this study, we conducted a complete investigation of the ToN-IoT database (Win7, Win10, Network, and IoT) to train the DenseNet and Inception Time models; the best result was when combining the network with Windows 10, with 100% accuracy.
* We used the Inception Time model in two shapes--first with a 1D input vector shape, and second with a time-series 2D input shape--using the sliding window technique with a window size of six in the start time model on the UNSW-NB15 database. There was a slight improvement in the results with multiple categories, with an accuracy of 98.6%.
* This paper is the first to use the Inception Time model on the new Edge-IIoT dataset, and there was an improvement in accuracy to 94.94% with multiple classes.  
The rest of this paper is organized as follows: Section 2 provides a summary of the background and related work. Section 3 presents a description of the dataset. Section 4 describes the background CNN architectures, outlining how the data should be processed and modelled to extract appropriate data for comparisons and discussion. The design of experiments is presented in Section 5. Finally, Section 6 analyzes the experimental results, before concluding the paper in Section 7.