## 1 Introduction  
Recently, the use of interconnected smart devices commonly called the Internet of Things (IoT) has seen exponential growth [1]. IoT gadgets can be accessed from any place, home, vehicle and office to make daily tasks as simple as they can. Such smart devices are utilised in smart cities, healthcare services, vehicular networks, industries, smart grids, and smart homes [2]. These smart gadgets have unique features such as minimal power consumption and lighter protocols, weight and compact size which make them more adjustable. Expanded dispatch of smart gadgets in advertisements has decreased trust regarding detecting gadgets and has made the web of things increasingly versatile [3]. With the two downsides and upsides, the devices linked to the Internet are at risk of attacks and digital threats, prompting failure of the administration to more dreadful conveyed refusal of administration [4]. There are no confirmed security techniques that ensure the digital safety of such gadgets. IoT infrastructure is prone to terrible security threats and various attacks, because it lacks built-in security mechanisms and standard supporting systems [5].  
IoT has become a capitated platform for invaders since it has the potential to launch all types of network attacks on the connected devices, which in most cases, result in some serious losses.  
Ransomware can be referred to as a malware type that can be devised to block access to the user files, device, or operating system [6]. Figure 1 represents the process of ransomware detection for cybersecurity in IoT platforms. Ransomware is commonly found in the form of crypto-ransomware and locker ransomware. Crypto-ransomware encodes key documents on a system of the user, utilizing complicated encryption methods and demand payments, generally cryptocurrency for decoding the credentials of victims [7]. Locker ransomware displays a lock screen that stops the victim from opening their system and demands money for access to a computer. Ransomware is more destructive, prominent, and advanced [8]. In comparison to static code analysis methods, machine learning (ML) approaches have proven to be effectual. ML has high potential in finding malware in Android and Windows OS systems [9]. Further studies on ML in malware recognition as a substitute for the use of signs has shown efficiency regarding the use of ML-related detection over signature-related techniques [10]. The decision to assess ML and DL methods as opposed to other non-ML-related methods has been considered due to their strong ability and adaptability to find unseen samples of ransomware malware.  
This article focuses on the development of an Optimal Graph Convolutional Neural Network based Ransomware Detection (OGCNN-RWD) technique for cybersecurity in an IoT environment. Primarily, the OGCNN-RWD technique involves learning enthusiasm for teaching learning-based optimization (LETLBO) algorithms for the feature selection procedure. Next, the GCNN model is used for ransomware classification, and its hyperparameters can be optimally chosen by the harmony search algorithm (HSA). To demonstrate the better results of the OGCNN-RWD system, a series of simulations were made on the ransomware database.  
Figure 1: Ransomware detection for cybersecurity in IoT platform.  
The rest of the paper is organized as follows. Section 2 provides the related works, and Section 3 offers the proposed model. Then, Section 4 gives the results analysis, and Section 5 concludes the paper.