## 1 Introduction  
The Internet of Things (IoT) continues to attract attention, as it can effectively and intelligently sense the environment through a series of smart devices and enable various smart applications [1, 2]. In particular, the IoT has been developed to enable smart applications, such as smart cities, smart transportation, smart homes, smart vehicles, smart hospitals, etc. [3, 4]. It is expected that by 2026, more than 100 billion smart devices will be deployed, which is unprecedented in computer history [5]. However, there are also a variety of security concerns related to the enormous quantity of data streams transferring among smart devices in the context of the deployment of IoT technologies [6]. The application of traditional security protection measures, such as encryption, authentication, and access control, has been deemed insufficient for systems with a large number of linked devices in which each device them is subject to particular vulnerabilities [7, 8]. For instance, the security methodologies used in current IoT technologies and protocols, such as MQTT (Message Queue Telemetry Transport), Z-Wave, ZigBee, RFID (radio frequency identification), etc., have become insufficient to warrant full confidence in their reliability, demonstrating that such security mechanisms are likely to be defeated by new forms of attacks that cannot be detected by existing solutions. Furthermore, identifying which solutions are acceptable for securing IoT systems is difficult because of the challenges brought about by the emergence of a wide range of heterogeneous and decentralized IoT devices [7]. As a tremendous amount of data is being created by a variety of IoT applications, deep learning has now become an effective solution for IoT security issues. Deep learning is a well-known machine learning model built by artificial neural networks (ANNs) that aims to mimic the human nervous systems by using layers of perceptrons [9]. ANNs have been widely studied in recent years, as computational power has dramatically increased and shown a strong ability to perform various tasks, e.g., image recognition, event prediction, fault detection, etc. [10; 11; 12]. Typically, an ANN structure contains a number of layers, each of which contains a number of nodes or \"neurons\". Such nodes are assigned a learned weight and can be stimulated by certain inputs. It has been proven that with enough layers and nodes, an ANN is able to mine complex patterns among large-scale dataset. The application of deep learning to IoT security is currently a new and active research field that is being widely studied. On the other hand, most existing security methods are based on a centralized system architecture, which may not be suitable for large-scale IoT systems. One breach in such a security design might leave the entire network vulnerable. Many of the security parameters used in IoT devices rely on cryptographic algorithms that demand substantial resources [13]. Furthermore, since heterogeneous IoT system platforms have been studied, it is challenging to build one security protocol that is compatible across all platforms. To address the security issue in decentralized IoT systems, blockchain technology has been studied and adopted to build secure IoT applications [14; 15]. Transaction authentication on a blockchain does not require the approval of a central authority, as is the case in centralized systems. Instead, it relies on the approval of a number of participants within the system. Accordingly, blockchain technology can reliably and transparently record transactions reliably owing to its decentralized architecture. Considering the decentralized paradigm of IoT systems, blockchain has emerged as a promising solution to ensure IoT security [16]. In general, deep learning and blockchain have become the most popular and promising techniques for the development of secure and decentralized IoT systems.