## 1 Introduction  
IoT is a revolutionary computing paradigm that has evolved rapidly over the last decade in almost every technological domain, such as smart homes, smart industries, smart transportation, smart healthcare [1, 2, 3, 4], use of sensors [5, 6, 7, 8], smart cities, and satellites [9], to name a few [10]. It comprises many IoT devices (Things) equipped with different sensors,actuators, storage, computational and communicational capabilities to collect and exchange data over the traditional internet [11]. The data captured and processed within the IoT network is of a sensitive nature that demands security from possible intrusions. Different security mechanisms such as firewalls, authentication schemes, different encryption methods, antiviruses, etc., are currently used to protect sensitive data from possible security attacks [12; 13; 14] on vulnerable devices [15; 16; 17] like the distributed denial of service (DDoS) attacks [18; 19; 20; 21] as the first line of defense. IoT can implement using software-defined networking [SDN] [22; 23; 24], future network architecture [25; 26; 27; 28], named data networking (NDN) [29; 30; 31] and cloud computing network [32] with voice over IP (VoIP) [33; 34; 35; 36] fiber optics [37; 38; 39], worldwide interoperability for microwave access (WiMAX) [40; 41; 42], deep learning (DL) [43], artificial intelligence (AI), and machine learning (ML) [44]. Due to the involvement of a huge amount of data, many new anomalies (either novel or the mutation of an old anomaly) are being generated very frequently. Thus, an intrusion detection system (IDS) that can act as a second line of defense can provide extra protection to an IoT network against security attacks. An IDS can be classified based on the deployment method and detection methodology. An IDS can be host-based IDS or a network-based IDS based on its deployment, but it can also be signature-based, anomaly detection-based, specification-based, or hybrid detection based on the detection method [45; 46]. For this study, our focus is to provide security to the IoT at the entry points by adopting the network-based IDS (NIDS) using the anomaly detection-based detection strategy. The main problem with the current IDSs is the increase in the False Alarm Rate (FAR) in detecting the zero-day anomalies [47]. Researchers have recently explored the possibility of using machine learning (ML), and deep learning (DL) approaches to improve detection accuracy and reduce the FAR for NIDS. Studies have shown that both ML and DL methodologies are efficient tools to learn valuable patterns from the network traffic to classify the flows as an anomaly or benign [48]. The DL has shown efficiency in learning valuable features from the raw data due to its deep architecture without human involvement, highlighting its importance of usage within NIDS for IoT networks. Deep Neural Networks (DNNs) are critical DL algorithms widely explored by researchers in different fields such as natural language processing, computer vision, and network security, etc. DNNs have performed exceptionally well in those fields due to their deep architecture to provide multiple abstractions for learning complex features for efficient predictions [49]. These attributes of DNN have made it an ideal methodology to be adopted for an IDS designed for an IoT network due to the involvement of a massive amount of data generated by IoT devices. In this study, we focus on the possibility of utilizing DNN for proposing an efficient NIDS solution within the framework of IoT. The main contributions of this study are four-fold. (1) To extensively discuss the state-of-work DL-based NIDS methodologies. (2) To propose an effective anomaly detection method for an IoT network using the DNN. (3) To test the efficiency of our model, we wanted to evaluate it by using the IoT-Botnet 2020 dataset and compare the performance of our model against different NIDS models based on different supervised DL algorithms.