## 1 Introduction
Anomaly-based IDS detection gains advantages in IoT because it detects zero-day attacks and needs fewer human interventions [20]. The hybrid approach combines both signatures-based and anomaly-based approaches. However, because it is impractical to rely on pre-defined attack patterns (signature-based) intrusion detection in IoT, the utilization of the signature-based IDS is limited in IoT networks [18; 19; 20]. To this end, anomaly intrusion detection systems play a vital role in intrusion detection in IoT environments. Most of the existing IDS use conventional machine learning techniques to develop detection models [21]. Machine learning techniques were widely adopted to construct the IDS model. However, due to the speed and volume of the IoT-generated data, conventional machine learning techniques that need well-crafted features engineering need intensive research efforts to extract the representative features from big and unstructured data generated by IoT devices. Thus, conventional machine learning-based solutions still encounter many challenges. Recently, deep learning techniques (DL) have been widely adopted for intrusion detection systems. DL expedites the analysis between fast and real data streams in extracting relevant information to predict the future of the IoT domain. DL is known to be more reliable than traditional learning because it can easily extract information, and hence, provides better accuracy [22]. Due to this, several studies have been focused on using deep learning techniques to provide new solutions tackling two different perspectives of both technical and regulatory, such as anomaly and malware detection; however, the results are still unconvincing. Furthermore, most IDS solutions have been adopted from existing computer networks, wireless sensors networks, and mobile ad hoc networks. Yet, the unique characteristics of IoT-based networks, such as connectivity to the global internet and lightweight resources, make the IDS proposed for these networks not suitable to IoT applications [13,14]. There are only a few surveys that have been found that focus specifically on DL techniques in the IoT domain [23]. To the best of the authors' knowledge, there is no review that is dedicated to investigating the effectiveness of the deep learning-based IDS solutions in the IoT security domain. Therefore, this paper was conducted to bridge this gap and investigate the most effective and efficient use of DL approaches in securing the IoT environment. This review provides an in-depth, focused, and high-quality analysis to orient future research toward finding robust anomaly-based IDS using DL techniques. The paper is organized as follows. The contributions introduced by this study are briefed in Section 2. Related work is presented in Section 3. The review method, which includes the review protocol, planning, research questions, is described in Section 4. Section 5 presents the search strategies, which include the primary records selection, secondary records selection, inclusion criteria, exclusion criteria, quality assessment (QA), data extraction, and synthesis. Section 6 presents the results, studies selection and quality assessment results, and overview of publication sources. Section 7 presents the outcomes, which include the answers to the research questions, taxonomy, analysis and discussion, and the open issues. Section 8 presents the discussion. Section 9 presents the future direction. Limitations of the study are illustrated in Section 10, and the study is concluded in Section 11.