The extensive growth in the Internet of Things (IoT) has been realized in diverse applications such as smart homes, smart cities, Intelligent Transport System (ITS), smart factories and so on. IoT integrates billions of smart devices (predicted to jump from 27 billion in 2017 to 125 billion by 2030) and establishes communication among them. However, this huge connectivity introduces a further need for analysis from the perspective of security. The involvement of millions of things and users brings increasing vulnerability for the IoT environment. On the other hand, Deep Learning (DL) approaches, which come from the family of machine learning (ML), have shown their efficiency in many research fields. Current studies have been shown the effectiveness of DL approaches in IoT security applications. In this paper, we first present a detailed analysis on IoT with its security requirements and challenges. Subsequently, we elaborate on the role of DL approaches in IoT security. We survey the state-of-art research works on securing IoT environments using DL approaches. Brief comparative analysis between DL algorithms, such as RNN, LSTM, CNN, DBN, AEs etc., is also provided. Finally, we highlight research problems presented in current studies and outline the future direction of DL algorithms in the IoT security domain.