Deep Learning based Intrusion Detection System for Internet of Things
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With the increase in number of Internet connected devices, security and privacy concerns are the major obstacles impeding the widespread adoption of Internet of Things (IoT). Securing IoT has become a huge area of concern for all, including the consumers, organizations as well as the government. While attacks on any system cannot be fully prevented forever, real-time detection of the attacks are critical to defend the systems in an effective manner. Limited research exists on efficient intrusion detection systems suitable for IoT environment. In this thesis, we propose a novel intrusion detection system that uses machine learning algorithms to detect security anomalies in IoT networks. This detection platform provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We provide a framework of the proposed system and discuss the intrusion detection process in detail. The proposed intrusion detection system is evaluated using both, real network traces for providing a proof-of-concept, and on simulation for providing evidence of its scalability. Our results confirm that the proposed intrusion detection system is capable of detecting real-world intrusions effectively.