The Internet of Things (IoT) is the new paradigm of our times, where digital devices and sensors from across the globe are interconnected with distributed applications and services that impact every area of human activity. With its huge economic impact and its pervasive influence over our lives, IoT is an attractive target for criminals, and cyber security becomes a top priority for the IoT ecosystem. Deep learning may provide a cutting-edge solution for IoT intrusion detection with its data-driven, anomaly-based approach and ability to detect emerging, unknown attacks. With the increase in the number of internets connected devices, security, and privacy concerns are the major obstacles impeding the widespread adoption of the Internet of Things (IoT). Securing IoT has become a huge area of concern for all, including 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 defending the system in an effective manner. Limited research exists on ancient intrusion detection systems suitable for IoT environments. This detection platform provides security as a service and facilitates interoperability between various network communications protocols used in IoT.