DCCGAN based intrusion detection for detecting security threats in IoT

Internet of things (IoT) consists of wired/wireless network, sensor, and actuator, where security is more important when more devices are connected to IoT. To increase more security in IoT devices, this manuscript proposes a dual-channel capsule generation adversarial network (DCCGAN) espoused intrusion detection scheme for detecting security threats in IoT network (DCCGAN-IDF-DST-IoT). Data are collected from MQTT-IoT-IDS2020 dataset and Bot-IoT dataset. Then, the data are fed to local least squares, which eradicate the redundancy and replace the missing value. The pre-processed dataset is supplied to fertile field optimisation algorithm (FFOA), which selects the relevant features. Then DCCGAN is used for classifying the data as normal or anomalous. The proposed technique is activated in Python language. The performance of proposed technique for MQTT-IoT-IDS2020 dataset attains 16.55%, 21.37%, 32.99%, 27.66%, 26.45%, 21.47% and 22.86% higher accuracy compared with the existing methods.