Attention segmental recurrent neural network optimised with sheep flock optimisation-based intrusion detection framework for securing IoT

This manuscript proposes an attention segmental recurrent neural network (ASRNN) optimised with sheep flock optimisation based intrusion detection scheme for securing internet of things (IoT) environment. Initially, the data is fed to pre-processing, wherein, the redundancy eradication and missing value replacements are performed by random forest and local least squares (LLS). Afterward, pre-processing data is supplied to the feature selection to select optimal features. The correlation feature selection-based processing of feature selection is done. The selected features are fed to attention segmental recurrent neural network, which categorises the data as normal or anomalies. Finally, sheep flock optimisation (SFO) is considered to optimise the ASRNN. The simulation performance of the proposed technique attains better accuracy 20.56%, 18.67%, 23.77%, 38.45%, 22.75%, 36.45%, higher precision 42.36%, 22.15%, 56.45%, 22.03%, 28.63%, and 21.36% compared with the existing methods.