Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN

On the internet, various devices that are connected to the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) share the resources that they have in
accordance with their respective needs. The information gathered from these Internet of Things devices was preserved in the cloud. The problem of latency is made
significantly worse by the proliferation of Internet of Things devices and the accessing of real-time data. In order to solve this issue, the fog layer, which was previously an
adjunct layer between the cloud layer and the user, is now being utilised. As the data could be retrieved from the fog layer even if it was close to the edge of the network, it
made the experience more convenient for the user. The lack of security in the fog layer is going to be an issue. The simple access to sources provided by the fog layer
architecture makes it vulnerable to a great number of assaults. Consequently, the purpose of this work is to build a seagull optimization-based feature selection approach
with optimum extreme learning machine (SGOFS-OELM) for the purpose of intrusion detection in a fog-enabled WSN. The identification of intrusions in the fog-enabled
WSN is the primary focus of the SGOFS-OELM approach that has been presented here. The given SGOFS-OELM strategy is designed to accomplish this goal by designing
the SGOFS approach to choose the best possible subset of attributes. In this work, the ELM classification model is applied for the purpose of intrusion detection. In conclusion,
the political optimizer (PO) is utilised in order to accomplish automatic parameter adjustment of the ELM technique, which ultimately leads to enhanced classification
performance. In order to demonstrate the usefulness of the SGOFS-OELM approach, a number of simulations were carried out. As compared to the other benchmark models
that were employed for this research, the suggested SGOFS-OELM models give the best accuracy, which is 99.97 percent. The simulation research demonstrates that the
SGOFS-OELM approach has the potential to deliver a good performance in the intrusion detection process.