Hybrid Deep Learning with Optimized Hyperparameters Based Intrusion Detection in Internet of Things for Smart Farming

Authors:
Publisher website
Google Scholar
Abstract
In addition to improving the country's economic prosperity, agriculture has significantly increased human life. Because of its rapid expansion, smart agriculture has recently gained popularity. This encompasses all computing technologies, including wireless sensor networks (WSN) and the Internet of Things (IoT). Agricultural regions strategically place IoT sensors to gather vital data about crops and fields, ultimately leading to higher overall production rates. To protect agricultural systems from cyber threats, intrusion detection in IoT-based smart farming uses deep learning (DL). DL systems have the ability to independently identify abnormal or unauthorized actions. This study creates a hybrid DL-enabled intrusion detection with particle swarm hyperparameter optimization (HDLID-PSHO) method that can be used in smart farming that is based on the IoT. The given HDLID-PSHO method carries out both the feature selection procedure and preprocessing. The proposed method uses a hybrid DL and transfer learning classification model to identify intrusion detection attempts. Finally, the Particle Swarm Optimization (PSO) algorithm allows for hyperparameter adjustment. The experimental validation confirmed that the proposed model performed well on ToN-IoT and NSL-KDD datasets.

This publication has 14 references indexed in Scilit: