ESREHO-MaxNet: Deep Maxout Network For Intrusion Detection And Attack Mitigation In Iot With Wrapper Based Feature Selection Approach

ESREHO-MaxNet: Deep Maxout Network For Intrusion Detection And Attack Mitigation In Iot With Wrapper Based Feature Selection Approach

Mali Shrikant Deelip, Govinda K.
Copyright: © 2022 |Pages: 26
DOI: 10.4018/IJSIR.304901
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Abstract

An effective intrusion detection method is developed using proposed ESREHO-based Deep Maxout network in the IoT environment. The plant images are captured by the sensor node and are routed to the sink node through CH that is selected by the method named Exponential SFO. The routed data is received at BS, where the intrusion detection strategy is done by undergoing the feature extraction, feature selection and intrusion detection phase. The log file data generated from the predicted data is fed to feature extraction phase, where the Bot-IoT features are acquired and then the unique features are optimally selected with wrapper model. The Deep Maxout network is employed to detect the intrusions from the data and if the detected user is considered as attacker then attack mitigation process can be done by reducing the data rate of packets. However, the proposed method achieves better performance with the measures of accuracy, TPR, energy, and throughput with the values of 0.9418, 0.942, 1.8004J, and 7662438bps for without attack.
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1. Introduction

IoT has increased more attention, as it can help user to monitor and store the data in large sized environment. In the developing technology, to increase the production of agriculture in the country helps to grow their economic conditions. However, cause of agriculture loss is mainly due to the factors, such as disease, pests and climate change occurs in different seasons. To develop a user friendly environment with the IoT devices helps the system to increase the production of agriculture. Accordingly, the IoT platform connects farmers with different range of crops and ensure in easier farming. In the recent decades, IoT has gained more development in the field of agriculture. The IoT device helps the system to access the information remotely in such a way that it allows to saves the energy. In the past decades, the IoT system is commonly used for urban area and hence deploying the IoT system in the agriculture field helps to increase the communication between rural communities. The usage of number of sensor devices in the network area to acquire status of plants makes the agriculture field more robust and transparent and it further helps to gain more knowledge about the plants. One of a major recent growth in IoT framework is designing a detection method with IoT devices in agriculture to increase the growth of agro industry. As farm is dispersed in large sized domain, detecting the disease affected plants results a complex task for the government officials and farmers. Most of emerging nations highly preferred IoT enabled farming, as it speedily find the disease of plants and stimulate growth of the plants (Mishra et al., 2021).

In general, the plant diseases are noted in leaves, hence locating the sensor devices in the agriculture farm may help to detect the category of disease infected plants. Accordingly, proper detection of plant disease ensure environment to reduce crop loss. To make the IoT environment to be more effective for plant recognition, sensor devices are required to be located over various lands at different ranges. The camera placed at the IoT devices are used to capture the plant images and these images are forwarded to skin node. The disease detection with IoT environment significantly increase the production of crop as it alert farmer clearly before causing disease damage to plants. Accordingly, early detection of disease may subject to some common issues (Mishra et al., 2021). Various studies and technologies are designed to offer smart agriculture system to agro society and increase quality and quality of productivity, such as yield production, drone patrolling in farmland, crop monitoring, and destroy insects pasts, and so on. When considering the rice cultivation, detection of disease is considered as a significant step in the Asian countries. Hence, investing on the plant disease management plays an important role for monitoring and the diagnosing the disease in the case of rice disease plants. Some of the symptoms that commonly exist in different parts of plants are leaves, fruits and stems. In general, these symptoms are detected only based on the observation (Kitpo & Inoue, 2018).

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