Cyber Attack Detection in Smart Agriculture Data Using Machine Learning Approaches

Abstract

A significant amount of potentially sensitive data may be leaked from sensors put everywhere in the internet in an Internet of Things(IoT) environment, It is crucial to first confirm the data source IOT system is IoT device identification environment. It is crucial to first confirm the data's source and identity in order to assure the veracity of such sensitive material. Practically speaking, the first step to a secure IoT system is IoT device identification. In critical or emergency scenarios, harmful behaviors like providing erroneous data that cause irreparable security issues can be stopped by using the right device identification approach. According to recent study, because of their instability or accessibility, primary identity metrics like Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient. As a result, it is crucial to take into account how to analyze sensor and packet header information to identify an IoT device. This article suggests a framework for device identification based on classification using combination of sensor measurement and a packet header data set. In order to provide improved security in IoT devices, various machine learning methods have been implemented to identify anomaly. Data gathered from IoT devices has been used to test the suggested technique under attack conditions.