A Novel Hybrid IoT Based IDS Using Binary Grey Wolf Optimizer (BGWO) and Naive Bayes (NB)

Abstract

One of the main objectives of intelligent environments is to enhance the quality of human life standard in terms of efficiency and comfort. The Internet of Things (IoT) model has newly evolved into the technology for establishing smart environments. IoT refers to physical things or devices which are able to exchange information with other devices. It is used in various fields such as smart home, smart city, industrial control, automobile industry, agriculture, intelligent transportation, home automation and appliances, healthcare, and many other fields. Moreover, it assures innovative business paradigms and advanced user experience. Privacy and security are counted as the key problems in any real-world intelligent environment for the IoT paradigm. Therefore, to implement the security of the IoT systems is becoming the first priority and big area of interest in the successful distribution of IoT networks. The open holes of security in IoT related systems create security risks that impact the smart applications. Mirai botnet is an example of one of the novel attacks that launched recently. The network of IoT is protected with authentication and encryption, but it can’t be mitigated against malicious and harmful attacks. Thus, IoT based Intrusion Detection System (IDS) is required to detect the attacks. In this paper, a novel hybrid IoT based IDS using Binary Grey wolf optimizer (BGWO) and Naive Bayes (NB) is presented to defend and secure intrusions on the IoT network. BGWO is used as feature selection and NB as a classification method. The results are compared with other optimization algorithms. The BoT-IoT data set is used as an experimental data set.