An Enhanced Approach for Intrusion Detection Modeling to Secure IoT Network by Using Big Data Analytics

Since 1980, the arrival of the internet has made fabulous changes, and presently, the Internet of Things (IoT) is having the same track. IoT becomes more attractive due to its potential, but on the other hand, the IoT network is targeted to be demolished. IoT networks are always under the risk of denial of services (DoSs) attacks, which have shocking significance. Under this situation, the need for cybersecurity actions like intrusion detection systems (IDSs) are very much essential. The scope of this article is to propose an IDS for big data architecture. The IoT dataset (BoT-IoT) was used with libraries of Apache Spark, and experimental work was evaluated on the F1 measure. The dataset was divided into few parts; the partial part of dataset was examined by random forest for binary classification resulted in 98.6% F1 measure. Main categorical phase was resulted in 98.4% and subcategory classification resulted in 84.1% F1 measure. For overall classification of dataset decision tree resulted in 96.4 for binary classification, 78.6 for major category and 74% for categorical classification.