Open AccessTowards Improved Random Forest based Feature Selection for Intrusion Detection in Smart IOT Environment
Author(s) -
Mr.B. Suresh,
Dr.M. Venkatachalam,
Dr.M. Saroja
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f1446.0981119
Subject(s) - computer science , feature selection , intrusion detection system , random forest , denial of service attack , machine learning , identification (biology) , data mining , artificial intelligence , benchmark (surveying) , naive bayes classifier , feature (linguistics) , feature extraction , unavailability , support vector machine , the internet , engineering , linguistics , philosophy , botany , geodesy , biology , world wide web , geography , reliability engineering
Internet of Things (IoT) is raised as most adaptive technologies for the end users in past few years. Indeed of being popular, security in IoT turned out to be a crucial research challenge and a sensible topic which is discussed very often. Denial of Service (DoS) attack is encountered in IoT sensor networks by perpetrators with numerous compromised nodes to flood certain targeted IoT device and thus resulting in vulnerability or service unavailability. Features that are encountered from the malicious node can be utilized effectually to recognize recurring patterns or attack signature of network based or host based attacks. Henceforth, feature extraction using machine learning approaches for modelling of Intrusion detection system (IDS) have been cast off for identification of threats in IoT devices. In this investigation, Kaggle dataset is measured as benchmark dataset for detecting intrusion is considered initially. These dataset includes 41 essential attributes for intrusion identification. Next, selection of features for classifiers is done with an improved Weighted Random Forest Information extraction (IW-RFI). This proposed WRFI approach evaluates the mutual information amongst the attributes of features and select the optimal features for further computation. This work primarily concentrates on feature selection as effectual feature selection leads to effectual classification. Finally, performance metrics like accuracy, sensitivity, specificity is computed for determining enhanced feature selection. The anticipated model is simulated in MATLAB environment, which outperforms than the existing approaches. This model shows better trade off in contrary to prevailing approaches in terms of accurate detection of threats in IoT devices and offers better transmission over those networks.