Towards Improved Random Forest based Feature Selection for Intrusion Detection in Smart IOT Environment
Suresh B1, Venkatachalam M2, Saroja M3

1Mr.B.Suresh, Research Scholar, Department of Electronics, Erode Arts and Science College (Autonomous), Erode, Tamilnadu, India and Assistant Professor, Department of Electronics and Communication Systems, VLB Janakiammal College of Arts and Science College (Autonomous), Coimbatore, Tamilnadu, India. 
2Dr.M.Venkatachalam, Associate Professor and Head, Department of Electronics, Erode Arts and Science College (Autonomous), Erode, Tamilnadu, India.
3Dr.M.Saroja, Associate Professor, Department of Electronics, Erode Arts and Science College (Autonomous), Erode, Tamilnadu, India.

Manuscript received on 23 August 2019. | Revised Manuscript received on 15 September 2019. | Manuscript published on 30 September 2019. | PP: 749-757 | Volume-8 Issue-11, September 2019. | Retrieval Number: K14460981119/2019©BEIESP | DOI: 10.35940/ijitee.F1446.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Internet of Things; DoS attacks; Security; feature selection; improved weighted random forest information.
Scope of the Article: