A Study of Soft Computing Based IoT Device Security System
Santhosh1, K. Thinakaran2
1Santhosh, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, (Tamil Nadu), India.
2K. Thinakaran, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, (Tamil Nadu), India.
Manuscript received on August 20, 2020. | Revised Manuscript received on September 05, 2020. | Manuscript published on September 10, 2020. | PP: 5-10 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.D1799029420 | DOI: 10.35940/ijitee.D1799.0991120
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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: The ubiquitous computing environment has increased interest in IoT technology. As IoT has open characteristics in the fields of industry, increased accessibility has raised the possibility of threats. As the IoT network was small on scale, there was risk of security. IoT development brought the network environment by combining networks, therefore risk of security attack compared to small network. The response time while operating IoT devices to detect intrusion through hacking, the artificial neural network responses using mobile devices. This process help to deal with hacking. By detecting virus in real time, this process help to prevent intrusion. As IoT security risks, we suggested an intrusion detection system using artificial neural network model in this study. The system which is developed in this can be adjusted to fit situations of IoT by facilitating modification of critical values. The research which detects anomaly through the response to be used for information security system which utilize IoT.
Keywords: Anomaly, Intrusion Detection, Artificial Neural Network, Information System, IoT, Security System.