Access Control Based on Log File for Internet of Things Devices
Arwa Aloqbi1, Manal Alshammari2, Amal Alatawi3, Amer Aljaedi4, Adel R. Alharbi5
1Arwa Aloqbi, College of Computing Information Technology University of Tabuk, Tabuk 71491, Saudi Arabia.
2Manal Alshammari, College of Computing Information Technology University of Tabuk, Tabuk 71491, Saudi Arabia.
3Amal Alatawi, College of Computing Information Technology University of Tabuk, Tabuk 71491, Saudi Arabia.
4Amer Aljaedi, College of Computing Information Technology University of Tabuk, Tabuk 71491, Saudi Arabia.
5Adel R. Alharbi, College of Computing Information Technology University of Tabuk, Tabuk 71491, Saudi Arabia.
Manuscript received on 08 June 2022 | Revised Manuscript received on 14 June 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 61-68 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B70940711222 | DOI: 10.35940/ijrte.B7094.0711222
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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 use of Internet of Things devices has lately increased significantly, leading to the management of a diverse set of nodes and a vast number of data. Most Internet of Things nodes have limited resources and are vulnerable to a variety of threats and failures. Therefore, numerous novel techniques have been conducted to secure resource-constrained devices such as access control. In this work, we proposed an access control mechanism by using the user log files when they interact with their Internet of Things devices. Where it is possible to define and enforce access control restrictions and follow logs through log files to monitor the user accessing behaviors. This mechanism can be applied as an extra security layer along with any traditional user authentication access control to have the effective and accurate access control to prevent intrusion reveal information in the Internet of Things devices. To do this, we developed three Internet of Things applications on mobile, table, and website pages with different functionalities and goals to store the user log file features. We collected a large-scale date-set from over a thousand participants. Three machine learning algorithms: J48, Part, and Naive Bayes are applied and compared to predict the legitimate users. Several experiments were performed with significant results.
Keywords: Internet of Things; Access Control; Attribute; Logs File; Machine Learning.
Scope of the Article: Internet of Things