ARCA-IoT: An Attack-Resilient Cloud-Assisted IoT System

IEEE Access

Mian Ahmad Jan¹  Fahim Arif²  Muhammad Babar²  Hammad Afzal²  Sabeen Javaid²  Zhiyuan Tan³ 

[1] Abdul Wali Khan University Mardan, Mardan, Pakistan;Department of Computer Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan;School of Computing, Edinburgh Napier University, Edinburgh, U.K.;

关键词: Cloud;    context;    IoT;    QoS;    service;    social;   

DOI  :  10.1109/ACCESS.2019.2897095

来源: DOAJ

【 摘 要 】

Putting trust in the world of the Internet of Things, where served and serving entities are often unknown, is very hard especially when personal and business information is often being exchanged for providing and consuming services. Moreover, the issues of interoperability and scalability of billions of heterogeneous things in the IoT systems require more attention. A user-centric model of complex interconnected things must be designed in a way that not only makes things trustworthy for common people but it also provides the solution for interoperability and scalability. ARCA-IoT is such a system which not only identifies the attributes (including quality of service) essential for trust but also presents a user-centric model that is robust enough to tackle the attacks made by dishonest entities to manipulate the trustworthiness. For scalability and interoperability, a cloud-assisted environment is introduced in the ARCA-IoT. An intuitive Naive Bayes approach is used to train the ARCA-IoT in a way that it calculates the probabilities of the trustworthiness of the entities and then identifies various types of attacks with the support of three proposed algorithms. The approach is validated with a specifically designed simulated environment. Based on our simulation results, the ARCA-IoT demonstrates the effectiveness in term of performance metrics such as accuracy, sensitivity, specificity, and precision. Besides this, the system outperforms the existing related approaches in terms of a qualitative analysis based on different parametric metrics such as interoperability, scalability, context-awareness, and a human-like decision.

【 授权许可】

Unknown