Title | Optimal Secure Two-Layer IoT Network Design |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Chen, Juntao, Touati, Corinne, Zhu, Quanyan |
Journal | IEEE Transactions on Control of Network Systems |
Volume | 7 |
Pagination | 398–409 |
ISSN | 2325-5870 |
Keywords | Communication networks, connectivity, cyberattack, human factors, Internet of Battlefield Things (IoBT), Internet of Things, IoBT Security, jamming, optimal design, pubcrawl, Resiliency, Resistance, Scalability, security, two-layer networks |
Abstract | With the remarkable growth of the Internet and communication technologies over the past few decades, Internet of Things (IoTs) is enabling the ubiquitous connectivity of heterogeneous physical devices with software, sensors, and actuators. IoT networks are naturally two layers with the cloud and cellular networks coexisting with the underlaid device-to-device communications. The connectivity of IoTs plays an important role in information dissemination for mission-critical and civilian applications. However, IoT communication networks are vulnerable to cyber attacks including the denial-of-service and jamming attacks, resulting in link removals in the IoT network. In this paper, we develop a heterogeneous IoT network design framework in which a network designer can add links to provide additional communication paths between two nodes or secure links against attacks by investing resources. By anticipating the strategic cyber attacks, we characterize the optimal design of the secure IoT network by first providing a lower bound on the number of links a secure network requires for a given budget of protected links, and then developing a method to construct networks that satisfy the heterogeneous network design specifications. Therefore, each layer of the designed heterogeneous IoT network is resistant to a predefined level of malicious attacks with minimum resources. Finally, we provide case studies on the Internet of Battlefield Things to corroborate and illustrate our obtained results. |
DOI | 10.1109/TCNS.2019.2906893 |
Citation Key | chen_optimal_2020 |