Efficient adaptive framework for securing the Internet of Things devices

Abstract The research on the Internet of Things (IoT) has made huge strides forward in the past couple of years. IoT has its applications in almost every walk of life, and it is being regarded as the next big thing that can change the way humans perceive about their daily life. Smart IoT devices of heterogeneous nature make an essential part of modern day IoT-based systems. The security of these devices is of paramount importance as they handle an enormous amount of critical data and its breach can lead to potentially life-threatening situations. To secure the IoT devices of heterogeneous nature, we formulated a weighted optimization problem in this work. The objective function of this problem is to secure the IoT devices while finding the best trade-off between their resource usage and throughput. To achieve the objective, we consider a pool of five different implementations of Advanced Encryption Standard (AES) cryptographic schemes that offer varied resources and throughput numbers. These implementation schemes are mapped to IoT devices of heterogeneous nature. The mapping is performed through a novel adaptive framework that can consider different weights for resources and throughput to eventually find the best trade-off between the resources and throughput of an IoT-based system. This framework considers the resource and throughput requirements of different IoT devices and uses the Hungarian algorithm to adaptively map different AES implementations on them. Extensive experimentation is performed where the best trade-off is found through varying resource and throughput weight combinations. The comparison of the proposed framework with random and greedy approaches is also performed. Comparison results show that the proposed framework adaptively secures the IoT-based system while providing better resource usage and throughput results. The proposed framework provides, on average, 11% and 17% better throughput and 3% and 13% better resource usage results as compared to random and greedy approach, respectively.

Abstract licence: CC BY-NC-ND

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