The Effect of Ironic Process Theory on Brain Signal-Based Encryption for IoT Devices

Abstract

Numerous encryption methods have been published to secure IoT devices in the last decade. Existing encryption methods still have disadvantages when it comes to securing IoT devices. On the other hand, a new encryption method using brain signals in IoT devices is gaining attention as a new solution. The encryption method based on brain signals essentially involves a hypothesis called imposed recall based on ironic process theory. The imposed recall was created with the expectation that imposing a specific choice on the subjects during the acquisition of brain signals would allow for better separation of EEG data. This paper presents experiments and approaches to prove the validity of the imposed recall hypothesis. With the experiments, the effects of ironic process theory on brain signal-based encryption can be observed. While performing the tests, varying approaches, including Granger causality, were applied to analyze the results. The results show that the imposed recall hypothesis can successfully reconstruct EEG data. The structured signals were determined to be effective in capturing matches of brain signals on subjects at different time intervals. Thus, the imposed recall hypothesis can be used in various fields, such as authentication, questioning, and identification, by reserving brain signals to be obtained from individuals. In addition, it was reported that it is possible to acquire the ability to provide security in both devices with limited hardware, such as IoT devices or complex systems.