Monitoring the security of audio biomedical signals communications in wearable IoT healthcare

The COVID-19 pandemic has imposed new challenges on the healthcare industry as hospital staff are exposed to a massive coronavirus load when registering new patients, taking temperatures, and providing care. The Ebola epidemic of 2014 is another example of a pandemic which a hospital in New York decided to use an audio-based communication system to protect nurses. This idea quickly turned into an Internet of Things (IoT) healthcare solution to help to communicate with patients remotely. However, it has grabbed the attention of criminals who use this medium as a cover for secret communication. The merging of signal processing and machine-learning techniques has led to the development of steganalyzers with very higher efficiencies, but since the statistical properties of normal audio files differ from those of purely speech audio files, the current steganalysis practices are not efficient enough for this type of content. This research considers the Percent of Equal Adjacent Samples (PEAS) feature for speech steganalysis. This feature efficiently discriminates the least significant bit stego speech samples from clean ones with a single analysis dimension. A sensitivity of 99.82% was achieved for the steg-analysis of 50% embedded stego instances using a classifier based on the Gaussian membership function.

Automated summary

The COVID-19 pandemic presents new challenges for the healthcare industry, particularly regarding the exposure of hospital staff to the virus. In response, a hospital in New York implemented an audio-based communication system to protect nurses during the 2014 Ebola epidemic, which later evolved into an IoT healthcare solution for remote patient communication. However, this technology has also attracted criminals who use it for covert communication. Current steganalysis practices are not efficient enough for speech content, but this research proposes a new feature, PEAS, which can effectively discriminate between stego speech samples and clean ones with high sensitivity.