# Securing IoT Data Using Steganography: A Practical Implementation Approach
###### Abstract  
Adding network connectivity to any \"thing\" can certainly provide great value, but it also brings along potential cybersecurity risks. To fully benefit from the Internet of Things \"IoT\" system's capabilities, the validity and accuracy of transmitted data should be ensured. Due to the constrained environment of IoT devices, practical security implementation presents a great challenge. In this paper, we present a noise-resilient, low-overhead, lightweight steganography solution adequate for use in the IoT environment. The accuracy of hidden data is tested against corruption using multiple modulations and coding schemes (_MCSs_). Additive white Gaussian noise (_AWGN_) is added to the modulated data to simulate the noisy channel as well as several wireless technologies such as cellular, WiFi, and vehicular communications that are used between communicating IoT devices. The presented scheme is capable of hiding a high payload in audio signals (e.g., speech and music) with a low bit error rate (_BER_), high undetectability, low complexity, and low perceptibility. The proposed algorithm is evaluated using well-established performance evaluation techniques and has been demonstrated to be a practical candidate for the mass deployment of IoT devices. IoT cybersecurity; data protection; signal modulation; information hiding; digital audio; audio steganography +
Footnote †: journal: _Electronica_  
of steganographic systems is to provide a secure, undetectable, and imperceptible way to conceal a high rate of data in a digital medium. It is used under the assumption that it will not be detected if no one is attempting to uncover it. Steganography techniques manipulate the characteristics of digital media files and use them as carriers (covers) to hide secret information (payload). Covers can be images [4; 5], audio [6; 7; 8; 9; 10], videos [11] and text [12; 13]. Protocols [14; 15] and storage devices [16] can also be used as carriers for hidden data. The payload is hidden in any type of digital cover using a key to secure the data and produce a stego file. Figure 1 illustrates a brief comparison between these two techniques. Steganography applications are not limited to data protection. They have also been used maliciously by criminals, hackers, terrorists, and spies [17; 18]. To defeat malicious interventions when communicating secretly, steganalysis techniques have been actively researched to counter steganography algorithms [19; 20]. Steganalysis aims essentially to detect the existence of the payload and does not necessarily consider its successful extraction. Steganalysis techniques have a dual role: (1) they are regarded as attacks to break steganographic algorithms and (2) used to measure the strength of steganographic algorithms. Steganalysis attempts to detect or destroy the payload using audio/image processing and statistical analysis approaches. The work of a steganalyst can be very challenging, especially when the only information available is the stego file (blind steganalysis). Most of the steganalysis techniques presented lately are based on learning to differentiate between the cover- and the stego-audio signals. The learning process is performed by machine learning, for example, by using a support vector machine (SVM), [21] on a dataset fed with a set of features extracted from the cover and stego signals. A decision is then made on whether the tested signal is a cover or a stego file (Figure 2). Well-selected features will strengthen the discriminatory power between the cover- and stego-audio signals. The features are intended to capture the differences between the cover and stego signals due to data embedding. In blind steganalysis, the only available information is the received signal. In this case, a reference signal is created to provide an estimate of the cover signal. Figure 1: Steganography versus cryptography. The reference signal could be created by applying a de-noising method to the input signal or by applying second steganography using steganographic tools [22; 23; 24; 25].