# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 2 Related Work  
Several steganography techniques have been developed recently. Some of them adopt data hiding in the time domain [27; 28], frequency domain (e.g., Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)) [6; 29], or  
Figure 2: Cover and stego signal classification using SVM. encoded domain (e.g., AMR, G.723.1, G729) [30; 31]. Most of these techniques are based on least significant bit encoding (LSB). In its naive implementation, the LSB technique could be used as a simplified demonstration of the data hiding process using steganography. Figure 3 shows how we generated a stego signal by replacing the first LSB layer of the cover signal with the digital payload \"2018\". LSB allows the embedding of a high payload capacity with low perceptibility. However, since the embedding is applied in the LSB plane of the cover signal, the secret message could be easily removed by an unauthorized user. To improve the undetectability of secret data, [PERSON]. [32] proposed a framework based on Generative Adversarial Networks (GANs) to implement optimal embedding for audio steganography in the temporal domain, whereas the authors of [33] proposed a generalized joint adaptive intra-frame and adaptive inter-frame steganography method (called AHCM) within compressed audio streaming and implemented an AdaMP3 Stego algorithm in MP3 audio based on the psychoacoustic model. To ensure hidden data robustness against LSB removal and re-sampling attacks, [PERSON]. [34] embedded secret audio into the cover audio by separating the processing of the amplitudes and signs of the secret audio. [PERSON]. [35] combined scrambling and steganography to provide high security for speech hiding in speech. Similarly, [PERSON]. [36] proposed hiding data under the hearing mask in high-frequency samples and [6] proposed a perturbation minimizing algorithm by finding an optimal embedding path and an optimal modification strategy. Discrete wavelet transform and sparse decomposition are used in [37]. There are also several audio steganography tools that are freely available over the Internet, i.e., Xiao Steganography [22], Steghide [23], S-Tools [24] and Camouflage [25]. In audio steganalysis, a second-order difference of pitch delay features was used in [38]. The authors of [39] modeled the deviation between the reference and the input signals by reversible Mel-cepstrum coefficients (R-MFCC), while the authors of [40] presented a steganalysis technique specifically designed for the steganographic method developed by [41]. A more generalized multi-layer architecture for the steganalysis of all mp3 encoders has been presented by [19]. The first layer of the architecture detects the encoder and the second layer performs the steganalysis. To distinguish between the cover and the stego signal, the authors of [20] used the entropy and the energy of the signal as the discriminator features. The authors claim that this combination enhances the performance of the classifier. A steganalysis method for quantization index modulation QIM steganography in a low-bit-rate encoded speech stream such as G.723.1 and G.729 is presented by [42]. The authors  
Figure 3: Data embedding in a signal spectrum using least significant bit technique. used the correlation characteristics of split vector quantization VQ codewords of linear predictive coding filter coefficients, arguing that it leads to a stronger correlation network. They showed that this technique improves the steganalysis results for low-bit-rate encoded speech streams. Although cyber-physical systems (CPSs)/IoT audio steganography techniques are non-existent in the state-of-the-art review, few CPSs/IoT image-based steganography attempts have been made. [PERSON] et al. [43] used mobile edge computing to implement image steganography in IoT, assuming that mobile edge computing fulfills the high computing power, data storage capacity, and bandwidth requirements for the Internet of Things (IoT) through edge servers that process data close to data sources or users.