# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 2 Related Work
To efficiently protect external product packing in IoT against anti-counterfeiting, [PERSON] et al. [44] used fractional-order spatial steganography (FSS) and blind steganalysis. To promote secure data transfer in a smart IoT environment, a security scheme is advocated to employ a combined approach of lightweight cryptography and the variable least significant bit substitution steganography technique to conform to the intrinsic constraints of IoT devices [45]. [PERSON] et al. [46] proposed a hybrid security model for securing the diagnostic text data in medical images. [PERSON] et al. [47] discuss the special characteristics, challenges, and peculiarities of ensuring security in IoT systems. Among these peculiarities are the distributed deployment infrastructure, the interoperability and heterogeneity of devices, and the large traffic volume of IoT elements. The authors state that these specific aspects of IoT play a major role in the increased probability of security attacks in IoT when compared to other systems in a controlled environment with well-defined security policies and tools. Owing to the intrinsic challenges imposed by the IoT characteristics, several proposals are presented in the current literature. Researchers, in [48; 49], have proposed schemes not specifically designed for IoT, but rather for securing data in processing power- and memory size-constrained devices such as mobile phones and embedded devices using simple low-computational complexity cryptography and steganography algorithms. The work presented in [50] attempts to design a security framework for IoT. This framework consists of two algorithms: AES and image steganography. The authors addressed IoT security by proposing a two-tier security scheme. However, they appropriated a classical image steganography algorithm without any adaptation to meet the IoT-specific challenges. Similarly to [50], the authors of [46] proposed an integrated model between a steganography technique and a hybrid encryption scheme to ensure the security of medical data transmission. However, their solution provides for an algorithm to better accommodate IoT security at the cost of the heavy processing required by the presented scheme. The proposals above are not explicitly designed to resolve cybersecurity issues in IoT but are instead designed to accommodate devices with low processing power, memory size, or battery constraints in traditional networks. In particular, none of these techniques have addressed the issue of embedded data distortion during transmission due to the noisy transmission environment. Additionally, these techniques are image-based, leaving IoT audio steganography unexplored. Audio signals contain a high level of redundancy, allowing a high payload capacity with minimum disturbance. The number of voice-enabled IoT devices is immensely increasing as this feature has become a standard in connected devices such as: mobiles, tablets, sensors, wearable devices, and smart speakers. On the contrary to images and video, audio files require lower data rates and energy for transmission, resulting in a shorter processing time, better energy consumption, and fewer storage requirements. This facilitates the accommodation of constrained IoT devices. This work proposes an IoT audio steganography realizing the perceptible importance of audio in the current digital society and the application trends that depend heavily on cheap, low-power, and intelligent signal processing algorithms and systems such as autonomous vehicles, health care, Intelligent Transportation Systems (ITS), smart grid, environment, and smart cities. In addition, IoT cybersecurity includes new requirements and challenges that must be first addressed before the deployment of these devices:Device capabilities: we adopted Fast Fourier Transform (\(FFT\)), which inherits its polynomial complexity \(O(N\log N)\)[51], uses a small FFT size, and hides data through bits replacement, resulting in minimum overheads.