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The Study of Genetic Type Steganographic Models to Increase Noise Immunity of IoT Systems

The Study of Genetic Type Steganographic Models to Increase Noise Immunity of IoT Systems

Dmitry S. Zaichenko, Irina S. Sineva
Copyright: © 2021 |Pages: 16
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
DOI: 10.4018/978-1-7998-8048-6.ch066
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MLA

Zaichenko, Dmitry S., and Irina S. Sineva. "The Study of Genetic Type Steganographic Models to Increase Noise Immunity of IoT Systems." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, IGI Global, 2021, pp. 1414-1429. https://doi.org/10.4018/978-1-7998-8048-6.ch066

APA

Zaichenko, D. S. & Sineva, I. S. (2021). The Study of Genetic Type Steganographic Models to Increase Noise Immunity of IoT Systems. In I. Management Association (Ed.), Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (pp. 1414-1429). IGI Global. https://doi.org/10.4018/978-1-7998-8048-6.ch066

Chicago

Zaichenko, Dmitry S., and Irina S. Sineva. "The Study of Genetic Type Steganographic Models to Increase Noise Immunity of IoT Systems." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, 1414-1429. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8048-6.ch066

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Abstract

Research and development in the field of the Internet of Things, or more generally M2M systems security, is the subject of daily discussion in the ICT market. With the rapid development of intelligent devices, the necessity of valuable information protection has generated many new methods and technologies. Stegoimages, along with genetic algorithms (GA), are a relatively new object in the field of information hiding. The assumption that their application can significantly improve the noise-resistant properties of stegofiles is justified by the properties of the GA, but it is a subject for detailed study, since in such an application the GA has not yet been considered. The proposed method is based on genetic coding that hides messages between Internet of Things devices and is capable of detecting both internal and external attacks in the intellectual infrastructure. A sufficiently high efficiency of preliminary GA coding is shown for objects such as hiding graphic information in a graphic stegocontainer.

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