Font Attributes based Text Steganographic algorithm (FATS) for communicating images: A nuclear power plant perspective

Abstract

Today even sensitive organizations like nuclear power plants are connected to the internet. As information security is of utmost priority to such organizations, achieving the same is a challenging task while using the public network like the internet. Though cryptography could be used to enhance the information security, it cannot hide its own presence from the attackers. This drawback lead security professionals to look for an alternate technique like steganography. Steganography hides the secret information, stealthily, inside another innocent looking cover medium like text, image, audio and video. Considering these, this paper proposes a novel text steganographic algorithm that embeds images as codes in the font attributes such as color, character spacing and kerning. The procedure of representing an image as codes makes the proposed method independent from the resolution of the image. Experiments were conducted by embedding diverse categories of images. A comparison with the best existing method depicted that the embedding capacity attained by the proposed method is higher at 1.59-bits/character, which suits best for low bandwidth environments. Various security aspects of the proposed method have also been discussed.

Information security governance in the electricity industry

“…Thus, IS governance is present in the corporate environment of large and medium companies, and this theme is becoming consolidated in the market, but information security focused on operational availability of an electricity generation plant is something really difficult to see nowadays (Krishnan et al, 2017).…”

“…Only 17.3% of respondents answered that the level of utilization was low or very low, leaving a good percent of organizations with high technological resources. Since technology influences performance and particularly information security, in this question, its importance in the literature was identified based on Machado et al (2016), Krishnan et al (2017).…”