Image Cryptosystem for IoT Devices Using 2-D Zaslavsky Chaotic Map

Abstract

Recently, the proliferation of the Internet of Things (IoT) services and applications has prompted several governments to deploy many cameras in critical sites, such as embassies, military institutions, and ministries. The images must be encrypted to protect data shared between IoT sensors/devices and embedded subsystems. Often, IoT devices are smaller and less powerful. A lot of traditional encryption methods are computationally inefficient. They require many rounds of coding, which takes up much space on a device. Despite this, this paper has developed a lightweight encryption algorithm based on chaos theory suitable for IoT devices with limited processing power. The confusion-diffusion structure is often divided into two independent components in chaotic image cryptosystems. However, it reduces the security of the cryptosystem since the independent design may be subjected to cryptanalysis individually. This paper presents an efficient chaotic image encryption algorithm based on a plaintext-associated approach. It incorporates confusion-diffusion operation to increase encryption reliability. The Zaslavsky map initial values are determined by the values and locations of an input image and random values to resist chosen-plaintext cryptanalysis. Furthermore, the proposed method uses a simultaneous confusion-diffusion process to resist any separate attack. The simulation and experimental analysis demonstrate that the proposed encryption algorithm has 0.3927 seconds as a fast encryption time. Moreover, it provides a high performance compared to several other chaotic-based image cryptosystems. as measured by the histogram, entropy (7.99723), pixel change rate (99.7194), unified average changing intensity (33.4635) within the critical interval, pixel correlation, global and local entropy, CDR (ciphertext difference rate), and gray difference degree (GDD). It also has higher security than other chaoticbased image cryptosystems.

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