**5.2 Security**

The following parameters were considered in the comparative analysis: ECC with AES, test samples [7].


*A Survey of Lightweight Image Encryption for IoT DOI: http://dx.doi.org/10.5772/intechopen.104431*


#### **Table 2.**

*Metric measures.*


#### **Table 3.**

*Comparison of the PSNR values.*

The PSNR, NPCR, UACI, and Mean Squared Error (MSE) metrics for Mona Lisa and Egg image are shown in **Table 2**.

Based on the test image in **Figure 7a**, **Table 3** offers the PSNR values for the encrypted images. Given the test image in **Figure 7a**, it is apparent that encryption using slightly different secret keys results in different Salsa Dance or AES cipher images. Salsa Dance, however, generates more dissimilar cipher-images than selective/full AES although the secret key is only changed by one bit. Thus, the method is highly sensitive to changes in the key, making the adversary's analysis of Salsa Dance even harder in terms of finding any relationship between the keys used.

#### **5.3 Discussion and comparative analysis**

According to the first paper [8], Compression of the image uses techniques that use less space to provide the same information, which solves the computation and high protection problem. The result is a low bandwidth, reduced storage space, and shortened computation times due to the compression.

According to the second paper [9], This paper describes a technical solution for meeting the confidentiality requirements associated with texture images that overcome the limitations of current techniques, in addition, large data volumes and high application requirements, including real-time performance, complexity, and security, are common.

According to the third paper [10], to reduce resource consumption, throughput, increase processing speed and reduce complexity, the PLIE method is an excellent choice for image encryption on mobile devices, It has been shown by a variety of

#### *Lightweight Cryptographic Techniques and Cybersecurity Approaches*


#### **Figure 6.**

*Sample input and output for hybrid algorithms [8].*

#### **Figure 7.**

*Encryption results of a sample texture image: (a) original image, and (b) encrypted [9].*

performance measurements to maintain privacy for users in mobile and to reduce encryption time by nearly 50% compared to existing methods such as AES.

For the study, input and output images include Mona Lisa (Grayscale 256 \* 256 Pixels), Mona Lisa (Colored 256 \* 256 Pixels), and Eggs (Grayscale 256 \* 256 Pixels). Representative input and output images, with encryption and decryption algorithms, are provided [8]. **Figure 6** shows Sample input and output for hybrid algorithms.

An example texture image and its encryption results are shown in **Figure 7**. Salsa Dance seems to disrupt the correlation between entries of the image while both full and selective encryption using AES fail to destroy the coarse pattern.

## **6. Conclusion**

Nowadays, all smartphones, laptops, and other communication devices connect to the cloud, making data accessible to everyone. IoT network is a group of various devices interconnected over the internet that exchange data between themselves and other services. It has a wide application range from smart applications to a variety of industrial applications. Encryption is one of the best techniques to guarantee end-toend security in the IoT network, as the volume of data transferred is so high. Because nodes in an IoT network have limited resources, classical cryptography methods are costly and inefficient, so lightweight block ciphers are one of the most sophisticated
