An efficient medical image encryption scheme for (WBAN) based on adaptive DNA and modern multi chaotic map

A Wireless Body Area Network (WBAN) is a network of wireless sensor nodes that can be installed inside or outside of the human body. This network is crucial in medical problems. Due to storage, battery power, and computational resource limitations, the security of medical information such as medical photographs or other information is a major concern in (WBAN). This work proposes an image encryption approach that addresses these constraints by utilizing adaptive DNA code bases and a new multi chaotic map architecture. DNA coding is used to enhances computing efficiency and gives great data transfer capabilities and the new multi chaotic map was formed by Combining Henon, Gaussian and Logistic map (HGL) To generate more chaotic pseudo-random sequences. Numerous analyses were conducted to test the proposed scheme, such as brute force, statistical, differential assaults and noise added analysis. Results indicated that this scheme has a strong level of security.


Introduction
The wireless network of the body area is one of most significant emerging health-care technologies. Via it we will keep track of the patient's wellbeing without affecting his daily life. With regard to WBAN, transmission of patient information must be protected without sacrificing the privacy and confidentiality of the data. Due to any change in data, incorrect diagnosis and treatment can result. The wireless body area network faces many challenges such as memory constraint, computing, usability and scalability [14].
DNA cryptography offers many benefits which make it a promising technology for WBAN [2,17] First, it has a large parallelism that helps improve the speed of computing. Second, it has wide capacity to store DNA molecules and low energy consumption. So, DNA coding is a modern uninterrupted data technology and provides high protection in WBAN over conventional coding such as DES and AES [11,16]. And for medical images, several scientists have reported that DNA coding is an effective technique for secure transmission of a medical images [3]. Chaotic encryption technology has characteristics such as vulnerability to initial conditions, pseudo-randomness and non-periodicity, making it commonly used for image encryption [8,22]. Because of the benefits of DNA coding and the chaotic method, researchers use these two approaches in image cryptosystems to create very effective and stable structures that are difficult to penetrate or crack [4,6,20].

Our involvement
In the proposed scheme adaptive DNA coding is used with a modern one-dimensional (HGL) chaotic mapping system that has more features than the traditional chaotic system (Henon map, Gaussian map, logistic map, …) like larger variable range, wide randomization and several chaotic series. So, the chaotic series generated by (HGL) is hard to prophesy. At first, the medical image input is hashed using SHA-256 hash function to obtain the initial chaotic map (HGL) value and get the chaotic series. At second, the image is crossover and adaptive DNA coding is applied on image and chaotic map to get two DNA matrix. Finally, the two DNA matrix is xored using Xor operation to get a new DNA matrix and DNA decoding rules is implemented to obtain cipher image. The results of simulation and the security analysis show that, the algorithm has been shown to exhibit strong encryption effects and can effectively withstand any type of attack. This article is divided as follow. Section 1 describes the proposed scheme's preliminaries. Section 2 discusses the suggested scheme for encryption. Section 3 illustrate experimental and simulation tools. Section 4 display security analysis and result. The conclusions are given in Section 5.

DNA operation
DNA (Deoxyribonucleic acid) is a nucleic acid that holds living organisms' genetic information. In one DNA sequence, there are four distinct nucleic acid bases: adenine -A, thymine -T, cytosine -C, guanine-G, where complementary pairs are C and G, T and A. We can get 24 encoding schemes if 4 bases A, T, C, G are encoded to 00,11,10,01. Due to the complementary connection between DNA bases, eight types of coding groups comply with the basic complementary rule as shown in Table 1, and there are eight types of XOR operation for each DNA base as well. in Table 2, The XOR operation for DNA rule5 [1].

Chaotic map
Chaotic maps are extremely sensitive dynamic systems to initial conditions and the parameters of control. These characteristics can be regarded as similar to the required cryptographic structures. The cryptography schemes based on Chaos utilizing boundary conditions as a key to cryptography. The chaotic map as Logistic map, Gaussian map and Henon map are illustrated as the follows, which are simple and standard dynamic equation of complicated chaotic pattern [19,24].
3 The proposed scheme for encryption The logistic and Gaussian map are merged in this scheme with Henon map as shown in Fig. 1 To obtain a modern one-dimensional chaotic map with a unique design and more variables, parameters and unpredictable attitude. The suggested (HGL) Map's Mathematical structure is defined in Eq. (1).
Where z n ,z nÀ1 is initial value and a, b, α ,r is the parameter of the chaotic system.  Table 2 The XOR operation for

Statistical analysis for (HGL) chaotic system
The randomness of the suggested HGL chaotic map is checked using the NIST test feature consisting of 16 statistical measures. Such measures determine whether or not the sequence generated is random. For such measures, the basic reliance is on the likelihood value (p-value). The value p is compared to the meaning level α which represents the boundary between the region of rejection and non-rejection. The significant level in NIST equals 0.01. It implies that the series is not a random series and deny for p-value less than 0.01 and for p-value more than 0.01, This ensures that the series is random and approved. Table 3 shows the results of the series created by the proposed chaotic map. As a consequence, the suggested sequence of chaotic HST map are totally random and ideal to be used.

Key generation
For this scheme, the hash function of SHA-256 is applied on the input image to obtain 256-bit of secret key K. In that every single bit alteration in the image would produce a totally different hash value. As in Eq. (2), the secret key K is divided into 8-bit blocks, and the primary values are extracted from Eqs. (3), (4). Since the initial values given are Z0 ' and Z1′.

Encryption algorithm
& Step1: image I is hashed to get the initial value of (Henon -Gaussian-Logistic map) according to Section (3.1). & Step2: calculate the value of H i andp i for plain image by using Eqs. (5), (6). Which is used to determine the DNA rule number used for coding. With it, each image is encoded by a different DNA rule than the other images and then a new image V (m, n) is produced using the crossover technique.

Tools
The proposed encryption and decryption scheme are executed on a personal computer using Wolfram (Mathematica11.3) and (MATLAB R2016a) software with Intel ®, core TMi7-8550u, CPU@1.80GH and 8 GB laptop running windows 10. The proposed scheme was conducted on multiple medical images like (MRI, X-Ray, CT, Ultrasound, ECG) images and Lena image with size of 256 × 256 and 512 × 512. Figure 4 display samples of the images tested for encryption

Key space analysis
The key space must be wide enough for a successful encryption scheme to stand up to the brute force attack. In this scheme the key is the control parameter (a, b, α, r), the initial values (Z0′, Z1′), The value of added pixel of image (H i ) and 256 bits for hash value. If computational precision of number equal 10 − 15 . The total key space will be equal 2 128 × 10 105 ≈ 3.4028 × 10 143 .This is greater than the permissible space for the key (2 256 ) for realistic symmetrical AES encryption [15]. So, it's large enough to withstand attack by brute force.

Key sensitivity analysis
Key Sensitivity ensures which only the right key could decode the encrypted image and any effort to decode it for other keys will failure. To evaluate key sensitivity, various key combinations are used to decrypt encrypted images by changing the initial values (Z0′, Z1′) of henon-Gaussian-logistic chaotic map just slightly. As show in Fig. 5, The decryption process by slight change in Z0′and Z1′ does not offer any characteristics of the plain image.

Information entropy analysis
The entropy of the information shall be considered to determine the quantity of randomness in the image. The maximum value of the information entropy for an ideal grayscale image is 8 by Eq. (7). Where m, P (mi) refers to the source of the information and the likelihood of the mi sign. Table 4 demonstrates the entropy results [5].

The histogram analysis
The image histogram shows the spread of pixel value inside the image. against the different intensity levels. The histogram of the encoding image must be uniform enough to be an effective image encoding system. Shows in Fig. 6, the histograms of original and cipher images. The histogram of the images is evidently very different and the histogram of the cipher image is standardized, that allows it to face the statistical assault.

Correlation analysis
This informs how much the relationship is between the similar pixels of the original and the encoded image. Cryptography algorithms should increase the connection between the adjacent pixels in the cipher image. Correlation coefficients for our structure are calculated by Eq. (8) utilizing randomly 4000 pairs of adjacent pixels from both the plain and encrypted image, and the results are shown in Table 6.
The results show that in the original image the correlation coefficients are near to 1while the coefficients of the cipher image are around 0. So that signifies how our scheme has assisted in removing the relationship in the image between neighboring pixels, which enables to avoid statistical attacks as shown in Fig. 6 and Table 5.

Differential attack analysis
The sensitivity of an encrypted image to the few changes in the original image is one way of measuring. The two variables used are NPCR and UACI, which are determined using the  Table 6, we notice that NPCR > 99.6% and UACI > 33.3% which make it able to overcome differential attacks [5].

Peak signal to noise analysis
The PSNR is used in the evaluation of image quality, arithmetically described by the mean square error (MSE) as in the Eq. (10). Table 7 shows the value of MSE and PSNR values using the proposed scheme. Results indicate that the PSNR values between both the encoded and the plain images are low and that the MSE values between both the encoded and the plain images are big. Which indicates that this scheme has a strong performance and greater safety.

Noise added analysis
During transmission over a noisy channel, the encrypted image is exposed to noise. The efficiency of the scheme is determined by the noise resistance and the reliable receiver's capability to identify the image after decoding. By adding Gaussian noise with variance (0.01, 0.1) and pepper and salt noise with density (0.05, 0.5) to the encrypted image, the proposed scheme is checked and then decrypted with appropriate key. Figure 7 displays the encrypted and decoded images.

Complexity
The computational complexity in this scheme shall be determined as the following, for an image with size M   . The computational complexity of the algorithm will also be reduced when the algorithm is run in parallel mode.

Comparison results with others
The performance of the proposed image coding scheme is compared with other approaches using Entropy, UACI, and NPCR tests and the results demonstrate the fact that our algorithm provides better performance in terms of protection compared to the results obtained by other algorithms. As shown in Table 8; The entropy of the proposed Lena image is greater than the entropy in the other references and this proves that the proposed algorithm is robust enough and capable of countering statistical attacks. Table 9 indicates that, relative to the other references, the UACI and NPCR values of the proposed Lena image are good enough and capable of overcoming differential attacks. Table 10 shows that; Medical images tested with the proposed algorithm have good Entropy, UACI and NPCR values compared to other references making them able to withstand statistical and differential attacks. Whereas a new multi-chaotic map was created by combining Henon, Gaussian and Logistic map (HGL) that produce more chaotic pseudo-random series. the security analysis and experimental Simulation Result show that this algorithm has high entropy value, low correlation, low PSNR value and uniform histogram, high level of security, large key space make it capable of resisting all kinds of attacks.
Funding Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
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