Over the decades, protecting the privacy of a health cloud using the design of a fog computing network is a very important field and will be more important in the near future. Current Internet of Things (IoT) research includes security and privacy due to their extreme importance in any growing technology that involves the implementation of cryptographic Internet communications (ICs) for protected IC applications such as fog computing and cloud computing devices. In addition, the implementation of public-key cryptography for IoT-based DNA sequence testing devices requires considerable expertise. Any key can be broken by using a brute-force attack with ample computing power. Therefore, establishing a model of DNA cryptography is extremely necessary to improve the interaction between current and new technologies. In addition, the implementation of public-key cryptography for IoT-based DNA sequence testing devices requires considerable expertise. The proposed algorithm can create a stable hybrid encryption algorithm based on DNA layers and advanced encryption standard (AES) to shorten encryption time and increase protection capacity to suit the IoT health cloud systems. The proposed model can protect the DNA sequence over the fog computing cloud against plain text attacks by generating (I) main key, which is the key to the EAES encryption algorithm; (II) Rule 1 key, which represents the DNA base number of possible key probabilities; and (III) Rule 2 key, which represents the number of binding probabilities of the DNA helical structure. This key is built to achieve higher levels of protection. An ECG encryption enhancement technique with multilayer AES and DNA computing (MLAESDNA) is proposed in this study. Results show that MLAESDNA can secure IoT signals via cloud computing.

Cryptography is the science of securing the content of messages and communications. Cryptanalysis, the other subdiscipline, seeks to compromise or defeat the security achieved by cryptography. Mathematics is the foundation of cryptography and cryptanalysis. Cryptography is commonly associated with encryption, the transformation of data and information into a form that is unusable by a person who is not authorized to access that information. Historically, cryptography was used to protect the confidentiality of sensitive messages for military and diplomatic communications. Based on this traditional definition, cryptography can be seen as the science of encryption and decryption of messages, whose primary concern is to protect a message if it is disclosed to someone other than the intended recipient. With the expansion of information economy where transmission of sensitive information across untrusted media has become prevalent, the use of cryptography has become common practice not only with organizations but also with individuals; the scope of data transmission has exceeded the range of information sharing and entertainment to the core of industrial, scientific, and medical domains [

Recent implementations of cryptography considered cryptography as much more than the acts of encryption and decryption. While encryption and decryption techniques are used to secure sensitive information where confidentiality is important, other aspects of information security are implemented through encryption. These aspects include authentication of the message, sender, and recipient; the integrity of the message; and the nonrepudiation of the message transfer [

The Internet of Things (IoT) has enhanced the collection and sharing of data, and has made it more accessible to software applications and their users based on cloud computing and fog computing [

Fog computing was developed to bridge the gap between IoT devices and data centers. The main purpose of fog computing is to speed up computing processing [

The study aims to build a multilayer reliable system of DNA sequence incorporating DNA computing and the AES algorithm that can be implemented and integrated into the biological environment on DNA computers. This technique can secure the DNA sequence over cloud-based fog computing platforms against plain-text attacks via generation of main key and rule keys. The study introduces several contributions as (i) a multilayer encryption algorithm that incorporates DNA and the AES algorithm, (ii) a reliable encryption technique for IoT-based medical healthcare systems, (iii) an encryption technique with decrement of ECG message length and hence, decrement of complex mathematical operations, and (iv) an encryption technique that improves encryption power and provides higher security and more complexity to multilayer AES and DNA (MLAESDNA).

The remainder of this study is structured as follows. Section 2 displays the current related work. Section 3 offers the indepth process of the suggested model. Section 4 provides the experiment results and their discussion. Section 5 presents the conclusions.

The rapidly growing applications of telemedicine and healthcare recently imposed the need for securing the transmission of medical data and records over the Internet or any other medium. This need motivated researchers to focus on the enhancements and modifications of existing encryption algorithms as well as develop new algorithms, as illustrated in Section 2.5. DNA inspired security encryption algorithm development due to the advanced, reliable method of encryption it is based on. Thus, several attempts have been made to enhance the standard security and encryption algorithms inspired by the DNA method of encryption.

Ref | Existing Methods | Technique | Advantages | Limitations |
---|---|---|---|---|

[ |
Modifying the AES algorithm to be used for image ciphering, especially HD | DNA computing and round-reduced AES block cipher integration | High security level | Not applied to network smart applications |

[ |
Data encryption standard (DES). |
New approach of the AES algorithm |
Dynamic key generation |
Cipher and key overhead |

[ |
== | Deep learning encryption |
Hiding data in a DNA sequence and deep learning | Computation basis is storage capacity required for DNA |

[ |
RSA |
DNA based-bit-based design and implementation of AES |
Building a complex DNA basis system is possible. |
As strong and robust as the standard algorithm |

[ |
== | DNA-based DNAES sequences with silent mutations | Applicable to any type of data |
Same security level as AES |

[ |
== | Altering the AES MixColumns transformation |
Analysis of security new MixColumns |
Same key length of AES |

[ |
DES |
Modified AES algorithm |
Adjustment of ShiftRow Transformation |
Same key length of AES |

[ |
(ECC) |
Hybrid confidentiality algorithm | Strong IoT data confidentiality | Same key length of AES |

The current study focuses on comparing and analyzing the encryption enhancement trials in the steganography sector as reported in the literature published during the previous years. A general trend is to strengthen a new encryption algorithm and counter the great power of computing, especially the new generation of quantum computing device. Traditional cryptographic systems are built on strong mathematical and theoretical bases. Therefore, several researchers are interested in developing a new DNA-based AES encryption algorithm, as mentioned in

The AES encryption algorithm is a common encryption algorithm developed by NIST to replace DES [

SubBytes transformation: AES consists of a 128-bit block of data, which means every entity in the database consists of 16 bytes. Additional byte transformation requires that every entity of a data record is converted into another form of data using an eight-bit Rijndael S-box [

ShiftRows transformation: In this easy transposition, data in the remaining three lines of the state that are dependent on the row position are transformed in one cycle to another location. In the following line, a one-byte circular shift to the left is done. In rows 3 and 4, two- and three-byte circular transformations to the left are executed sequentially [

Mix Columns transformation: This transposition is similar to multiplying the states represented by columns with a matrix [

AddRound Key transformation: The current state and the encryption key are XORed in this transformation. Hence, this transformation is the inverse of its own. The transformation is composed of several steps. The initial step, AddRound Key operation, is performed followed by the processing of data block that consists of SubBytes, ShiftRows via round function, Mix Columns, and AddRound Key transformation [

In Singh [

In the existing research, an AES algorithm for data security is designed to provide more security using a Polybius square matrix, thus increasing the number of rounds. Another work generated the key using chaotic maps where encryption is accomplished using AES [

DNA computing refers to the concept of using biological neurons and molecules, rather than digital computers, to perform complex computations. This area of science was recently explored by an American scientist named Leonard Adelman. His contributions showed how biological molecules can be implemented and studied to solve complex mathematical computations. Initially, no relationship was observed between molecules and cryptography, but excessive research in this area established a new field of science that related the biological molecules and the science of encryption to enhance the features and capabilities of biological molecules for the science of cryptography [

It supports much denser information than traditional computers that require 1,000,000,000,000 cubic nanometers to store storage media, such as videotapes.

The DNA processes operations in parallel using trillions of strands because each operation on a test tube of DNA is carried out on all strands in the tube in parallel.

The linear operation of traditional computers implies that data can be manipulated in one block after another. For example, chemical reactions in biological environments occur in a parallel fashion, and every step composing these reactions influences numerous strands within the DNA sequence. Using the DNA computer for these calculations is much beneficial because it requires less energy and memory space than conventional computers [

Current cryptographic algorithms have a mathematical basis. DNA-inspired algorithms are combinations of current and new cryptographic technology. This section describes in detail MLAESDNA based on data encryption that integrates AES and DNA computing. MLAESDNA aims to enhance security by increasing the key length size using the DNA layers around the AES algorithm, which leads to preventing the piracy of the illegal users.

MLAESDNA, as shown in

DNA encryption is used to increase the key length, which adds more complexity to the AES such that it becomes immune in a manner that adapts the technological development. The key length of the proposed algorithm is (24 × 2128 × 3 × 10) bits and is calculated as follows:

The DNA key size is 24. This key introduces the DNA sequence where the probability of the key could be

The standard AES main key size is 2128.

The key size is 3 according to three different DNA bases which can be represented as one of the following:

The standard AES round number 10.

The steps of the proposed method are explained in the following sections. The steps executed during the operation of the algorithm are outlined, and each following step is essential for the procedure of the algorithm and designed according to the algorithm design considerations to produce better algorithm performance metrics.

This step intends to convert ECG signals into binary bits using MATLAB functions to suit DNA conversion, as illustrated in

DNA encryption starts with transforming the binary message obtained from the previous step through the variable DNA bases into a DNA helix.

Rules | A | T | C | G |
---|---|---|---|---|

Rule 1.1 | 11 | 10 | 01 | 00 |

Rule 1.2 | 11 | 10 | 00 | 01 |

Rule 1.3 | 11 | 00 | 10 | 01 |

Rule 1.4 | 00 | 11 | 10 | 01 |

Rule 1.5 | 00 | 11 | 01 | 10 |

Rule 1.6 | 00 | 01 | 11 | 10 |

Rule 1.7 | 01 | 00 | 11 | 10 |

Rule 1.8 | 01 | 00 | 10 | 11 |

Rule 1.9 | 01 | 10 | 00 | 11 |

Rule 1.10 | 10 | 01 | 00 | 11 |

Rule 1.11 | 10 | 01 | 11 | 00 |

Rule 1.12 | 10 | 11 | 01 | 00 |

Rule 1.13 | 10 | 11 | 00 | 01 |

Rule 1.14 | 10 | 00 | 11 | 01 |

Rule 1.15 | 00 | 10 | 11 | 01 |

Rule 1.16 | 00 | 10 | 01 | 11 |

The SubBytes operation is a nonlinear byte substitution that operates on each byte of the state independently, as shown in

The inverse of SubBytes is the same operation using the inversed S-Box, which is also precalculated, and is a SubBytes step, as shown in

In the SubBytes step, each byte in the state is replaced with its entry in a fixed eight-bit lookup table, S; b (i, j) = S (i, j).

In this operation, each row of the state is cyclically shifted to the left, depending on the row index.

The 1st row is shifted 0 positions to the left.

The 2nd row is shifted 1 position to the left.

The 3rd row is shifted 2 positions to the left.

The 4th row is shifted 3 positions to the left.

The inverse of ShiftRows is the same cyclically shift but to the right. It is needed later for decoding. In the ShiftRows step, bytes in each row of the state are shifted cyclically to the left. The number of places each byte is shifted differs for each row.

In the Mix Columns step, the four bytes of each column of the state are combined using an invertible linear transformation. The Mix Columns function takes four bytes as input and outputs four bytes, where each input byte affects all four output bytes. Together with ShiftRows, Mix Columns provides diffusion in the cipher. During this operation, each column is multiplied by the known matrix that for the 128-bit key is

In this operation, a Round Key is applied to the state by a simple bitwise XOR. The Round Key is derived from the Cipher Key by the means of the key schedule. The Round Key length is equal to the block key length 128 bits.

DNA swapping starts with turning the hexadecimal message obtained from the AddRoundKey step into a binary message. This message is transformed through the variable DNA bases into a DNA helix. Then, it is ciphered through the DNA bases to present a wholly different outcome that is returned into binary text. Once again, the message is transformed into a decimal message.

No. of Rule | DNA sequence |
---|---|

Rule 2.1 | A = T, C = G |

Rule 2.2 | A = C, T = G |

Rule 2.3 | A = G, T = C |

The experimental analysis includes different security tests and results such as keyspace analysis, statistical analysis, numerical analysis, differential analysis, and encryption quality. These tests are the most considerable tests to demonstrate the satisfactory security of the proposed algorithm. The PhysioBank dataset, a large, growing archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community, is used in this study.

The proposed technique was simulated using a reliable simulation tool, namely, “MATLAB version (2017b).”

Specification | Details |
---|---|

Model | Dell Inspiron 5000 series |

CPU | 4 GHz Intel Core i7-5500U |

CPU speed | 3.40 GHz (dual-core, 4 MB cache, up to 3 GHz with Turbo Boost) |

Generation | 8th generation |

Graphics | AMD Radeon R7 M265 |

Memory | 16 GB |

Storage | 2TB HDD |

OS | Microsoft Windows 10 version 1909 build 18363.693 |

Encryption and decryption time can be used to calculate the encryption and decryption throughput of the algorithms. The performance parameters include the time taken by the algorithm for the encryption and decryption of input ECG signals. To avoid biased results, the experiment was run 10 times, and the average of the results was considered the average of the experiment.

ECG Name | AES Only | 2-rounds AES and DNA | 5-rounds AES and DNA | 10-rounds AES and DNA | ||||
---|---|---|---|---|---|---|---|---|

ET (s.) | DT (s.) | ET (s.) | DT (s.) | ET (s.) | DT (s.) | ET (s.) | DT (s.) | |

Mitdb/100 | 25.77 | 50.03 | 2.40 | 3.66 | 5.87 | 8.71 | 11.35 | 17.84 |

Mitdb/105 | 23.14 | 50.70 | 2.41 | 3.80 | 5.98 | 9.28 | 14.03 | 18.54 |

: | : | : | : | : | : | : | : | : |

Mitdb/217 | 24.89 | 53.21 | 2.68 | 4.35 | 6.37 | 10.03 | 12.38 | 17.86 |

Mitdb/219 | 24.27 | 54.30 | 2.50 | 4.31 | 6.10 | 10.06 | 13.61 | 18.67 |

*ET (s.) = Encryption time (second), DT (s.) = Decryption time (second).

A complete investigation was conducted on the security of the proposed encryption technique. Several security analysis methods are used to test a cipher’s resistance to different types of attacks. Keyspace analysis is used to measure the resistance to brute-force attack. Histogram, correlation analysis of the adjacent values, and correlation analysis of the original and encrypted ECG signal are used to measure the resistance to statistical attack. Numerical analysis, for instance entropy, is a measurement of randomness. Mean square error (MSE) is used to evaluate the performance of implemented focus measures to the ECG signal quality.

The proposed algorithm was compared with other works, and the results suggest that the proposed algorithm needs (5.179340 × 1027) years to be broken or hacked.

Technique Name | Key Length (bits) | Keyspace | Amount of Time for Breaking (years) |
---|---|---|---|

AES Original | 128 | 2^{128} |
1.078950 × 10^{25} |

LEA: [ |
128 | 2^{128} |
1.078950 × 10^{25} |

MLAESDNA | 4 × 128 × 3 × 10 | 2^{4} × 2^{128} × 3 × 10 |

The proposed algorithm was applied to various ECG signals.

To test the effectiveness of the cryptosystem, the correlation between two contiguous values was examined in the plain ECG signal and the cipher ECG signal using the following procedure: First, 50 pairs (horizontal, vertical, and diagonal) of adjacent values from the original ECG signal and the encrypted ECG signal were randomly selected. Then, the correlation coefficient of each pair was calculated [

Entropy is one of the most important features that define the level of randomness and uncertainty in an ECG signal and is widely used to measure the uniform distribution of pixel gray-level in the ECG signal. The entropy is close to 8; therefore, the diffusion is good and produces a high disorder at output [

ECG Signal Name | Original ECG Signal | Encrypted ECG Signal | ||
---|---|---|---|---|

AES | MLAESDNA | AES | MLAESDNA | |

Mitdb/100 | 0.00620406 | 0.00620406 | 0.0494641 | 0.0362337 |

Mitdb/105 | 0.0114078 | 0.0114078 | 0.0535951 | 0.0452119 |

: | : | : | : | : |

Mitdb/217 | 0.0114078 | 0.0114078 | 0.0906764 | 0.0452119 |

Mitdb/219 | 0.00620406 | 0.00620406 | 0.0494641 | 0.0362337 |

To measure the encryption strength of the proposed algorithm, several quantitative metrics such as MSE are utilized to estimate the variance between the encrypted ECG signal and the original ECG signal.

ECG Signal Name | AES | MLAESDNA |
---|---|---|

Mitdb/100 | 0 | 0 |

Mitdb/105 | 0 | 0 |

: | : | : |

Mitdb/217 | 0 | 0 |

Mitdb/219 | 0 | 0 |

Security is the major issue of any encryption technique. A good encryption algorithm should encounter most kinds of recognized attacks. Keyspace analysis is used to measure the resistance to brute-force attack. In the proposed algorithm, the keyspace is equal to (24 × 2128 × 3 × 10). This value exceeds the effective key size necessary to ensure computational security against future brute-force attacks. The histograms of the ciphered ECG signals are clearly steady and considerably different from those of the original ECG signals, which means performing statistical cryptanalysis on the ciphered ECG signal is very difficult. The correlation coefficient values indicate that the value distribution of the cipher ECG signals show a wide distortion of the correlation among values. Thus, the value information cannot be obtained from the adjacent values. Moreover, the information entropies of the cipher ECG signals are close to the ideal value, which can verify that the cipher ECG signal of the proposed algorithm has a good randomness. Therefore, the proposed algorithm is strongly resistant to differential attacks. These results are achieved due to the strong process of confusion and diffusion of the proposed algorithm. MSE is used to evaluate the performance of implemented focus measures. Remarkably better results are achieved with the proposed algorithm. The conducted experiments and results of various statistical measures demonstrate the resistance of the proposed algorithm to classical types of attack.

The results of histogram analysis, the correlation among adjacent values, the entropy results, and the MSE results demonstrate that MLAESDNA is resistant to statistical attacks. These results are related to the high sensitivity of the three different keys and the high randomization of DNA computing. Furthermore, this study could make a breakthrough into the era of cryptographic algorithm design and implementation in medical fog-computing-based healthcare applications. The implementation of the proposed technique in other domains may be altered by variable platform architectures. The study is also limited to securing medical messages other than ECG signals whose transmission requirements and metrics may vary. The proposed technique also encountered the generation and processing of four keys offered by DNA rules. These combinations may result in encryption and processing time that may be a critical issue in public health safety and emergency IoT-based applications.

This study presents MLAESDNA, a multilayer encryption algorithm incorporating DNA computing and AES algorithm. Increasing the key length has many advantages in IoT, especially in medical health systems, because it decreases the ECG message length and the complex mathematical operations that use more resources and take a longer time to process. MLAESDNA uses four keys offered by DNA rules, which improves encryption power and provides higher security and more complexity. The required decryption breaking time is remarkably increased more than 48 times of the breaking time using the original algorithm. Combining the concept of AES and DNA computing successfully enhances the encryption/decryption processes. The results show that MLAESDNA is better than the original AES algorithm and other algorithms. The results of the experiments conclude that MLAESDNA provides a high level of security, integrity, efficiency, and robustness. MLAESDNA fulfils the requirements needed to transfer the ECG signals over insecure healthcare system channels. In general, the area of joint encryption is a rich area for research. In the future work, the speed of the encryption and decryption execution time will be enhanced by integrating the quantum computing concept with MLAESDNA, applying parallel processing for MLAESDNA, and applying MLAESDNA on all medical signals in industry.

The authors thank the BIOCORE Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka.