A Novel Hybrid Encryption Method Based on Honey Encryption and Advanced DNA Encoding Scheme in Key Generation

Nowadays, increased information capacity and transmission processes make information security a difficult problem. As a result, most researchers em-ploy encryption and decryption algorithms to enhance information security domains. As it progresses, new encryption methods are being used for information security. In this paper, a hybrid encryption algorithm that combines the honey encryption algorithm and an advanced DNA encoding scheme in key generation is presented. Deoxyribonucleic Acid (DNA) achieves maximal protection and powerful security with high capacity and low modification rate, it is currently being investigated as a potential carrier for information security. Honey Encryption (HE) is an important encryption method for security systems and can strongly prevent brute force attacks. However, the traditional honeyword encryption has a message space limitation problem in the message distribution process. Therefore, we use an improved honey encryption algorithm in our proposed system. By combining the benefits of the DNA-based encoding algorithm with the improved Honey encryption algorithm, a new hybrid method is created in the proposed system. In this paper, five different lookup tables are created in the DNA encoding scheme in key generation. The improved Honey encryption algorithm based on the DNA encoding scheme in key generation is discussed in detail. The passwords are generated as the keys by using the DNA methods based on five different lookup tables, and the disease names are the input messages that are encoded by using the honey encryption process. This hybrid method can reduce the storage overhead problem in the DNA method by applying the five different lookup tables and can reduce time complexity in the existing honey encryption process.


Introduction
Using cryptographic methods, valuable information is kept secure. By using such methods, the sender can securely transmit and store sensitive information over the internet. When information is encrypted using a cryptographic method, the resultant output may be unintelligible to an outsider without access to the key. Knowing the key is a crucial component of the encryption and decryption processes [1]. Nowadays, most web applications generate the key from the user's entered password because it can be easily remembered by the users. Therefore, it can become the main weakness of the message transmission process because security flaws demonstrated that many people all across the world utilize quick-toremember passwords. The majority of people just choose one simple password that they use for all websites, writing it down for future use, making it more vulnerable to attacks.
The honey encryption is the data protection algorithm for protecting the brute force attack and it can deceive the attackers. If an attacker attempts to decrypt plaintext with the incorrect key or honeywords, a user data protection mechanism known as honey encryption (HE) can produce valid-looking plaintext and can effectively fool unauthorized users. If an attacker attempts to decrypt with any of a large number of invalid passwords, the honey encryption process generates a honey message. If not, the HE procedure generates the right ciphertext.
Every incorrect password guess made by a hacker is made into a perplexing dead end by HE [2].
The Distribution Transforming Encoder is a cryptographic primitive used in the creation of the improved HE processes. The DTE is a collection of encoding and decoding operations, each of which produces a value in the seed space S of n-bit strings as an output and accepts a space of plaintext messages as input (M).
Values in the seed space S of n-bit strings are converted into plaintext during the decoding phase. In order to determine the appropriate message ratio, the DTE method in the improved HE algorithm uses the probability distribution function [3].
The example of an improved enhanced honey encryption process is shown in Figure 1. The improved honey encryption process mainly consisted of two main parts: the message distribution process and the passwords or key distribution process. The key distribution process used the simple hashing and salting algorithm for encoding the passwords [3]. In our proposed system, we use DNA encoding in the password encoding process to reduce the time complexity problem and improve the security level of the key process. However, the existing DNA encryption has complex steps and memory overhead problems. Therefore, we proposed an advanced DNA encoding scheme that uses five different lookup  . Honey encryption process using hashing algorithm [3].
tables for the key generation process in an improved honey encryption algorithm.

Background Theory
Information security plays a very important role in every aspect of life. It provides more security for information that cannot be hacked by anyone but the sender and receiver. The proposed approach will provide more security to the data while transmitting the messages by using improved honey encryption with an advanced DNA encoding scheme in the key generation process.

Improved Honey Encryption Algorithm
The honey encryption process includes honeywords generation process for key or passwords distribution and distribution transforming encoding (DTE) process for message distribution.
In Figure 2, the message space M consists of five messages (m), such as "Apple, Orange, Strawberry, Chocolate, and Mango". This message space is mapped to the seed space, S m using the DTE process. The DTE process uses a discrete distribution function for mapping the message space to the seed space S m overcoming the message space limitation. In the key or password distribution module, keys or passwords are mapped randomly into seed space S k . Before mapping the key, passwords are converted into those keys using advanced DNA encoding.
To generate the ciphertext, the resulting seed space S k is XORed with seed space of message S m .  [3] in the message distribution process. The DTE process uses the discrete distribution function for mapping the messages in the seed space. In Figure 3, we map the five messages by calculating the following step-by-step process, and we denote n as the total number of messages.   Step 1: Number of input messages: 5.

Distribution Transforming Encoder
Step 2: Using 2 n−1 formula, calculate the seed space S m range.
Step 3: Accordingly, to the formula, we take 4 bits binary numbers for placing the seed space.
From the calculation, we can achieve a better result in the HE algorithm if we use the DTE process using a discrete distribution function.

Advanced DNA Encoding Scheme
Deoxyribonucleic Acid (DNA) is made up of four basic nucleic acids: adenine (A), cytosine (C), guanine (G), and thymine (T). It transfers the characters to the DNA-based entities and converts the user's password into the DNA code.
This DNA code is used as the input in the password distribution process. The second approach generates the DNA code that can be used as the key to the HE processes. Therefore, encryption and decryption can be done using five data lookup tables. Figure 4 is the block diagram of the DNA encoding process.
In our proposed system, we use proposed DNA encryption process in the key generation process from user passwords. In the following Figure 5, the user's chosen password is "Crypt@4U". In the DNA encryption process, user-selected passwords are converted into the DNA sequence using the randomly chosen five data lookup tables.

Creating Five Different Data Lookup Tables
We use 64 characters in creating five different data lookup tables. We don't need to consider case-sensitive in our creation of data lookup tables. Therefore, we can reduce the memory overload problem compared to the conventional DNA encryption algorithm because the conventional DNA encryption algorithm includes the complex steps. We arrange the following five different data lookup

Case Study
The following example shows the detailed case study process of the advanced DNA encryption algorithm using five different data lookup tables. In this case study, "Crypt@4U" is used as an example password for converting the DNA code using the advanced DNA encryption algorithm.

Password chosen by the user: Crypt@4U
Convert the user's password into DNA code Using data lookup table 1  Crypt@4U-AGAACGAATGTCATGTCAGCTGAG  Using data lookup table 2  Crypt@4U-CGTCCCCAATTGCTCGCTTCATAC  Using data lookup table 3  Crypt@4U-AAGAGCATACCTAGTTACCTCCGA  Using data lookup table 4  Crypt@4U-GTGGCCGAATGTGCTCTCTACTCA  Using data lookup table 5 Crypt@4U-AGGCGACTGAAGTGAAGCCTTTCT Among the five different data lookup tables, the algorithm randomly chooses one data lookup table. Therefore, the resulting DNA codes are different, although the passwords are the same. So, our advanced DNA encryption algorithm can protect against brute force attacks.

Flowchart of Our Proposed System
The flowchart of our proposed system is shown in Figure 6. This flowchart illustrates the system's step-by-step processes.

Results and Discussions
In this section, we discussed two main parts: the implementation process and experimental results.

Implementation Process
The implementation of the proposed system includes a registration page, login page, home page, inbox page, and message encryption and decryption process. If the user is a new user, the user must fill out the user registration form and complete the registration process. Otherwise, users can log in to the system using the login button. After successful login, the user can see the home page of their accounts. On their home page, users can initiate two processes, such as message encryption and decryption. In the encryption process, users type the message and receiver name and then click the encryption button. The following Figure 7 and Figure 8 describe the receiver decrypting the message using the correct key and getting the correct message. Otherwise, he decrypts the wrong key and gets the wrong message.

Experimental Results
The proposed systems are implemented in a web application by developing a simulated messaging application and the language used is the Python programming language. In this testing, we calculated the execution time for the encryption   Table 6. The data distributed column to column from left to right in the data lookup table-3 is the most time-consuming, and the data distributed row by row from top to bottom in the data lookup table-1 is the least time-consuming according to the testing-1.
The following Table 7 describes the execution times for the different messages "Omicron, Coronavirus, Monkeypox, and InfluenzaA (H1N1)" encrypted with the same password "blockC!" using the different data lookup tables. Because the "Influenza" message was used repeatedly in previous testing [9], the execution time for the 16 character message "InfluenzaA (H1N1)" encryption is 1.54 ms, which is less than the execution time for the "7, 9, and 11" character messages encryption. So, our proposed system can reduce the time when the same messages are transferred repeatedly.
The next testing 3 is to encrypt the different messages with different passwords using the random different data lookup tables. Table 8 shows the execution times for different messages of the same length "Lassa fever, Ebola virus, and YellowFever" encrypted with 7 passwords, 10 passwords, and 15 passwords, respectively. The execution time is 1.26 ms, 1.28 ms, and 1.38 ms, respectively.
In the testing 4, the 18 characters message "Lassa fever-Guinea" encryption with 15 passwords has an execution time of 1.37 ms as shown in Table 9. So, the password length is the same, the execution time is also nearly the same according to the testing, 3 and 4.
In the final testing, the different password lengths (8, 10 and 12) were encrypted compared with the other generations of honeywords using a hashing algorithm and our proposed models using a DNA encoding scheme. It can be seen that the proposed method, which encrypts the messages that are the most commonly used passwords by users, the time complexity of the proposed method is faster than the existing method [3], as shown in Table 10.  According to Table 10, we make the testing process of the length of the passwords longer by using the hashing and salting algorithm and our proposed system. From the results of the existing and our proposed systems, it is clear that our proposed system takes less time for encryption and decryption processes compared to the existing system. Therefore, the processing time of our proposed system is faster than the existing hashing and salting algorithm because the ex-N. N. Khin, T. Win Journal of Computer and Communications isting system has complex steps that include left shift, right shift, and binary conversion. However, our system uses five simple data lookup tables.

Comparison Results of Mathematical Model between Traditional Honey Encryption Algorithm and Improved Honey Encryption Algorithm
The main creation of the honey encryption algorithm is the discrete transforming encoder (DTE). The purpose of DTE is to place the message M into the seed space S m for distribution. The DTE uses the probability function, and it needs to satisfy the property of probability theory. The properties of probability are that the range of values exists between 0 and 1, and the sum of total values needs to get the value of 1 [10]. The improved honey encryption algorithm uses the discrete distribution function of the probability function to overcome the message space limitation problem. The traditional honeyword encryption algorithm uses the cumulative distribution function, and it has the message space limitation problem because it can't satisfy the property of probability theory.
The following examples show that the improved honey encryption algorithm satisfies the properties of probability theory and can overcome the message limitation problems. In the example, we define the seed space message as S m , and the seed space value range P(s) is between 0 and 1, and M represents the message. From Table 11, the traditional honey encryption algorithm using the cumulative distribution function can't satisfy the probability theory. Therefore, the traditional method faces the message space limitation problem when the number of messages is greater than five. However, the improved honey encryption algorithm can solve the message space limitation problem because it can satisfy the property of probability theory [3]. Therefore, we use an improved honey encryption algorithm in our proposed system.

Limitations
Our proposed system is tested with the disease names as the input messages and it can use up to 30 characters for the message space, but the system is limited to a maximum of 255 characters. The testing results are listed by disease name, and every test is limited to test by disease name because proposed system is based on disease name. Since most users only use up to 30 characters, system tested up to 30 passwords in our proposed system. A limitation of the study in this system is that users can securely transfer personal information to each other over the web via a single Wi-Fi network.

Conclusion
The traditional honey encryption algorithm is the strong encryption algorithm for protecting against brute force attacks and facing the message space limitation problem. The improved honey encryption algorithm can solve the message space limitation problem. So, we use a hybrid method of the improved honey encryption and the enhanced DNA encoding scheme in our proposed system. Therefore, our proposed system can protect against brute force attacks and can solve the message space limitation problem. Moreover, we use the enhanced DNA encoding scheme in the key generation process of the improved honey encryption algorithm. Therefore, our proposed system can reduce the processing time more than the traditional honey encryption algorithm. And then, if we apply the advanced DNA encoding scheme in the honeyword generation process, our proposed system can get better security. In the paper, we describe the details and case study of an advanced DNA encoding scheme and mathematical model in order to prove that the DTE process can overcome the message space limitation problem. In the future, we will apply our proposed improved honey encryption algorithm that uses an advanced DNA encoding scheme in the key generation process in large organizations, such as the area of medical fields for transferring medical records.