# A Proposed Harmony Search Algorithm for Honeyword Generation

Authors: Yasser A. Yasser; Ahmed T. Sadiq; Wasim AlHamdani
Journal: Advances in Human-Computer Interaction
 (2022)

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## Abstract

The honeyword system is a password cracking detection technique that aims to improve the security of hashed passwords by making password cracking simpler to detect. Many honeywords (false passwords) accompany the sugarword (true password) to form the sweetwords (false and true passwords) for every user. If the attacker signs in using a honeyword, a silent alarm trigger shows that the honeyword system might be compromised. Many honeyword generation techniques are presented; each one has a flaw in the generating process, a lack of support for all honeyword characteristics, and a slew of honeyword problems. The harmony search algorithm (HSA), a metaheuristic intelligence algorithm inspired by music, is used in this article to offer a novel method for generating honeyword. The suggested honeyword generation technique will enhance the generating process, enhance honeyword characteristics, and address prior approaches’ shortcomings. This paper will show several previous honeyword generation techniques, explain the suggested one, discuss the experimental findings, and compare the new honeyword generation method with the previous ones.


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## Body

## 1. Introduction

Because of its simplicity and memorability, password-based authentication is the most widely recognized authentication method. However, numerous attack methods, such as password cracking, have been used to examine this approach [1, 2]. Password cracking is an uncommon and generally unethical method of retrieving passwords from data maintained or transmitted by a computer system [3].Honeywords is an easy technique to increase the quantity of “honeywords” (false passwords) connected with each user’s account, therefore enhancing the security of hashed passwords and making password cracking simpler to detect [4, 5]. An adversary who obtains entry to the hashed passwords database and reverses the hashing will not determine the real password. If a honeyword is used in the login process, a “silent alert” will be activated [6, 7]. Honeychecker is an additional server that can distinguish between the real and honeywords and is linked to the login server through a secure connection [8, 9]. A metaheuristic is a higher-level process or heuristic used in computer science and mathematical optimization to identify, develop, or choose a heuristic (partial search algorithm) that may offer a suitably good solution to an optimization problem [10]. An optimization issue is a problem in mathematics, computer science, and economics where the goal is to identify the optimum answer out of all the possible ones [11]. The metaheuristic algorithms can be swarm, nature-inspired, physics-based, evaluation-based, or unique solutions [12]. Harmony search algorithm (HSA) is a unique music-inspired algorithm that mimics the improvisation process of musicians aiming to resolve optimization problems by obtaining the optimal solution [13, 14].The suggested honeyword system proposes to use the harmony search algorithm HSA, a metaheuristic music-inspired intelligence algorithm, to provide a novel technique for generating honeyword. Many adjustments have been made to the HSA to suit the problem nature, honeyword generation, and treating solutions as honeywords. The suggested system tokenizes the real password and then handles every token type in different techniques. Every token type has its generator (alphabet, digits, special characters generator). Alphabet token generator uses the proposed HSA, while the digits and special characters tokens use simple random generators.The suggested method chooses the harmony search algorithm to produce honeywords benefiting from the algorithm’s characteristics in terms of quick convergence and population diversity, ease of implementation, and fewer parameters adjustment.The contributions of this paper are many. (1) The proposed system uses the harmony search algorithm offering a novel technique for generating honeyword, (2) the suggested generation technique will enhance the honeyword generating process, support honeyword characteristics, and address prior approaches’ shortcomings, (3) the password alphabet token of the proposed generating algorithm provided great results for generating meaningful words from meaningful words; the most interesting is that the proposed algorithm can find meaningful words from rubbish words, (4) the proposed algorithm suggests its evaluation criterion for the generated honeyword (alphabet token), called the approximation factor, and (5) the sugarword cannot be guessed even if the attacker knows one of the sugarword tokens. In sweetwords, every token is redundant six times. So, if the attacker knows one of the sugarword tokens, then the chance of picking the sugarword at random is 1/6(≈17%).This paper will describe a few honeyword generating techniques, offer a basic explanation for the honeyword technique, illustrate the harmony search algorithm, explain the proposed system with proposed HSA, show the experimental findings, compare with the previous honeyword generation methods, with discussion, and end with the conclusion.


## 2. Related Works

Over the recent few years, much research has presented honeyword generating methods. In addition, there are several asymptotical researches in this area.(i)
In [15], this research suggests several honeyword generation techniques, including modifying a portion of the password, utilizing a dictionary, adding a tail by the system, honeywords supplied by the system, honeywords supplied by the user, and hybrid approaches. These approaches are divided into two groups based on whether or not they impact the user interface (UI), and each category contains a variety of honeyword generation techniques:(1)
Legacy-UI(Chaffing-by-tail-tweaking, Chaffing-by–tweaking-digits, Simple model, Modeling syntax, “Tough nuts”, Hybrid generation methods).(2)
Modified-UI(Take-a-tail, Random pick).(ii)
In [16], this approach is known as “Storage-index,” and it proposes an alternative approach for the honeyword generation that selects honeywords based on existing user passwords in the system to produce realistic honeywords. Honeywords are still employed in the suggested approach to detecting password cracking. This approach imitates honeywords by utilizing existing passwords rather than generating honeywords and saving them in a password file.(iii)
In [17], PDP stands for Paired Distance Protocol, a novel honeyword generating method with a new user interface. To log in, the user will need three pieces of information: a username, a password, and a password-tail. In addition to the username and password, the user chooses a password-tail of t > 1 from a list of (1) alphabetic characters (a-z) and (2) digits upon enrolling (0–9).(iv)
In [18], as new honeyword generation approaches, the “evolving-password model,” “user-profile model,” and “append-secret model” are suggested.(1)
Evolving-password model: The following two separate computation steps can be utilized to finish the process: (a) Counting the number of times password patterns and tokens are used, and (b) generating honeywords from post frequencies and maintaining frequency lists.(2)
User-profile model: Honeywords are made by merging diverse user-profile data by constructing distinct sets from provided data that contain tokens of various types, such as “alphabet-strings,” “digit-strings,” and “special-character-strings.”(3)
Append-secret model: The system requests the user’s username, password, and an additional item, such ase, to produce a random string s that includes numbers, characters, and symbols. The model yields r after running the function f (p|e|s). H (password|r) will be saved in the system’s password file.(v)
In [19], Akif et al. suggest a new honeyword generation approach that includes all four ways. As a consequence, the system received four groups of honeywords that are generated from the following:(1)
Existing user information: Creating data with two sections of public personal questions. The first section will concentrate on characters, while the second will concentrate on numbers. Honeywords will be constructed by combining the answers to the first and second sections.(2)
A dictionary attack: The fundamental idea behind generating suitable honeywords after scanning through the dictionary attack is to utilize the actual password with a modification of up to three numbers or characters.(3)
A generic password list: Honeywords selected at random from a collection of the 500 worse passwords make up this honeyword group.(4)
Shuffling the characters: Honeyword is created by combining scrambled characters or digits from the ID user.


## 3. Honeywords

The honeywords method works by creating honeywords (false passwords) from sugarword (real password), then entering them all as sweetwords into the username and password file, and hashing them all [20, 21]. If the adversary gets plain passwords from hashed passwords, he must guess the real password amongst some of the sweetwords correctly; otherwise, a quiet alert to the system administrator may be fired, signaling that password cracking is feasible [22, 23]. The administrators’ actions are dictated by the organization’s policies and may include banning, deferring, or notifying the account [24].Flatness, let z be the adversary’s estimated chance of accurately predicting the sugarword. Since an adversary can succeed with a chance of 1/k by predicting sugarword randomly, the user’s password pi is chosen using the generation Gen(k; pi). The adversary has at least a (1-(1/k)) chance of picking a honeyword if the honeyword is as flat as possible (i.e., 1/k flat) [25, 26]. For example, in the complete flat honeywords, if the sweetwrods k = 25, the adversary has a (1/25 = 4%) chance of selecting a sugarword and a (1–4% = 96%) for selecting a honeyword [27].User login, the honeypot is examined by the login server when a user wants to connect to his account (the administrator makes fake accounts to detect the attack) [28]. If the account is fake, the administrator will get a warning as a possible attack; if the account is legitimate, hash the user’s password and compare it to the file of sweetwords before submitting checking to the honeychecker [29].


## 4. Harmony Search Intelligence Algorithm

The harmony search algorithm (HSA) is a metaheuristic optimization algorithm based on a natural event where a musician looks for the best notes to create perfect harmony, comparable to searching for the best solutions to a problem [30, 31]. The HSA is simple and easy to implement, has a population diversity, converges rapidly to the best solution, and finds a good enough one in an acceptable amount of time. It can find a balance between exploration and exploitation [32, 33]. Random search, harmony memory considering rate (HMCR), and pitch adjusting rate (PAR) are the three operators that make up the HSA performance process [34].The improvising of musicians for a pitch commonly needs to follow one of these rules: (1) playing any pitch from memory, (2) playing a neighboring pitch from memory, and (3) producing a completely random pitch from the sound range. This process is mimicked in each variable selection of the HSA: (1) selecting any value from the HS memory, (2) selecting a nearby value from the HS memory, and (3) selecting a completely random value from the potential value range [14, 35]. Algorithm 1 shows the general steps of the algorithm, which may be changed based on the problem encoded.Algorithm 1: The general steps of the harmony search algorithm [36].
Step 1: Set up the optimization issue and algorithm parameters (N, HMCR, PAR, and bw).Step 2: Create a harmony memory (HM).N of harmonies are generated (randomly) in the search space and stored in HM at first.Step 3: Create a new harmony from the HM.1st stage: A random number in the range (0, 1) is produced (rand).If rand > HMCR, the new harmony’s decision variable (xnew,j) is created at random. Harmony memory rate (HMCR) is an acronym for harmony memory rate, which ranges from (0, 1).If rand =< HMCR is not specified, one of the harmonics stored in HM is chosen at random, for example, k where 1 =< k =< N. The matching value of harmony k from HM is then used to choose xnew,j.2nd stage: Using a pitch adjustment, the improvised note can be moved to a neighboring value within the range of possibilities. Pitch adjusting rate (PAR) is a parameter in HS that is in the range (0, 1). After 1st stage, a random number that is in the range (0, 1) with uniform distribution is produced to execute the pitch adjustment method (rand). If rand =< PAR is true, bw should be used to move the improvised note to an adjacent value. Where bw is a random distance bandwidth (a scalar value), bandwidth equals the maximum pitch adjustment change. If rand > PAR, the improvised note remains unchanged.Step 4. Update the HM.In HM, compare the new harmony to the worst harmony. If the new one has higher fitness than the poorest one in the HM, it will take its position. Otherwise, it will be removed.Step 5: Continue using Steps 3 and 4 until the termination condition is met.


## 5. The Proposed Harmony Search Algorithm

The proposed honeyword system suggests using the unique metaheuristic music-inspired harmony search algorithm (HSA) as a novel method for the honeyword generating process. The HSA underwent many changes to appropriate the problem space of honeyword generation and handle its solutions as honeywords. This study chooses the HSA because of its simplicity, easy implementation, diversity, rapid converges, providing the best to a good solution, and supplying a balance between exploration and exploitation.The proposed honeyword system was adopted for the legacy-UI, which is more convenient to users because it is just required for username and password to enter. The last one involves alphabets, digits, and special characters. 36 sweetwords are used in the proposed system, which meansk = 36; the adversary has a 1/36(≈3%) chance of successfully selecting the sugarword and has a (1–3% = 97%) probability of selecting a honeyword. The recommendation for the proposed system is that the attacker will not be able to pick the sugarword even if knowing one of its tokens because every token in the sweetwords has been repeated for five times. In this case, the attacker has a 1/6(≈17%) probability of selecting the sugarword.The proposed honeyword system aims to enhance the generating process, enhance honeyword characteristics, and address problems of prior approaches (detailed discussion in Section6.2).The proposed HSA handles the password tokens in a different technique. For each tokens type, there is a different generator (alphabet, digits, special characters generator). These generators are working in parallel. For the alphabet tokens, the HSA constructs its pitch adjustment technique and evaluation criteria.


### 5.1. Proposed HSA Tokens Generators

As mentioned before, the proposed HSA has three tokens generators that are working in parallel. The alphabet generator is the most important and complicated one depending on the HSA technique in the solution of the problems, whereas the digits and special characters generator is simpler depending on the simpler random generating technique. The generators are as follows.


#### 5.1.1. The Proposed HSA Alphabet Tokens Generator

It is the most important part of the honeyword since it is the attacker’s favorite choice for guessing the true password. It is the most complicated generator that depends on the HSA technique in the solution of the problems; the tokens of the password will be treated as pitches. The sugarword’s alphabet token will be used as the input for the generators. It is regarded as the seed that is used to produce the honeywords alphabet tokens. Make six copies for the top five tokens generated by the alphabet generator; then divide the 30 tokens into five groups (columns). Each group has six similar tokens. Six copies of the alphabet seed should be added. As a result, the HSA will include 36 alphabet tokens.


#### 5.1.2. The Proposed HSA Digits Tokens Generator

This generator is based on random generation, and the seed will be the sugarword’s digit token. The generator will generate five tokens of the same length as the seed. Make six copies of each of the five generated digit tokens; then divide the 30 tokens into five groups (rows), each with six similar tokens. The HSA will have 36 digit tokens after adding six copies of the digit seed.


#### 5.1.3. The Proposed HSA Special Characters Token Generator

This generator is based on random generation, and the seed will be the sugarword’s special characters token. The generator will generate six tokens of the same length as the seed. Make six copies of each of the five special characters tokens; then divide the 30 tokens into five groups (rows), each with six similar tokens. Thus, the HSA will have 36 special characters tokens after adding six copies of the seed of the special character.


### 5.2. Pitch Adjustment Technique and Evaluation Criteria of Proposed HSA

For the alphabet token, this section presents the adjacent pitches (tokens) generating technique which consists of four operations (insert, delete, translocation, and swap) and the evaluation criteria used by the proposed HSA.


#### 5.2.1. Pitch Adjustment Technique (Adjacent Token Generating Technique) of the Proposed HAS

The change in token should be concerningbw (distance bandwidth), representing the maximum pitch adjustment change. The adjacent token generating for the alphabet tokens depends on four operations. For each seed token, four tokens will be generated; then the best one will take the place of the seed token. The four operations are as follows:(1)
Insert: Randomly choose certain character places on the token; then insert random characters.(2)
Delete: Randomly choose certain characters placed on the token and delete them.(3)
Translocation: Pick a character’s place on the token at random; then swap them around.(4)
Swap: Choose several character positions on the token at random; then swap those characters.Example 1.
For the proposed HSA that used (bw = 0.3∗ (Token length)) during the adjacent pitches (token) generating, if the sugarword alphabet token is (sea) then bw = 0.3 ∗ (3) = 0.9, so 1 character will be changed. The adjacent pitches (tokens) are (sear, sa, aes, tea) in sequence.


#### 5.2.2. Evaluation Criteria of the Proposed HAS

The initial population’s alphabet tokens will be evaluated on the seed token that is taken from the sugarword, but the evaluation of the generated alphabet adjacent tokens will be metric on the pitches token as its seed. The proposed HSA has its evaluation criterion for the generated tokens; it is called the approximation factor. The value of the approximation factor is in the range (0, 1), which is calculated as the sum of the four criteria values. Each criterion has a different value as mentioned in Section 5.4. The four criteria are as follows:(1)
Character similarity: The character similarity between the seed token’s characters and the produced token’s characters.(2)
The length similarity: The lengths of the characters in the seed token and the produced token are similar.(3)
The PoS (part of speech) similarity: The seed token and the produced token are equivalent in terms of PoS.(4)
Meaningful word: Is the token a word in the English language?


### 5.3. The Proposed HAS Algorithm Steps

The system uses the proposed HSA to generate the honeywords as tokens generating process; the sugarword is tokenized into three separate tokens: alphabet, digits, and special characters, then each one is handled in different generators (alphabet, digits, special characters generator), and then the resulting honeywords are collected with the sugarword to provide the sweetwords. The tokens of the password will be treated as pitches. The proposed HSA is showed in Algorithm2.Algorithm 2: The proposed harmony search algorithm.
Parametern pitches size (population-size), HS harmony size (number of pitches that made harmony), HM harmony memory, HMS harmony memory size (equal to the max-generation), HMCR harmony memory considering rate, PAR pitch adjusting rate, bw distance bandwidth (the maximum pitch adjustment change), ap number of generated adjacent pitches, Mg max-generation, Ec evaluation criteria, d number of the generated digits tokens, dl number of digits that changed in the generated token, s number of the generated special characters tokens, sl number of special characters that changed in the generated token.BeginTokenization/ ∗ parse the sugarword to the alphabet of, numbers, and special characters token  ∗ /If the token is an alphabetGenerate the initial pitches population with n randomlyCompute the fitness of the population with considering to Ecfor i = 1 to Mgfor j = 1 to nGenerate rand1 in range (0, 1) and HMCR random in range (0, 1)if rand < HMCRlet a pitch selected form the population randomlyGenerate rand2 in range (0, 1) and PAR random in range (0, 1)if rand < PARGenerate adjacent pitches with ap respect to bw and choose the best adjacent as the pitchend ifelse generate pitch randomlyend ifend forCompute the fitness of the new population with considering to EcDrop the worst pitches of the population generate ones randomlySelect the best pitches with HS as the harmony then save in HMend forReturn the best harmony in HM as the alphabet honeyword tokensend ifIf the token is a digitfor i = 1 to dfor j = 1 to dlChanges the digits of the token by other digits randomlyend forend forReturn the d tokens as the digits honeyword tokensend ifIf the token is a special characterfor i = 1 to sfor j = 1 to slChanges the special characters of the token by other special characters randomlyend forend forReturn the s tokens as the special characters honeyword tokensend ifCollect honeyword tokensProvide sweetwords by adding sugarword to honeywords then permutate and hashed the sweetwordsEndExample 2.
For the proposed HSA using the parameters listed in Section 5.4. if the sugarword is (killer6+). The generated sweetwords by the proposed HSA will be as follows.


### 5.4. Parameters

Many parameters that impact the HSA’s performance are used in the suggested honeyword generating system. Table1 lists the parameters utilized in the HSA.Table 1
The HSA parameters values.
NoParameterValues1Pitches size (population-size)n802Max-generationMG303Harmony size (number of pitches that made harmony)HS54Harmony memory size (equal to max-generation)HMS305Harmony memory considering rateHMCRRandom in range (0, 1)6Pitch adjusting ratePARRandom in range (0, 1)7Number of generated adjacent pitchesap48Distance bandwidth (maximum pitch adjustment change)bw0.3×(Token length)9Evaluation criteria EcCharacter similarityLength similarityPoS (part of speech) similarityMeaningful word0.20.10.10.610Number of the generated digits tokensd511Number of digits that changed in generated tokendlToken length12Number of the generated special characters tokenss513Number of special characters that changed in generated tokenslToken lengthThe proposed HSA is experimented with a variety of parameter values before settling on the ones that offer the greatest performance for the suggested system. The parameters tested with many values are as follows:(i)
The population-sizen: With the HSA experiment having variety of population sizes (20, 40, 60, and 80), the generation of size (80) was chosen.(ii)
The max-generationMG: There were no improvements in results after 30 rounds, although using several iterations (10, 20, 30, 40, …, 100). So, the alphabet token was given the maximum generation number (30).(iii)
The distance bandwidth (maximum pitch adjustment change)bw: Changing in token during adjacent pitches generation has been attempted in a variety of sizes (1 character, 2 characters, 0.25 ∗ (token length), 0.3 ∗ (token length), 0.5 ∗ (token length)); the changing size (0.3 ∗ (token length)) was chosen.(iv)
Evaluation criteriaEc: Many values have been experimented (0.3, 0.2, 0.2, 0.3) & (0.4, 0.1, 0.1, 0.4) & (0.3, 0.2, 0.1, 0.4) & (0.3, 0.1, 0.1, 0.5) & (0.2, 0.2, 0.1, 0.5) & (0.2, 0.1, 0.2, 0.5) & (0.2, 0.2, 0.1, 0.5) for the evaluation criteria (character similarity, length similarity, PoS (part of speech) similarity, and meaningful word), but the values (0.2, 0.1, 0.1, 0.6) were chosen because they led to the production of meaningful words, which disturb the attacker on guessing the password.


## 5.1. Proposed HSA Tokens Generators

As mentioned before, the proposed HSA has three tokens generators that are working in parallel. The alphabet generator is the most important and complicated one depending on the HSA technique in the solution of the problems, whereas the digits and special characters generator is simpler depending on the simpler random generating technique. The generators are as follows.


### 5.1.1. The Proposed HSA Alphabet Tokens Generator

It is the most important part of the honeyword since it is the attacker’s favorite choice for guessing the true password. It is the most complicated generator that depends on the HSA technique in the solution of the problems; the tokens of the password will be treated as pitches. The sugarword’s alphabet token will be used as the input for the generators. It is regarded as the seed that is used to produce the honeywords alphabet tokens. Make six copies for the top five tokens generated by the alphabet generator; then divide the 30 tokens into five groups (columns). Each group has six similar tokens. Six copies of the alphabet seed should be added. As a result, the HSA will include 36 alphabet tokens.


### 5.1.2. The Proposed HSA Digits Tokens Generator

This generator is based on random generation, and the seed will be the sugarword’s digit token. The generator will generate five tokens of the same length as the seed. Make six copies of each of the five generated digit tokens; then divide the 30 tokens into five groups (rows), each with six similar tokens. The HSA will have 36 digit tokens after adding six copies of the digit seed.


### 5.1.3. The Proposed HSA Special Characters Token Generator

This generator is based on random generation, and the seed will be the sugarword’s special characters token. The generator will generate six tokens of the same length as the seed. Make six copies of each of the five special characters tokens; then divide the 30 tokens into five groups (rows), each with six similar tokens. Thus, the HSA will have 36 special characters tokens after adding six copies of the seed of the special character.


## 5.1.1. The Proposed HSA Alphabet Tokens Generator

It is the most important part of the honeyword since it is the attacker’s favorite choice for guessing the true password. It is the most complicated generator that depends on the HSA technique in the solution of the problems; the tokens of the password will be treated as pitches. The sugarword’s alphabet token will be used as the input for the generators. It is regarded as the seed that is used to produce the honeywords alphabet tokens. Make six copies for the top five tokens generated by the alphabet generator; then divide the 30 tokens into five groups (columns). Each group has six similar tokens. Six copies of the alphabet seed should be added. As a result, the HSA will include 36 alphabet tokens.


## 5.1.2. The Proposed HSA Digits Tokens Generator

This generator is based on random generation, and the seed will be the sugarword’s digit token. The generator will generate five tokens of the same length as the seed. Make six copies of each of the five generated digit tokens; then divide the 30 tokens into five groups (rows), each with six similar tokens. The HSA will have 36 digit tokens after adding six copies of the digit seed.


## 5.1.3. The Proposed HSA Special Characters Token Generator

This generator is based on random generation, and the seed will be the sugarword’s special characters token. The generator will generate six tokens of the same length as the seed. Make six copies of each of the five special characters tokens; then divide the 30 tokens into five groups (rows), each with six similar tokens. Thus, the HSA will have 36 special characters tokens after adding six copies of the seed of the special character.


## 5.2. Pitch Adjustment Technique and Evaluation Criteria of Proposed HSA

For the alphabet token, this section presents the adjacent pitches (tokens) generating technique which consists of four operations (insert, delete, translocation, and swap) and the evaluation criteria used by the proposed HSA.


### 5.2.1. Pitch Adjustment Technique (Adjacent Token Generating Technique) of the Proposed HAS

The change in token should be concerningbw (distance bandwidth), representing the maximum pitch adjustment change. The adjacent token generating for the alphabet tokens depends on four operations. For each seed token, four tokens will be generated; then the best one will take the place of the seed token. The four operations are as follows:(1)
Insert: Randomly choose certain character places on the token; then insert random characters.(2)
Delete: Randomly choose certain characters placed on the token and delete them.(3)
Translocation: Pick a character’s place on the token at random; then swap them around.(4)
Swap: Choose several character positions on the token at random; then swap those characters.Example 1.
For the proposed HSA that used (bw = 0.3∗ (Token length)) during the adjacent pitches (token) generating, if the sugarword alphabet token is (sea) then bw = 0.3 ∗ (3) = 0.9, so 1 character will be changed. The adjacent pitches (tokens) are (sear, sa, aes, tea) in sequence.


### 5.2.2. Evaluation Criteria of the Proposed HAS

The initial population’s alphabet tokens will be evaluated on the seed token that is taken from the sugarword, but the evaluation of the generated alphabet adjacent tokens will be metric on the pitches token as its seed. The proposed HSA has its evaluation criterion for the generated tokens; it is called the approximation factor. The value of the approximation factor is in the range (0, 1), which is calculated as the sum of the four criteria values. Each criterion has a different value as mentioned in Section 5.4. The four criteria are as follows:(1)
Character similarity: The character similarity between the seed token’s characters and the produced token’s characters.(2)
The length similarity: The lengths of the characters in the seed token and the produced token are similar.(3)
The PoS (part of speech) similarity: The seed token and the produced token are equivalent in terms of PoS.(4)
Meaningful word: Is the token a word in the English language?


## 5.2.1. Pitch Adjustment Technique (Adjacent Token Generating Technique) of the Proposed HAS

The change in token should be concerningbw (distance bandwidth), representing the maximum pitch adjustment change. The adjacent token generating for the alphabet tokens depends on four operations. For each seed token, four tokens will be generated; then the best one will take the place of the seed token. The four operations are as follows:(1)
Insert: Randomly choose certain character places on the token; then insert random characters.(2)
Delete: Randomly choose certain characters placed on the token and delete them.(3)
Translocation: Pick a character’s place on the token at random; then swap them around.(4)
Swap: Choose several character positions on the token at random; then swap those characters.Example 1.
For the proposed HSA that used (bw = 0.3∗ (Token length)) during the adjacent pitches (token) generating, if the sugarword alphabet token is (sea) then bw = 0.3 ∗ (3) = 0.9, so 1 character will be changed. The adjacent pitches (tokens) are (sear, sa, aes, tea) in sequence.


## 5.2.2. Evaluation Criteria of the Proposed HAS

The initial population’s alphabet tokens will be evaluated on the seed token that is taken from the sugarword, but the evaluation of the generated alphabet adjacent tokens will be metric on the pitches token as its seed. The proposed HSA has its evaluation criterion for the generated tokens; it is called the approximation factor. The value of the approximation factor is in the range (0, 1), which is calculated as the sum of the four criteria values. Each criterion has a different value as mentioned in Section 5.4. The four criteria are as follows:(1)
Character similarity: The character similarity between the seed token’s characters and the produced token’s characters.(2)
The length similarity: The lengths of the characters in the seed token and the produced token are similar.(3)
The PoS (part of speech) similarity: The seed token and the produced token are equivalent in terms of PoS.(4)
Meaningful word: Is the token a word in the English language?


## 5.3. The Proposed HAS Algorithm Steps

The system uses the proposed HSA to generate the honeywords as tokens generating process; the sugarword is tokenized into three separate tokens: alphabet, digits, and special characters, then each one is handled in different generators (alphabet, digits, special characters generator), and then the resulting honeywords are collected with the sugarword to provide the sweetwords. The tokens of the password will be treated as pitches. The proposed HSA is showed in Algorithm2.Algorithm 2: The proposed harmony search algorithm.
Parametern pitches size (population-size), HS harmony size (number of pitches that made harmony), HM harmony memory, HMS harmony memory size (equal to the max-generation), HMCR harmony memory considering rate, PAR pitch adjusting rate, bw distance bandwidth (the maximum pitch adjustment change), ap number of generated adjacent pitches, Mg max-generation, Ec evaluation criteria, d number of the generated digits tokens, dl number of digits that changed in the generated token, s number of the generated special characters tokens, sl number of special characters that changed in the generated token.BeginTokenization/ ∗ parse the sugarword to the alphabet of, numbers, and special characters token  ∗ /If the token is an alphabetGenerate the initial pitches population with n randomlyCompute the fitness of the population with considering to Ecfor i = 1 to Mgfor j = 1 to nGenerate rand1 in range (0, 1) and HMCR random in range (0, 1)if rand < HMCRlet a pitch selected form the population randomlyGenerate rand2 in range (0, 1) and PAR random in range (0, 1)if rand < PARGenerate adjacent pitches with ap respect to bw and choose the best adjacent as the pitchend ifelse generate pitch randomlyend ifend forCompute the fitness of the new population with considering to EcDrop the worst pitches of the population generate ones randomlySelect the best pitches with HS as the harmony then save in HMend forReturn the best harmony in HM as the alphabet honeyword tokensend ifIf the token is a digitfor i = 1 to dfor j = 1 to dlChanges the digits of the token by other digits randomlyend forend forReturn the d tokens as the digits honeyword tokensend ifIf the token is a special characterfor i = 1 to sfor j = 1 to slChanges the special characters of the token by other special characters randomlyend forend forReturn the s tokens as the special characters honeyword tokensend ifCollect honeyword tokensProvide sweetwords by adding sugarword to honeywords then permutate and hashed the sweetwordsEndExample 2.
For the proposed HSA using the parameters listed in Section 5.4. if the sugarword is (killer6+). The generated sweetwords by the proposed HSA will be as follows.


## 5.4. Parameters

Many parameters that impact the HSA’s performance are used in the suggested honeyword generating system. Table1 lists the parameters utilized in the HSA.Table 1
The HSA parameters values.
NoParameterValues1Pitches size (population-size)n802Max-generationMG303Harmony size (number of pitches that made harmony)HS54Harmony memory size (equal to max-generation)HMS305Harmony memory considering rateHMCRRandom in range (0, 1)6Pitch adjusting ratePARRandom in range (0, 1)7Number of generated adjacent pitchesap48Distance bandwidth (maximum pitch adjustment change)bw0.3×(Token length)9Evaluation criteria EcCharacter similarityLength similarityPoS (part of speech) similarityMeaningful word0.20.10.10.610Number of the generated digits tokensd511Number of digits that changed in generated tokendlToken length12Number of the generated special characters tokenss513Number of special characters that changed in generated tokenslToken lengthThe proposed HSA is experimented with a variety of parameter values before settling on the ones that offer the greatest performance for the suggested system. The parameters tested with many values are as follows:(i)
The population-sizen: With the HSA experiment having variety of population sizes (20, 40, 60, and 80), the generation of size (80) was chosen.(ii)
The max-generationMG: There were no improvements in results after 30 rounds, although using several iterations (10, 20, 30, 40, …, 100). So, the alphabet token was given the maximum generation number (30).(iii)
The distance bandwidth (maximum pitch adjustment change)bw: Changing in token during adjacent pitches generation has been attempted in a variety of sizes (1 character, 2 characters, 0.25 ∗ (token length), 0.3 ∗ (token length), 0.5 ∗ (token length)); the changing size (0.3 ∗ (token length)) was chosen.(iv)
Evaluation criteriaEc: Many values have been experimented (0.3, 0.2, 0.2, 0.3) & (0.4, 0.1, 0.1, 0.4) & (0.3, 0.2, 0.1, 0.4) & (0.3, 0.1, 0.1, 0.5) & (0.2, 0.2, 0.1, 0.5) & (0.2, 0.1, 0.2, 0.5) & (0.2, 0.2, 0.1, 0.5) for the evaluation criteria (character similarity, length similarity, PoS (part of speech) similarity, and meaningful word), but the values (0.2, 0.1, 0.1, 0.6) were chosen because they led to the production of meaningful words, which disturb the attacker on guessing the password.


## 6. Results and Discussions

The experimental results, a comparison between the HSA and the prior honeyword generating method, and discussion will be covered in this study section.


### 6.1. Experimental Results

The HSA is experimented on a variety of password tokens, including the alphabet token, which is the most significant token because guessing the true password is the attacker’s primary goal. Table2 shows the experimental results, using the parameters listed in Section 5.4. The generating procedure for the alphabet token will be based on the HSA approach in the solution of the problem; 80 tokens will be created, but only the best five will be displayed in the results table. A basic random generator will be used for the digit and special characters tokens, with characters changes occurring at random but with the same seed token length. The generated tokes will be six tokens. For the full example, see Example 2.Table 2
Experimental results of the proposed HSA.
Seed tokenPop-size/Max-genHoneyword tokens/approximation factor1Hunter80/30Punter/0.966Hunger/0.966Bunter/0.966Sunder/0.933Sinter/0.933Putter/0.933Punier/0.933Luster/0.933Lunger/0.933Hitter/0.933Mincer/0.9Jitter/0.9Hungry/0.9Handler/0.842Puntes/0.833Hinted/0.8332Shadow20/30Hallow/0.866Shaaban/0.842Salw/0.833Slaw/0.833Skaw/0.83340/30Shaver/0.9Slalom/0.9Khaddar/0.871Hallow/0.866Shaaban/0.84260/30Cracow/0.9Slalom/0.9Shad/0.9Shallow/0.871Shannon/0.84280/30Slalom/0.9Shaver/0.9Shad/0.9Shampoo/0.871Shallow/0.8713Apple80/30Apace/0.919Anile/0.919Angle/0.919Applier/0.887Apron/0.8880/30Ample/0.96Apace/0.919Anile/0.9199Apogee/0.883Spoke/0.8880/30Anile/0.919Applier/0.887Apogee/0.883Spoke/0.88Apron/0.884fOOtball80/30sOftball/0.924gOOfball/0.9fOOthill/0.9fOretell/0.875fOOtman/0.8625Superman80/30Superfine/0.922Supermen/0.875Cesarean/0.875Sumer/0.8624Sumerian/0.856Pokemon80/30Pokeweed/0.887Poker/0.885Pokey/0.885Power/0.857Cowpoke/0.8287Babbygirl80/30Babytalk/0.9Babyhood/0.9Babysitter/0.88Backfield/0.877Basilisk/0.8758Pepper80/30Popper/0.966Peeper/0.966Hepper/0.966Temper/0.933Pipped/0.9339Chocolate80/30Iconolatry/0.89Chowchowl/0.855Chordate/0.855Cholesterol/0.836Cockateel/0.82210Jacket80/30Packet/0.966Market/0.933Cackle/0.933Cachet/0.933Sallet/0.911Kebvtco80/30Greatcoat/0.844Ketch/0.828Kabob/0.828Sketchy/0.7999Jukebox/0.799912Yothd80/30Moth/0.9Hoth/0.9Goth/0.9Voter/0.88Roped/0.8813Nusi80/30Nuss/0.95Nisi/0.95Suss/0.9Rust/0.9Puss/0.9149368N/A1649057623828843153515314N/A2379439711120011652N/A294441876317‘∗^!N/A^_&’.,@ =;).,!∗∼((]:$18/[&N/A(`,}∗”∼|∗_,>`/^19)<N/A#];&∗-?|>&Table2 illustrates the suggested HSA’s generated tokens for various token kinds in order to claim the capability to handle any password token type. Token 1 (hunter) demonstrates that the proposed HSA may yield a large number of useful tokens; 16 generated tokens crossed the 0.6 threshold. Token 2 (shadow) displays created tokens in various Pop-size/Max-gen settings; there are usually decent results, but Pop-size = 80/Max-gen = 30 produces the best results. Token 3 (apple) demonstrates how the proposed SSA creates distinct tokens for each try, even when the tokens and Pop-size/Max-gen are the same. Token 4 (fOOtball) demonstrates the proposed SSA’s ability to handle the password’s capital letters. Tokens 5–10 are alphabet tokens that represent several significant words. Tokens 11–13 display the produced alphabet tokens for trash words. Tokens 14–16 are digit tokens. Tokens 17–19 depict tokens with distinctive characteristics.


### 6.2. Comparison

A comparison between the proposed honeyword systems included the proposed HSA with the prior honeyword generating methods shown in this section.The proposed HSA honeyword generation technique is better than earlier honeyword generation methods in terms of honeyword generating because it enhances the generating process benefiting from its characteristics in problem-solving (diversity, rapid convergence, providing the best to a good solution, and supplying a balance between exploration and exploitation).The proposed HSA enhances the most important honeyword properties (flatness, DoS resistance, and storage), which are not always present in the best possible way in prior honeyword generation techniques. Flatness: The proposed HSA guarantees perfect flatness unconditionally with a 1/36(≈3%) chance for the attacker to correctly pick the sugarword and has a (1–3% = 97%) chance of selecting a honeyword. The recommendation for the proposed HSA is that the attacker has a 1/6(≈17%) probability of selecting the sugarword even with knowing one of its tokens. DoS Resistance: The DoS attack performs by guessing and entering a honeyword to deny the services of the system. The suggested HSA generates honeywords that the adversary cannot guess. Storage: While the proposed HSA stores usernames and sweetwords, several earlier generating techniques save additional data and information.Prior honeyword generating systems face several problems. The proposed honeyword system addresses the seven most pressing concerns of honeyword systems. The following are the seven problems:(i)
Conditional flatness problem: It is the satisfaction of some requirements to attain perfect flatness that is regarded as a weakness Unlike unconditional flatness, which indicates not having to meet any conditions, that is considered a strength. On the other hand, most earlier honeyword generating methods give perfect flatness under certain conditions, but the suggested honeyword system guarantees perfect flatness unconditionally.(ii)
Weak DoS resistance problem: The attacker can predict the honeywords, whereas strong DoS resistance implies the adversary cannot guess the honeywords. Many of the earlier honeyword generating techniques have a weak DoS resistance, but the suggested honeyword system has a strong DoS resistance.(iii)
Storage overhead problem: It is the need for more storage space. Many earlier honeyword generating techniques require additional storage costs, but the suggested honeyword system does not.(iv)
Correlation problem: The presence of a correlation connecting username and password is a problem. As a result, the real password may simply be determined from honeywords. The suggested honeyword system solves the problem by keeping the correlated component the same in the honeywords.(v)
Consecutive and frequented numbers problem: Users prefer rememberable numerical patterns. Thus, many chose to use consecutive or frequented numbers in their passwords, such as ‘123,’ 1234, 111, or 2222,’ which results in the sugarword being recognized. The suggested honeyword system provides a list of the most frequented and consecutive numbers to solve this problem. If the sugarword contains consecutive or frequented numbers, the algorithm will select numbers from the list at random for the honeywords.(vi)
Special date problem: Several people like to put a date in their passwords related to their birth dates, anniversary, the greatest year in school, or any other comparable dates that will reveal the sugarword. As a result, the suggested honeyword system will generate a list of the last 50 years. The system will select years at random from the list to put in the honeywords if the year’s number is shown in sugarword.(vii)
User information security problem: Many of the preceding honeyword generating approaches rely on personal knowledge-based questions, which need users to supply personal information and detail for the methods to create honeywords. If the system is hacked and personal information is exposed, it might be utilized on another system, posing a risk to the user. As a result, employing this approach is a security concern when viewed as a weakness, but not utilizing it is a strength. Therefore, the suggested honeyword system does not need the user to provide any personal information.Table3 shows a comparison between the proposed HSA and prior honeyword generation methods in the most critical honeyword system problems.Table 3
A comparison in most pressing problems of honeyword systems.
No.MethodsCond. flatness problemWeak DoS resist. problemStorage overhead problemCorre. problemCons. and frequent numbers problemSpecial date problemUser info. security problem1Proposed HSANoNoNoNoNoNoNo2Chaffing-by-tail-tweaking [15]YesYesNoYesYesYesNo3Chaffing-by-tweaking-digits [15]YesYesNoYesYesYesNo4Simple model [15]YesNoNoYesNoNoNo5Modeling syntax [15]YesNoNoYesYesYesNo6Chaffing with “tough nuts” [15]N/ANoYesNoN/AN/ANo7Take-a-tail [15]NoNoNoNoNoNoNo8Random pick [15]YesNoNoYesNoNoNo9Hybrid generation methods [15]YesNoNoYesYesYesNo10Storage-index [16]YesYesYesYesNoNoNo11PDP [17]YesNoYesNoYesNoNo12Evolving-password model [18]YesNoNoYesYesYesNo13User-profile model [18]YesYesYesYesYesNoYes14Append-secret model [18]YesNoNoNoYesNoNo15User information method [19]YesYesYesYesYesNoYes16Dictionary attack method [19]YesYesNoYesNoNoNo17Generic password list method [19]YesNoNoYesNoNoNo18Shuffling characters method [19]YesYesNoYesYesYesNo


## 6.1. Experimental Results

The HSA is experimented on a variety of password tokens, including the alphabet token, which is the most significant token because guessing the true password is the attacker’s primary goal. Table2 shows the experimental results, using the parameters listed in Section 5.4. The generating procedure for the alphabet token will be based on the HSA approach in the solution of the problem; 80 tokens will be created, but only the best five will be displayed in the results table. A basic random generator will be used for the digit and special characters tokens, with characters changes occurring at random but with the same seed token length. The generated tokes will be six tokens. For the full example, see Example 2.Table 2
Experimental results of the proposed HSA.
Seed tokenPop-size/Max-genHoneyword tokens/approximation factor1Hunter80/30Punter/0.966Hunger/0.966Bunter/0.966Sunder/0.933Sinter/0.933Putter/0.933Punier/0.933Luster/0.933Lunger/0.933Hitter/0.933Mincer/0.9Jitter/0.9Hungry/0.9Handler/0.842Puntes/0.833Hinted/0.8332Shadow20/30Hallow/0.866Shaaban/0.842Salw/0.833Slaw/0.833Skaw/0.83340/30Shaver/0.9Slalom/0.9Khaddar/0.871Hallow/0.866Shaaban/0.84260/30Cracow/0.9Slalom/0.9Shad/0.9Shallow/0.871Shannon/0.84280/30Slalom/0.9Shaver/0.9Shad/0.9Shampoo/0.871Shallow/0.8713Apple80/30Apace/0.919Anile/0.919Angle/0.919Applier/0.887Apron/0.8880/30Ample/0.96Apace/0.919Anile/0.9199Apogee/0.883Spoke/0.8880/30Anile/0.919Applier/0.887Apogee/0.883Spoke/0.88Apron/0.884fOOtball80/30sOftball/0.924gOOfball/0.9fOOthill/0.9fOretell/0.875fOOtman/0.8625Superman80/30Superfine/0.922Supermen/0.875Cesarean/0.875Sumer/0.8624Sumerian/0.856Pokemon80/30Pokeweed/0.887Poker/0.885Pokey/0.885Power/0.857Cowpoke/0.8287Babbygirl80/30Babytalk/0.9Babyhood/0.9Babysitter/0.88Backfield/0.877Basilisk/0.8758Pepper80/30Popper/0.966Peeper/0.966Hepper/0.966Temper/0.933Pipped/0.9339Chocolate80/30Iconolatry/0.89Chowchowl/0.855Chordate/0.855Cholesterol/0.836Cockateel/0.82210Jacket80/30Packet/0.966Market/0.933Cackle/0.933Cachet/0.933Sallet/0.911Kebvtco80/30Greatcoat/0.844Ketch/0.828Kabob/0.828Sketchy/0.7999Jukebox/0.799912Yothd80/30Moth/0.9Hoth/0.9Goth/0.9Voter/0.88Roped/0.8813Nusi80/30Nuss/0.95Nisi/0.95Suss/0.9Rust/0.9Puss/0.9149368N/A1649057623828843153515314N/A2379439711120011652N/A294441876317‘∗^!N/A^_&’.,@ =;).,!∗∼((]:$18/[&N/A(`,}∗”∼|∗_,>`/^19)<N/A#];&∗-?|>&Table2 illustrates the suggested HSA’s generated tokens for various token kinds in order to claim the capability to handle any password token type. Token 1 (hunter) demonstrates that the proposed HSA may yield a large number of useful tokens; 16 generated tokens crossed the 0.6 threshold. Token 2 (shadow) displays created tokens in various Pop-size/Max-gen settings; there are usually decent results, but Pop-size = 80/Max-gen = 30 produces the best results. Token 3 (apple) demonstrates how the proposed SSA creates distinct tokens for each try, even when the tokens and Pop-size/Max-gen are the same. Token 4 (fOOtball) demonstrates the proposed SSA’s ability to handle the password’s capital letters. Tokens 5–10 are alphabet tokens that represent several significant words. Tokens 11–13 display the produced alphabet tokens for trash words. Tokens 14–16 are digit tokens. Tokens 17–19 depict tokens with distinctive characteristics.


## 6.2. Comparison

A comparison between the proposed honeyword systems included the proposed HSA with the prior honeyword generating methods shown in this section.The proposed HSA honeyword generation technique is better than earlier honeyword generation methods in terms of honeyword generating because it enhances the generating process benefiting from its characteristics in problem-solving (diversity, rapid convergence, providing the best to a good solution, and supplying a balance between exploration and exploitation).The proposed HSA enhances the most important honeyword properties (flatness, DoS resistance, and storage), which are not always present in the best possible way in prior honeyword generation techniques. Flatness: The proposed HSA guarantees perfect flatness unconditionally with a 1/36(≈3%) chance for the attacker to correctly pick the sugarword and has a (1–3% = 97%) chance of selecting a honeyword. The recommendation for the proposed HSA is that the attacker has a 1/6(≈17%) probability of selecting the sugarword even with knowing one of its tokens. DoS Resistance: The DoS attack performs by guessing and entering a honeyword to deny the services of the system. The suggested HSA generates honeywords that the adversary cannot guess. Storage: While the proposed HSA stores usernames and sweetwords, several earlier generating techniques save additional data and information.Prior honeyword generating systems face several problems. The proposed honeyword system addresses the seven most pressing concerns of honeyword systems. The following are the seven problems:(i)
Conditional flatness problem: It is the satisfaction of some requirements to attain perfect flatness that is regarded as a weakness Unlike unconditional flatness, which indicates not having to meet any conditions, that is considered a strength. On the other hand, most earlier honeyword generating methods give perfect flatness under certain conditions, but the suggested honeyword system guarantees perfect flatness unconditionally.(ii)
Weak DoS resistance problem: The attacker can predict the honeywords, whereas strong DoS resistance implies the adversary cannot guess the honeywords. Many of the earlier honeyword generating techniques have a weak DoS resistance, but the suggested honeyword system has a strong DoS resistance.(iii)
Storage overhead problem: It is the need for more storage space. Many earlier honeyword generating techniques require additional storage costs, but the suggested honeyword system does not.(iv)
Correlation problem: The presence of a correlation connecting username and password is a problem. As a result, the real password may simply be determined from honeywords. The suggested honeyword system solves the problem by keeping the correlated component the same in the honeywords.(v)
Consecutive and frequented numbers problem: Users prefer rememberable numerical patterns. Thus, many chose to use consecutive or frequented numbers in their passwords, such as ‘123,’ 1234, 111, or 2222,’ which results in the sugarword being recognized. The suggested honeyword system provides a list of the most frequented and consecutive numbers to solve this problem. If the sugarword contains consecutive or frequented numbers, the algorithm will select numbers from the list at random for the honeywords.(vi)
Special date problem: Several people like to put a date in their passwords related to their birth dates, anniversary, the greatest year in school, or any other comparable dates that will reveal the sugarword. As a result, the suggested honeyword system will generate a list of the last 50 years. The system will select years at random from the list to put in the honeywords if the year’s number is shown in sugarword.(vii)
User information security problem: Many of the preceding honeyword generating approaches rely on personal knowledge-based questions, which need users to supply personal information and detail for the methods to create honeywords. If the system is hacked and personal information is exposed, it might be utilized on another system, posing a risk to the user. As a result, employing this approach is a security concern when viewed as a weakness, but not utilizing it is a strength. Therefore, the suggested honeyword system does not need the user to provide any personal information.Table3 shows a comparison between the proposed HSA and prior honeyword generation methods in the most critical honeyword system problems.Table 3
A comparison in most pressing problems of honeyword systems.
No.MethodsCond. flatness problemWeak DoS resist. problemStorage overhead problemCorre. problemCons. and frequent numbers problemSpecial date problemUser info. security problem1Proposed HSANoNoNoNoNoNoNo2Chaffing-by-tail-tweaking [15]YesYesNoYesYesYesNo3Chaffing-by-tweaking-digits [15]YesYesNoYesYesYesNo4Simple model [15]YesNoNoYesNoNoNo5Modeling syntax [15]YesNoNoYesYesYesNo6Chaffing with “tough nuts” [15]N/ANoYesNoN/AN/ANo7Take-a-tail [15]NoNoNoNoNoNoNo8Random pick [15]YesNoNoYesNoNoNo9Hybrid generation methods [15]YesNoNoYesYesYesNo10Storage-index [16]YesYesYesYesNoNoNo11PDP [17]YesNoYesNoYesNoNo12Evolving-password model [18]YesNoNoYesYesYesNo13User-profile model [18]YesYesYesYesYesNoYes14Append-secret model [18]YesNoNoNoYesNoNo15User information method [19]YesYesYesYesYesNoYes16Dictionary attack method [19]YesYesNoYesNoNoNo17Generic password list method [19]YesNoNoYesNoNoNo18Shuffling characters method [19]YesYesNoYesYesYesNo


## 7. Discussion

The results of the experiments showed that the proposed method effectively produces passwords with all of its tokens (alphabet, digits, and special characters), particularly the alphabet token, with its difficulties in relating to meaningful phrases. The alphabet token generation technique had excellent results in terms of creating meaningful words from meaningful words; perhaps most notably, the system was able to create meaningful words from rubbish words. As a consequence of the analysis of the results, the proposed HSA determines that the Pop-size should be more than the Max-gen; as a result, the proposed system picks Pop-size = 80/Max-gen = 30 based on experience. The results reveal that Pop-sizes of 20, 40, 60, and 80 produce good results, while Pop-size = 80 produces a better approximation factor. The produced honeywords have a lot of desirable qualities, according to the results. (1) Independent tokens generation: it generates each password token type separately. (2) Different solutions generation: Even if the Pop-size/Max-gen is set to (80/30), every generating operation generates distinct honeywords. (3) Manipulation of several password patterns: it can carry out a variety of token order password patterns. (4) Sweetwords with a high level of security: They have strong security against attacker guessing. (5) Capital letters handling: it can carry out the capital letters of alphabet tokens.The comparisons between the proposed HSA and prior generating techniques demonstrate that the current approach is superior in three dimensions: Honeyword producing process, honeyword characteristics, and resolving previous method problems. The essential property, flatness, shows a significant improvement for the suggested system; the proposed system has a better flatness 1/36(≈3%). Furthermore, even if he knows one of the sugarword tokens, the adversary has a 1/6(≈17%) probability of picking the sugarword.


## 8. Conclusion

The suggested system uses the harmony search algorithm, a unique metaheuristic music-inspired algorithm to offer a novel approach for the honeyword generation process, which is modified numerous times to match the problem. Furthermore, it effectively employs an intelligence algorithm (HSA) for security reasons, namely, password cracking detection system (honeyword system). The proposed HSA enhances the generating process, satisfies honeyword characteristics, addresses prior approaches’ shortcomings, generates meaningful words from rubbish words, and suggests its evaluation criterion called the approximation factor.The alphabet token is the most significant and complicated part of the sugarword. Therefore, the suggested system is used for the proposed HSA technique to generate the alphabet token in the solutions of the problems. On the other hand, the digit and special characters tokens depend on a simple random generating technique.A kind of limitation can infect the proposed method if the initial population is not well-diversified, which may lead to more iterations of implementation.Employing the knowledge gained from this research of using metaheuristic algorithms, this paper makes suggestions for honeyword generating techniques and seeks to identify another intelligence methodology that may give ideal solutions (honeywords). Researchers can use HSA in their study and attempt to figure out how to use it for solving multiobjective optimization issues. Further study in this subject may aim towards identifying and resolving other problems that honeywords systems face, with few places where HSA might be improved and hybridized with another algorithm.



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*Source: 9607550-2022-03-25.xml*