# A Novel Dictionary Generation Methodology for Contextual-Based Password Cracking

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Received March 29, 2022, accepted May 25, 2022, date of publication June 2, 2022, date of current version June 8, 2022.
Digital Object Identifier 10.1109/ACCESS.2022.3179701

A Novel Dictionary Generation Methodology for
Contextual-Based Password Cracking
AIKATERINI KANTA 1,2 , (Graduate Student Member, IEEE), IWEN COISEL1 ,
AND MARK SCANLON 2 , (Senior Member, IEEE)
1 European Commission Joint Research Centre (DG JRC), Varese, 21027 Ispra, Italy

2 Forensics and Security Research Group, School of Computer Science, University College Dublin, Dublin 4, D04 V1W8 Ireland

Corresponding author: Aikaterini Kanta (aikaterini.kanta@ucdconnect.ie)

ABSTRACT It has been more than 50 years since the concept of passwords was introduced and adopted in
our society as a digital authentication method. Despite alternative authentication methods being developed
later, it is reasonable to assume that this prevailing authentication method will not fall out of popularity
anytime soon. Naturally, each password is closely connected to its creator. This connection has given rise
to advanced techniques aimed at exploiting user habits for password cracking. Such techniques are often
generic approaches that leverage large datasets of human-created passwords. Recent research has underlined
the influence that context can have during password selection for a user. This information could be of
significant added value when digital investigators need to target a specific user or group of users during a
criminal investigation. There are no automated approaches that can extract and utilize contextual information
during the password cracking processes. In this paper, a methodology and framework for creating custom
dictionary word lists for dictionary-based password cracking attacks are introduced, with a specific focus on
leveraging contextual information encountered during an investigation. Furthermore, a detailed explanation
of the framework’s implementation is provided, and the benefits of the approach are demonstrated with the
use of test cases.
INDEX TERMS Password cracking candidate generation, context based password cracking, password
cracking, wordlist creation.

I. INTRODUCTION

Despite known security concerns, password-based authentication remains the most widely used method of authentication [1]. A 2021 study showed that the online identity
of almost one in three Americans was stolen in the last
year alone, and another 13% were uncertain whether their
credentials were part of a data breach [2]. In a spirit of
strengthening security, password policies are nowadays more
restrictive and require users to select stronger passwords.
Salting the passwords1 additionally increases the complexity
of password cracking process, as each salt must be considered
sequentially. Salting renders the commonly used rainbow
table based password cracking approach obsolete. Typically,
the password remains the weakest link to gain entry into a
The associate editor coordinating the review of this manuscript and
approving it for publication was Ilsun You .
1 The salt is a random string (typically 3 to 5 random characters) that is
concatenated to the password before hashing it for database storage.

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system [3]. This weakness is accentuated when an attacker
is focusing on gaining access to a multi-user system and
not targeting any one specific user. A single weak password
could grant attackers access to such a system, rendering
the effort and precautions taken by security concerned system administrators void. In these cases, attackers focus on
generic approaches – effectively modeling the popular habits
and trends of real-world users’ password choices [4]. These
attacks use large dictionaries of human-created passwords
available online from previous data leaks/breaches. Furthermore, attacks have evolved to become more refined and
sophisticated to compensate for the increase in computational
cost of the underlying algorithms and the strengthening of
password policies [5].
Of course, there are also targeted attacks that focus on one
specific user. For example, this is the case for law enforcement during a lawful criminal investigation, e.g., attempting
to retrieve evidence from a suspect’s online/offline account
or whenever encrypted devices are encountered during digital

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

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A. Kanta et al.: Novel Dictionary Generation Methodology for Contextual-Based Password Cracking

forensic examination [6]. Of course, generic approaches can
be attempted, as they rely on mimicking user tendencies or
they leverage passwords originating from data leaks. However, this use case can also benefit from a more targeted
context-based approach. This targeted approach should take
into account the fact that users often follow certain habits
when creating their passwords. Their use of numbers and
symbols is often meaningful, and the placement of capital letters and non-alphabetical characters is often predictable [7].
Users choose passwords that are memorable or meaningful
to them. This is due to the fact that a typical user maintains
tens of different passwords for different systems and devices.
Since these password habits exist, the knowledge of personal
information about a specific user can lead to more educated
guesses of their passwords. This information could include
important dates in their lives, names of family and friends,
related locations, as well as their interests, likes, and dislikes.
A particularly insightful piece of personal information could
turn out to be their password, or part thereof.
In this manner, password candidates lists (dictionaries)
bespoke to each individual can be created. Often, this information is easily and publicly available online, e.g., accessible
on their social media profiles or professional websites. In the
case of a law enforcement investigation, additional information could be obtained through warrants, interrogations, etc.
Taking the bespoke approach one step further, thematic
dictionary lists around specific topics can be assembled.
In terms of law enforcement, there is a significant potential
benefit from this in expediting cases. During an investigation, it can be of paramount importance to gain access to
encrypted devices, an often insurmountable task given limited resources [8]. Manually creating customized dictionaries
for each suspect would be a very time-consuming process.
To overcome this, having some established lists on commonly
encountered topics and interests could result in an optimized
start to the password guessing process.
In this paper, a methodology for creating bespoke and
topic-specific dictionary lists is introduced, starting with a
single contextual seed word. Dictionary lists are fully customizable; the length of the list and the contextual broadness
of the generated password candidates are customizable by the
user. Merging lists from multiple seed words is also an option.
An evaluation of the proposed methodology is presented
and the first assessment that demonstrates the viability and
impact of context-based password cracking is outlined. The
contribution of this work includes:
The design of a novel methodology for creating bespoke
dictionary lists based off a user’s interests or specific
topics with customizable depth of search is provided.
• Several experiments on a prototype implementation
are described, assessing the impact of the proposed
approach on password cracking.
• The benefits of the proposed approach over existing
approaches alone are demonstrated.
•

The rest of the paper is organized as follows: Section II
offers an overview of related work in the field, focusing
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specifically on user tendencies when it comes to password
creation and password strength. Section III presents the
proposed methodology for creating bespoke dictionary lists
and outlines the details of how the methodology has been
implemented, providing an in-depth explanation of the development choices made. Section V presents some proof-ofconcept experiments using the resultant password candidate
dictionaries compared to a commonly used baseline in the
literature. Finally, the paper culminates with a discussion of
the results and the conclusions and future work are outlined.
II. BACKGROUND AND RELATED WORK

This section provides some related work and background
information in the field of password cracking to appreciate
the context of the proposed methodology.
A. PASSWORD CRACKING TECHNIQUES

The most straightforward password cracking technique is an
exhaustive search (also called a brute force attack) where
all combinations of a given alphabet, including digits and
special characters, up to predetermined length are tested.
With no defined maximum password length or limits for
attempts, exhaustive searches are guaranteed to work – the
only variable is time. Nowadays, passwords up to 8 characters
long can be checked in a reasonable amount of time with just
a single GPU [8]. For longer passwords or when the targeted
hash function is not optimal, this approach is not efficient and
is deemed computationally infeasible.
Therefore, many other methods have been developed
to close that gap, such as rainbow tables, dictionary lists
(with or without password candidate mangling rules) and
more recently machine learning approaches. Rainbow tables
are a time-memory trade-off focused on precomputing
an almost exhaustive predefined search space of passwords. These tables store a minimal amount of information enabling fast recovery of a given password if it fits
into the predefined search space [9]. The use of salting
in password-based authentication methods makes rainbow
table-based approaches entirely obsolete, as one rainbow
table would need to be constructed for each possible salt,
of which there are near infinite possibilities.
When it comes to machine learning methods, these include
Markov-based models to significantly reduce the size of
the password space that needs to be searched [10], probabilistic context-free grammars [11], and neural networks to
model the resistance of human-chosen passwords to guessing
attacks [12]. One such example of a neural network is Generative Adversarial Networks (GANs); where a neural network
is developed to create password candidates that are as close to
the distribution of real passwords originating from real-world
password leaks [13].
One of the most common password-cracking methods
remains the dictionary-based attack. Dictionary attacks are
often combined with a set of password mangling rules that
specifies the variations to the dictionary word will be tried
– these rules aim to mimic common real-world user behavior
when creating a password. For example, replacing letters with
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numbers or symbols, e.g., replacing ‘i’ with ‘1’ or ‘!’, letter
capitalizations, or adding numbers/symbols at the beginning,
middle or end, etc.
B. PASSWORD SELECTION TRENDS

The number of accounts that every regular computer user
owns is increasing. Single Sign-On (SSO) approaches and/or
password managers can assist users in password management, while simultaneously strengthening the passwords
used. However, as shown by [14], these approaches are still
not yet widely adopted. Consequently, a large proportion of
users re-use their passwords [15]–[17]. This is likely to avoid
having to remember an increasing number of increasingly
complex passwords (enforced through increasingly strict
password policies). Reusing passwords with/without slight
modifications among different services significantly reduces
their security. For example, if one of these passwords is
leaked, all login credentials that re-use the leaked password,
or a variation thereof, are in danger of compromise and should
be considered as unsafe [18].
When looking at leaked password lists from various data
breaches, common trends have emerged in password selection. For example, when asked to create a password with
lowercase and uppercase letters, users are likely to capitalize
the first letter of their password [7]. When asked to include
numbers and/or special characters in their passwords, they are
very likely to use number sequences such as ‘123’, number
repetitions such as ‘111’, meaningful numbers such as ‘314’,
or use letter substitutions such as ‘@’ for ‘a’ and ‘1’ for ‘i’ [7].
One study showed that users tend to believe that adding digits
to the password increases the complexity of guessing it, while
the use of keyboard patterns and common phrases was not
perceived as a bad password practice [19].
A study focused on Chinese users [20] showed that more
than 50% had passwords that consisted of only digits. The
same study also showed that professionals generally chose
lengthier passwords than students, and 12% included personal information in their passwords, e.g., birth dates or years.
Another study that analyzed RockYou (a popular password
cracking dictionary used throughout the literature) showed
that 4.5% of the passwords contained dates [21]. This type
of information, while personal, is often easily accessible to
adversaries [22].
Another analysis of passwords showed that password
selection is far from random and that in fact it follows the
distribution of natural language [23]. Users prefer to choose
simple noun bigrams as found in natural language. When
looking at differences in password preferences between people of different nationalities, some subtle differences were
found by [24]. For example, the authors demonstrated that
Arabic users were three times more likely to include their
mobile phone number in their password, while people from
India and Pakistan were more prone to use names.
It is therefore of significant interest to look more into password selection trends, what trend information can be derived
and potentially leveraged in lawful password cracking.
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C. PASSWORD STRENGTH

Enforcing the selection of strong passwords can help to protect digital systems from password cracking attacks. Password strength meters fulfill strength evaluation requirements
forbidding users from inadvertently selecting weak passwords. However, a comparison study conducted on strength
meters from some of the most popular websites and systems showed they are highly inconsistent [25]. The same
password on different strength meters can be evaluated from
adequate to great, depending on what parameters each meter
uses for its evaluation. These parameters include entropy,
length, estimated number of guesses it would take to crack the
password, etc.
The use of password strength meters can have the desired
effect of users choosing more difficult passwords to fulfill
the meter’s requirements, but subsequently need to resort
to writing the password down because they cannot remember them [26]. Furthermore, entropy, which is one of the
most common measures of password strength, has been
shown to be an ineffective metric against intelligence-based
attacks [27].
In order to mitigate against these issues, various alternatives to password meters have been proposed, e.g., limiting
the number of login attempts, two-factor authentication, and
the use of graphical passwords [28] or mnemonic based
passwords. More recently, other methods have been proposed where Markov Chain methods are leveraged to create
a multi-modal strength metric for passwords [29].

III. DICTIONARY CREATION METHODOLOGY

As mentioned in the previous section, dictionary attacks are
an effective way to crack passwords. There are many publicly
available dictionary lists that are used for the purpose of
password cracking, many of which originating from leaked
password lists from data breaches. One of the most famous
lists is RockYou. This list originates from the RockYou
company leak in 2009. The complete list of passwords from
this leak are available as they were stored in plaintext by the
company.
To this end, it seems logical that the best way to increase
the chances of cracking a password (or cracking as many
passwords as possible) from a list of hashed passwords is to
create a more robust dictionary list. The dictionary generation
approach proposed as part of this work leverages the fact
that: 1) users tend to choose passwords based on real words,
2) users choose passwords that are meaningful to them, and/or
3) users often use personal information including names, birth
dates, places, and interests, e.g., sports, cars, popular cultural
references, etc. This selection of features is based on statistical analysis of over 3.9 billion real-world passwords [7].
The authors used the HaveIBeenPwned dataset to deconstruct
the passwords down into their constituent components and
classify them according to context. This analysis demonstrated that the aforementioned categories are some of the
most popular chosen in the real world.
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FIGURE 1. A depiction of the tree-like structure of Wikipedia.

A reasonable hypothesis is that if a user is tasked with
defining a password for a website of a specific topic, the
probability that this password might be thematically close to
that topic is higher, e.g., more likely to choose a car related
password for a car forum. Therefore, a dictionary generation strategy based on thematic categories can prove useful.
Ideally, the building of a diverse portfolio of dictionaries
for various contexts can be used alone or in combination
according to a specific target.
The approach outlined as part of this paper for creating dictionaries starts with Wikipedia.2 The reasoning behind this is
that each page in Wikipedia provides links to other Wikipedia
entries that are thematically close – from a semantic, cultural
and common association standpoint. This thematic linking of
content can be pictured as a tree-like structure stemming from
the root word, or seed phrase. This tree-like structure enables
the selection of a starting point and the definition of the depth
and breadth of the exploration. An example of the tree-like
structure of Wikipedia can be seen in Figure 1.
An example of the Wikipedia-driven topic hierarchy is
shown in Figure 1. Assuming that the seed topic is ‘‘Manga’’,
each of the links referenced in manga’s Wikipedia entry leads
to further related Wikipedia pages, from different types of
manga, to famous Japanese actors, writers, and illustrators,
to manga-related TV networks, etc. Proceeding down one
level, i.e., visiting each of these Wikipedia entries, leads to
further new related pages, and so on. For the purpose of
collecting this information from Wikipedia, DBPedia was
used, as outlined in further detail in the following section.
A. DBPedia

DBPedia3 is a crowd-sourced project aiming to offer a structured manner to access the information found in Wikipedia.
The DBPedia information contains the abstract of each article
found on each Wikipedia page, as well as the information
contained in the article’s infobox. The infobox contains
a summary of the most relevant information related to each
article. As infoboxes in Wikipedia do not consistently follow

a single structure, that information is collected with mappings. Mappings assign each entity in the infobox a DBpedia
ontology type so that each attribute in the infobox is mapped
to the DBpedia ontology [30]. This provides an easy way to
leverage the structure and links between Wikipedia pages,
providing an interconnecting web of content that is thematically related.
1) KEYWORD EXTRACTION

In order to extract information from DBPedia, the Python
library rdflib4 is used, which is a library for the Resource
Description Framework (RDF).5 RDF is a data model that is
used to merge graph data when the underlying schemas differ.
B. CREATING THE LAYERS

The starting point for creating a context based dictionary is a
single seed word/topic/phrase and its corresponding DBPedia
article. For example, if the objective is to create a dictionary
about Manga, the starting point would be the DBPedia page
for Manga. The first step is to collect all the links on the
Manga entry that point to other related entries. As these are
directly connecting to manga, as part of this paper they are
referred to as the first layer. The next step is to visit these new
entries and repeat the same process; collecting more and more
links along the way. Consequently, each new link is classified
into a different layer, according to how many ‘‘hops’’ it is
from the starting point of the graph. A reasonable assumption
that is made at this stage is that a link that resides in layer
one, i.e., directly linked to the Manga entry, is likely to be
thematically more relevant to Manga than a link that is on
layer two, three, or subsequent layers.
Furthermore, each new layer added significantly increases
the complexity. As one example, layer one for the DBPedia
article for Manga contains 314 entries, while layer two
contains 19,727. Additionally, as many of these entries are
interconnected, i.e., the Manga entry points to the Dragon
Ball Z entry and vice versa, particular care is taken not to
include any repeating entries. The interconnected web of

2 wikipedia.org

4 https://rdflib.readthedocs.io/en/stable/

3 https://www.dbpedia.org/

5 https://www.w3.org/RDF/

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A. Kanta et al.: Novel Dictionary Generation Methodology for Contextual-Based Password Cracking

FIGURE 2. A methodology diagram for creating a dictionary from Wikipedia/DBPedia.

the articles can also be used as a relevancy metric for each
page encountered – similar to one of the indicator’s web
search engines use to determine a webpage’s relevancy based
on how many pages link to it, such as Google’s PageRank
algorithm [31].
A comprehensive diagram of the proposed process is
shown in Figure 2. The length and scope of this list can be
configured at the moment of generation. It can be limited to
one layer, referred to as ‘‘contextual dictionary 1’’ (CD_1)
as part of this work, two layers (CD_2), three layers (CD_3),
etc. With each new layer added, the quantity of data increases
exponentially. Therefore, the trade-off between speed, dictionary length, and ultimate success rate is a consideration [32].
Furthermore, among the links contained in a Wikipedia
(and corresponding DBPedia entry), some generic and nontopic-specific links can be found. These usually are used
for Wikipedia’s internal hierarchy and labeling of contents
in each entry, and these are excluded from the generated
dictionaries.

C. DICTIONARY LIST SANITATION

At the culmination of the previous process, the first version
of the dictionary list is created. At this point, subsequent
steps are taken to sanitize this list and exclude entries (or
partial entries) that are not contextually close to the starting seed word(s). Many linked pages from Wikipedia articles have the form List of [Topic] or Categories: [Topic].
For example, using the Manga seed word, some of the
linked Wikipedia pages include ‘‘List of Japanese manga
magazines by circulation’’ and ‘‘Categories: Languages of
Japan’’. Although the contents of these are thematically
relevant and useful, these entries themselves do not offer
added value and are therefore excluded from our dictionary
list.
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Regarding entries consisting of more than one word, each
entry is included in the resultant password candidate dictionary list in two ways; as a concatenation of the words without
spaces and as separate words. If these separate words consist
of common stop words, they are removed. The removal of
stop words happens for two main reasons; 1) this group of
words does not provide any value to our process, and 2) as
the size of the dictionary length decreases, a corresponding
decrease in processing time follows [33]. As an example,
if the entry The Girl From Ipanema is found, these three
entries are added to the list: TheGirlFromIpanema, Girl,
Ipanema.
IV. BENEFITS, LIMITATIONS, AND TRADE-OFFS

As can be seen in Figure 2, the starting point for the proposed
contextual dictionary approach is a single Wikipedia article
stemming from the available contextual information about a
target individual or community. In any digital investigation,
this bespoke dictionary generation step could be one of the
first after collecting evidence on the individual related to his
interests, hobbies, and other personal information. However,
it might prove fruitful not to choose the bespoke dictionary
approach from the get go. The reason for this is that users
still tend to choose passwords that are not very difficult and
possibly easy to crack with more unsophisticated methods,
i.e., exhaustive search or ‘‘off-the-shelf’’ dictionary attacks.
It is reasonable to first eliminate weak password candidates
with an exhaustive search before using the approach outlined
in this paper or to pursue both approaches simultaneously.
Furthermore, this exhaustive search can commence from
the beginning of the investigation as it does not require collecting any other information, as it is entirely independent
of any context. While the exhaustive search is carried out,
evidence and information that can help launch the bespoke
context-based dictionary attack can be collected.
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This begs the question of where exactly in the password cracking pipeline the proposed approach might fit. The
answer is that there is no one-size-fits-all solution to this
question. If time is of the essence and it is known that the
suspect is someone technologically and security savvy, then a
reasonable assumption can be made that an exhaustive search
of up to 8 characters is not likely to produce results; therefore,
this choice may be skipped or postponed. If this is the case,
but the process of collecting evidence to launch the targeted
dictionary attack is still ongoing, another dictionary attack
might take precedent.
As mentioned in Section II, dictionary attacks are one of
the most popular types of password cracking techniques used.
It can be argued either way whether a regular dictionary attack
could take precedent over a context-based dictionary attack
depending on the specific case and the number of passwords
to be retrieved. A good approach would be to target easy-toguess passwords first with a regular dictionary approach and
then follow with a more intelligent attack for more difficult
passwords later. If they are in possession of the investigator,
previous passwords and variations thereof should be tested
first. These can also offer insights into the suspect user’s
personal mangling rule selection. In any case, the specific
parameters of the case will dictate the choice.
A significant consideration when choosing the proposed
approach is the length of the generated dictionary. A smaller
dictionary will allow for a larger number of combinations of
mangling rules to be attempted over a fixed time period (or
fixed number of guesses). Smaller dictionaries will result in
more mangled attempts being made based on more relevant
password candidates, e.g., passwords in CD_2 (which are
direct links of the seed word) will be contextually closer
to the seed word. As a result, given a fixed time (or fixed
number of attempts), there is a trade-off to consider between
checking more, i.e., more distant, password candidates and
checking fewer, i.e., more related, candidates with more mangling rules. This is an especially important choice as more
layers are added as the length of the dictionary list increases
correspondingly.
The last consideration for the proposed approach is the
information that is included in it. As the traversal from the
seed word to subsequent layers is taking place, the decision
was made to only include links found in each DBPedia article. The reason for this is once again based on a trade-off.
In the initial design of this approach, adding the sanitized text
of the abstract and/or article was considered. The approach
consisted of an extraction of keywords from this text and
the incorporation of them into the list along with the links.
Ultimately, the inclusion of words from the abstract/article
itself was decided against, as this did not offer any significant
increase in value. It is also reasonable to assume that the
links contained in each Wikipedia article are also the most
important related topics to the original seed word. While,
there is the possibility that some good password candidates
are missed as a result of this decision, this trade-off is deemed
acceptable to result in more relevant password candidates.
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V. EXPERIMENTS

To measure the impact of contextual dictionaries, a number of password cracking experiments were conducted to
compare the results of a contextual dictionary against a
commonly-used baseline dictionary. The configuration of the
experiments is described in the next section, and the results
are evaluated.
A. SETUP AND DATASETS USED

To conduct the experiments, University College Dublin’s
Sonic High-Performance Computing Cluster was used. This
cluster consists of 43 nodes with memory sizes ranging from
128Gb to 1.5Tb6 A leaked community of manga fans was
chosen as the target community for the first experiment, using
the term ‘‘manga’’ as the seed word for the generation of
the dictionaries. The evaluation of the generated password
candidate dictionaries relies on a leaked dataset from the
website MangaTraders – a forum for Manga and Anime fans.
The dataset used in this paper is made up of 618,237 unique
passwords provided by the online service hashes.org.
As a second experiment, the Comb4 dataset [32], which
consists of four datasets is used (one of which being MangaTraders). The other three datasets are Axemusic (data leak
from music forum), Jeepforum (data leak from a car forum)
and Minecraft (data leak from a video game forum). The sizes
of these dictionary lists that make up Comb4 are shown in
Table 1. The use of these datasets for the purpose of this
research has been approved by the Office of Research Ethics
in University College Dublin.
As a baseline to compare the results of these experiments
against, the RockYou dictionary has been used. There are two
publicly available versions of RockYou. The first consists of
32 million passwords with repeated password entries (providing insight to the most frequently used passwords). For the
experimentation outlined as part of this paper, the frequency
of passwords is not of use and the version of RockYou used
consists of 14 million unique passwords.
When it comes to the generated dictionaries, the seed word
used was ‘‘Manga’’ and two dictionaries of two and three
layers, called CD_2 and CD_3 respectively, were produced.
Their lengths are shown in Table 1.
For the evaluation of the results, two well-known password cracking tools, OMEN [34] and Prince7 were used.
OMEN is a password cracking tool using a Markov model
and produces password candidates in order of decreasing
probability. Prince is a password candidate generator that
uses one dictionary list to produce combinations of words
as password candidates. Depending on the length specified,
different combinations of words from the dictionary list are
concatenated to create new password candidates.
While time is dependent on the resources available for
password cracking, as a reference, using our HPC cluster,
6 https://www.ucd.ie/itservices/ourservices/researchit/researchcomputing/
sonichpc/
7 https://github.com/hashcat/princeprocessor

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FIGURE 3. Comb4 evaluated with CD_2, CD_3 and RockYou using OMEN.

FIGURE 5. Comb4 evaluated with CD_2, CD_3 and RockYou using Prince.

FIGURE 4. MangaTraders evaluated with CD_2, CD_3 and RockYou using
OMEN.

FIGURE 6. Mangatraders evaluated with CD_2, CD_3 and RockYou using
Prince.

each password cracking run with 10 billion guesses took
approximately 9-10 hours for OMEN, while with Prince it
took approximately 14-15 hours. It should be noted that
the passwords were in plain text; therefore, no hashing was
involved. The next section provides an overview of the experiments that were performed and an analysis of the results.

TABLE 1. The size of the datasets involved in the experiments.

B. EVALUATION SECTION

Both Comb4 and MangaTraders were evaluated using CD_2,
CD_3, and RockYou as input dictionaries. 10 billion password candidates were generated from each of the three evaluation dictionaries for both the OMEN and Prince attacks. The
results of the cracking progress over time for CD_2, CD_3
and RockYou with Comb4 and MangaTraders using OMEN
can be found in Figure 3 and Figure 4 respectively. Likewise,
the results of the cracking progress over time for CD_2, CD_3
and RockYou with Comb4 and MangaTraders using Prince
can be found in Figure 5 and Figure 6 respectively.
A key difference between Figures 3 and 4 (which represents OMEN) and Figures 5 and 6 (which represents Prince),
is that CD_2 is more performant compared to CD_3 using
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OMEN and CD_3 is better with Prince. The explanation
for this resides in the inner configurations of each of these
tools. For CD_2, which is significantly smaller than CD_3,
there are more variations of the same password candidate
being attempted for the constant fixed number of guesses,
i.e., 10 billion for each password cracking run. For OMEN,
which produces candidates in order of decreasing popularity,
this means that the most likely candidates will be not only
checked first, but checked with a higher number of variations, i.e., more mangling rules applied, in the case of CD_2
compared to CD_3. For Prince, which is based on combining
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FIGURE 7. Passwords cracked by OMEN with CD_2, CD_3 and RockYou,
classified by zxcvbn.

FIGURE 8. Passwords cracked by OMEN with CD_2, CD_3 and RockYou,
classified by zxcvbn.
TABLE 2. Strength Distribution using zxcvbn for CD_2, CD_2 and
RockYou, using OMEN.

dictionary words, a larger dictionary list offers a wider range
of combinations, and therefore CD_3 performs better.
As expected, RockYou performs the best using OMEN and
Prince. The reason for this is that RockYou is a 14 millionlong dictionary of real-world passwords, while CD_2 and
CD_3 are 345 and 19 times smaller, respectively. Not only is
the size difference significant, but RockYou is also a diverse
dictionary that represents to a very large extent how people
create their real-world passwords. RockYou is indicative of
the password culture in our society, which is why it is one of
the most popular dictionaries for password cracking attacks.
When comparing Figure 3 to Figure 4 and comparing
Figure 5 to Figure 6, it is notable that the number of recovered
passwords from MangaTraders is about half of what it is for
Comb4. This is particularly interesting considering the fact
that Comb4 contains 1,096,481 unique passwords, about
twice as many as MangaTraders. This means that CD_2 and
CD_3, have performed very well when the passwords they
are trying to crack are of non-identical, but similar, context.
If the number of cracked passwords is the only metric taken
into account, then RockYou is the best performer. In this case,
a larger and more diverse dictionary list performs the best and
cracks the most passwords. However, in many real world scenarios other measures of performance take precedent over the
sheer number of recovered passwords. For example, if time
is of the essence or a single, strong password needs to be
cracked, RockYou might not be a good choice.
This is why it is important to also examine other metrics.
For example, how strong are the passwords being cracked?
For this, the password strength meter zxcvbn, which is the
Dropbox-developed strength meter, has been used. According
to this meter, passwords are classified into five different
classes based on how easily they can be cracked. Class 0 is
considered the most easy to crack, while Class 4 contains the
passwords that are deemed the most difficult to crack.
Figure 7 shows how many passwords have been cracked
per zxcvbn Class for CD_2, CD_3 and RockYou using
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OMEN and Figure 8 shows the same results from using
Prince. It can be seen that for both OMEN and Prince, the
number of Class 1 passwords that have been cracked with
RockYou is very large. The reason for this is that RockYou is
a generic dictionary list of popular passwords. It is reasonable
that RockYou would perform well for passwords that are
easy to crack. With zxcvbn, passwords from Classes 0 to
2 belong to this ‘‘easy’’ category [7].
Tables 2 and 3 offer a breakdown of how many passwords
were cracked by each dictionary per class and per cracking
tool. As can be seen in Table 2, when it comes to Omen,
for Class 4 passwords, all three dictionaries did not perform
well. Nevertheless, CD_2 and CD_3 cracked almost as many
passwords as RockYou, which is an important feat, given the
discrepancy in dictionary size between the three dictionaries.
When it comes to the rest of the classes, the results are more
impressive, with the passwords found by CD_2 and CD_3
ranging between 13% and 47% of those found by RockYou
in each Class. Looking at Prince, the results are comparable
and most impressively, for Class 1, CD_3 found 80% of the
passwords that RockYou found, as can be seen in Table 3.
When it comes to Class 4, Prince was significantly better
than OMEN, and CD_2 and CD_3 recovered approximately
12% of the passwords recovered by RockYou. However, the
overlap of the results achieved using CD_2 and CD_3 versus
RockYou is not what demonstrates the true value of the
proposed approach.
If a real-world law enforcement password cracking scenario is considered, RockYou (or similar) can be used to
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TABLE 3. Strength Distribution using zxcvbn for CD_2, CD_2 and
RockYou using Prince.

TABLE 4. Passwords found by only manga 2 layers or 3 layers (OMEN)
Unique Passwords found using CD-2 and CD-3 that were not found by RY
using OMEN.

TABLE 5. Passwords only found using the contextual-based approach of
manga 2 layers or 3 layers (Prince).

crack passwords while simultaneously using the approach
proposed as part of this paper. The value of this approach lies
in the analysis of the passwords that using CD_2 and CD_3
were able to crack that using RockYou alone did not. Table 4
outlines the number of unique passwords per class that were
cracked solely by CD_2 and CD_3 respectively, and were not
cracked by RockYou using OMEN and Table 5 shows the
same for Prince. From these two tables, it can be observed
that, in fact, there is value in running the context-based dictionary attack in conjunction with RockYou.
As mentioned before, for Class 4 passwords using OMEN,
CD_2 cracked 50 passwords and RockYou cracked 64. However, what is notable about that is that 49 of those passwords
recovered by CD_2 were unique to CD_2, bringing the total
number of Class 4 passwords cracked to 113. This is an
increase of 76.5% compared to simply running RockYou.
A similar increase can be observed in the case of CD_3 in
the recovery of unique passwords for CD_3 versus RockYou.
Therefore, it can be observed that even though the absolute
numbers are low compared to more easily crackable classes
of passwords, the amount of extra passwords cracked with
custom, targeted dictionaries is substantial.
Another class with a significant number of unique passwords cracked using CD_2 and CD_3 versus RockYou is
Class 3, with a 5.7% and 5.4% increase of cracked passwords
using CD_2 and CD_3 respectively. Overall, the fact that the
extra percentage of unique passwords cracked using CD_2
and CD_3 were most significant for the two most difficult
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classes proves that the proposed approach is valid and that
targeted, contextual dictionary lists can offer a significant
advantage to the cracking process. This can be put into context even more, if we consider a digital investigation with a
tech-savvy suspect, where - if their password is vulnerable
to dictionary attacks - it’s still more likely to be Class 3 and
above.
In general, the highest increase in found passwords was
achieved with CD_3 and Prince. CD_2 achieved to find
10.1% more passwords that were not already recovered by
Rock You. For Class 1 passwords this increases to 15.5%.
This is a very significant percentage, especially considering
that - as mentioned above - the custom dictionaries performed especially well with the classes of stronger passwords. It could be argued that when time is of the essence,
the targeted approach (because the size of the dictionary list
is much smaller) could be the first tool to be used in the toolkit
of the investigator.

VI. DISCUSSION

The results of the above experiments show the impact of
context on password cracking. Humans are creatures of habit;
When choosing passwords, they tend to repeat words and
patterns and select words that are familiar and meaningful
to them. Their passwords must make sense for them so that
they can remember them more easily. Even in the case of
users choosing random words, e.g., a passphrase of four random dictionary words, the mechanism they use for password
selection does provide insight. Of course, not everyone is
like this. Many people nowadays use password managers
and let the tool generate random, therefore secure passwords,
on their behalf. Therefore, neither typical dictionary attacks
nor context-based approaches would prove effective against
them.
Nevertheless, there is merit to the proposed targeted dictionary approach. The experiments above demonstrate that,
conclusively, context matters. In the case where an investigator has information about the individual(s) that are targeted in
a case, this approach should be considered. If there is only a
single suspect and there is need to act fast, it may prove more
useful to use the proposed targeted approach first. As mentioned, RockYou is significantly larger than CD_2 and CD_3,
which means it will take longer to execute. A smaller, more
focused, bespoke dictionary using OMEN, which prioritizes
the most likely password candidates first, might be the best
option to choose in the first instance.
Of course, if the aim is to crack more than one password, other factors need to be considered, including how
customizable the list should be. Is it better to start with one
or more seed words? Is the number of passwords cracked
enough to determine success, or is there a need for other, more
sophisticated metrics, i.e., the number of passwords cracked
in a specific amount of time or the strength of the cracked
passwords? The quality of a dictionary can be measured in
several different ways depending on the desired use case [32].
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A. Kanta et al.: Novel Dictionary Generation Methodology for Contextual-Based Password Cracking

VII. CONCLUSION AND FUTURE WORK

The primary contribution of this paper is a novel framework for creating new, custom dictionary lists for any
topic of interest that may be required – leveraging the
power of structured information found on Wikipedia (and
DBPedia). This can provide the blueprint for easily creating
customized dictionary lists for any topic, combine them,
tailor them according to how deep and comprehensive they
need to be, and personalize them to the needs of each
investigation.
A desirable by-product of the proposed approach is that it
helps investigators crack passwords for topics about which
they know little or nothing. For example, investigators do
not have to know anything about the desired topic to be
able to build a custom dictionary list of the most important
words about that topic. Additionally, this dictionary generation utility helps investigators keep up with current trends in
password cracking and easily create new dictionary lists to
accommodate them.
Our experiments have demonstrated people that often,
people choose passwords related to the topic of the website/system that the password is for, or are thematically close
to that topic. Therefore, using a custom dictionary list can
offer a significant advantage to the cracking process and
ultimately result in higher success rates compared to using
a generic dictionary alone. Our experiments show that the
use of the proposed approach, in conjunction with existing
approaches, results in up to 15.5% additional passwords being
cracked over existing approaches alone. This increased likelihood of cracking a particular user’s password could mean
the difference between a digital investigation progressing or
being stuck in its tracks.
Fortunately, there are many avenues to explore to further enriching the process of creating context based dictionaries. Other sources of contextual information that can
be considered include Wiki articles, forums, and social
media. For example, a Twitter hashtag could be a good
starting point for creating a dictionary list containing
what people have to say on a specific topic right now.
It could also provide insight into the slang, colloquialisms, and common words or phrases associated with each
keyword.
Furthermore, when it comes to the structure of the resultant
dictionary list, identifying ways to prioritize candidates that
are more relevant to the seed word can prove beneficial.
Of course, because of the tree-like structure of Wikipedia,
words found in the second layer can be assumed to be closer
to the seed word than those in the third layer, but there is
room to improve this process. One avenue of future work is
to estimate how related a candidate word is to the seed word.
In doing so, the resultant list of words could be prioritized,
allowing the most related candidate words to be attempted
first. This would additionally allow for the exclusion of irrelevant words. This could also be used to augment the dictionary
with relevant words from additional sources, as mentioned
above.
VOLUME 10, 2022

Finally, in terms of optimally applying this approach in
real world scenarios, one focus for future work is to create
a bank of precomputed seed word lists generated on common
and popular topics so that they do not need to be regenerated
whenever re-encountered.
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59188

AIKATERINI KANTA (Graduate Student Member,
IEEE) received the B.Sc. degree in electrical and
computer engineering from the Democritus University of Thrace and the M.Sc. degree in digital
investigation and forensic computing from University College Dublin (UCD), where she is currently pursuing the Ph.D. degree with the School
of Computer Science. She is a Grantholder Cat.
20 at the European Commission’s Joint Research
Centre, Ispra, Italy. Her research interests include
computer security, cyber security, digital forensics, password cracking techniques, and open source intelligence. She is a member of the UCD Forensics
and Security Research Group.

IWEN COISEL received the Ph.D. degree in cryptography from the Orange Laboratories, Caen,
France, in 2009. The topic of the Ph.D. degree
was anonymous authentication systems dedicated
to low cost devices. He was with the Orange
Laboratories. He was a Researcher with the
Crypto Group, Université Catholique de Louvain,
Belgium, where he was involved in private authentication systems. He has been a Scientific/a Technical Project Officer with the Joint Research
Center, European Commission, Ispra, Italy, since 2012. His research interests
include cryptography, cybersecurity, digital forensic, machine learning, natural language processing, password cracking techniques, and password
strength metrics.

MARK SCANLON (Senior Member, IEEE)
received the M.Sc. and Ph.D. degrees in remote
digital forensic evidence acquisition. He is currently an Associate Professor with the School
of Computer Science, University College Dublin
(UCD), and the Founding Director of the UCD
Forensics and Security Research Group. He is also
a Fulbright Scholar in cybersecurity and cybercrime investigation. His research interests include
evidence acquisition, evidence whitelisting and
data deduplication, data encryption, file synchronization service forensics,
network forensics, and digital forensics education. He is a Senior Editor
of Forensics Science International: Digital Investigation journal (Elsevier)
and is a Keen Editor, a Reviewer and a Conference Organizer in the field,
including Digital Forensics Research Workshop (DFRWS).

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