Mtech Seminar Report on Achieving Flatness by Selecting Honeywords from Existing Passwords

Mtech Semiar Report
on
ACHIEVING FLATNESS: SELECTING THE
HONEYWORDS FROM EXISTING USER PASSWORDS
Submitted in partial fulﬁllment for the Award of the Degree of Masters of Technology in
Computer Science & Engineering
Submitted by
SREYA SRIDHAR P P
(Candidate Code:11)
Under the guidance of
Mrs.Ruksana M Yoonus
Assistant Professor
Department of Computer Science & Engineering
Department of Computer Science and Engineering
COLLEGE OF ENGINEERING & MANAGEMENT PUNNAPRA
KERALA
November 2017

CERTIFICATE
This is to certify that the thesis entitled “ACHIEVING FLATNESS: SELECTING THE
HONEYWORDS FROM EXISTING USER PASSWORDS ” is a bonaﬁde record of the
project work done by Sreya Sridhar P P (Candidate Code:11)under my supervision and
guidance, in partial fulﬁllment for the award of Degree of Master of Computer Science and
Engineering from the Technical University of Kerala for the year 2017.
Mrs Ruksana M Yoonus
(Seminar co-ordinator)
Assistant Professor
Dept.of Computer Science & Engineering
Dr.SureshKumar N
Assosciate Professor &Head of Department
Dept.of Computer Science & Engineering
Place:Punnapra
Date:09/11/2017
i

Acknowledgement
It is a matter of great pleasure for me to submit this seminar report on “ACHIEVING
FLATNESS: SELECTING THE HONEYWORDS FROM EXISTING USER PASS-
WORDS” as a part of curriculum for the award of Degree of Masters of Technology in
Computer Science & Engineering.
First and foremost, I wish to place on record my ardent gratitude to my seminar co-
ordinator Mrs.Ruksana Myoonus, Assistant Professor, Department of Computer Science
& Engineering. Her tutelage and guidance was the leading factor in translating my efforts
to fruition. Her prudent and perspective vision has shown light on my trial to triumph.
I am extremely happy to mention a great word to DR.SureshKumar N, Head of Depart-
ment of CSE for providing me with all facilities for the completion of this work.
Finally yet importantly, I would like to express my gratitude to Prof, the principal of my
institution for providing me with all the facilities for completion of this work.
I would also extend my gratefulness to all the staff members in the Department. I am
also thankful to all my friends and well wishers who greatly helped me in my endeavour.
SREYA SRIDHAR.P.P
11
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Abstract
Disclosure of password files is a severe security problem that has affected millions of users
and companies like Yahoo, RockYou, LinkedIn, eHarmony and Adobe. Leaked passwords
make the users target of many possible cyber- attacks. Here a simple but clever idea behind
the study is the insertion of false passwords called as honeywords associated with each
user’s account. If honeywords are selected properly, a cyber-attacker who steals a file of
hashed passwords cannot be sure if it is the real password or a honeyword for any account.
Hence, when an adversary tries to enter into the system with a honeyword, an alarm is
triggered to notify the administrator about a password leakage.
At the expense of increasing the storage requirement by 20 times, here introduce a
simple and effective solution to the detection of password file disclosure events. In this
study, introduce the honeyword system and present some remarks to highlight possible
weak points. Also, it suggest an alternative approach that selects the honeywords from ex-
isting user passwords in the system in order to provide flat honeyword generation method.
It also reduce storage cost of the honeyword scheme II.
ii

Contents
1 Introduction 1
2 Problem Identification 3
3 Literature Survey 4
3.1 Chaffing-by-Tweaking 5
3.1.1 Chaffing-by-tweaking-digits 5
3.2 Chaffing-with-a-Password-Model 7
3.2.1 Modeling syntax 7
3.2.2 simplex model 8
3.3 Chaffing with Tough Nuts 8
3.4 Chaffing with Hybrid Method 9
4 Achieving Flatness: Selecting the Honeywords from Existing User Passwords 11
4.1 Initialization 11
4.2 Registration 12
4.3 Honeychecker 13
4.4 Login process 14
5 Security Analysis 15
5.1 DoS Attack 15
5.2 Password Guessing 16
5.3 Brute-Force Attack 17
5.4 Same User in Multiple Systems 17
6 Comparison of Password Generation Models 19
6.1 Storage Cost 20
iii

6.2 DoS Resistance 20
6.3 Flatness 21
6.4 Usability 21
7 Conclusion 22
References 23
iv

List of Figures
1 Password File F1 for the proposed model. 12
2 Password File F2 for the proposed model. 12
v

Achieving Flatness: Selecting the Honeywords from Existing User Passwords
Chapter 1
1 Introduction
Nowadays the Password file disclosure is one of the major security problem that has
affected many of users and companies. Passwords are a notoriously weak authentication
mechanism. Users frequently choose poor passwords. An adversary who has stolen a file of
hashed passwords can often use brute-force search to find a password p whose hash value
H(p) equals the hash value stored for a given user’s password, thus allowing the adversary
to impersonate the user.
These recent events have demonstrated that the weak password storage methods are
currently in place on many web sites. For example, the LinkedIn passwords were using
the SHA-1 algorithm with out a salt and similarly the passwords in the eHarmony system
were also stored using unsalted MD5 hashes [3]. Indeed, once a password file is stolen, by
using the password cracking techniques like the algorithm of Weir et al [3]. It is easy to
capture most of the plaintext passwords. The security of many computer systems hinges
on the secrecy of a single word if an adversary obtains knowledge of a password, they will
gain access to the resources controlled by this password. Human users are the ‘weakest
link’ in password control, due to our propensity to reuse passwords and to create weak
ones. Policies which forbid such unsafe password practices are often violated, even if these
policies are well-advertised.
There are two issues that should be considered to overcome these security problems:
First, passwords must be protected by taking appropriate precautions and storing with their
hash values computed through salting or some other complex mechanisms. Hence, for an
adversary it must be hard to invert hashes to acquire plaintext passwords. The second point
is that a secure system should detect whether a password file disclosure incident happened
or not to take appropriate actions. In this study, focus on the latter issue and deal with
Department of Computer Science And Engineering 1

fake passwords or accounts as a simple and cost effective solution to detect compromise of
passwords. Honeypot is one of the methods to identify occurrence of a password database
breach. In this approach, the administrator purposely creates deceit user accounts to lure
adversaries and detects a password disclosure, if any one of the honeypot passwords get
used [6], [7]. This idea has been modiﬁed by Herley and Florencio [6] to protect online
banking accounts from password brute-force attacks. According to the study, for each user
incorrect login attempts with some passwords lead to honeypot accounts, i.e., malicious
behavior is recognized. For instance, there are 108 possibilities for a eight-digit password
and let system links 10,000 wrong password to honeypot accounts, so the adversary per-
forming the brute- force attack 10,000 times more likely to hit a honeypot account than the
genuine account.
In this study, analyze the honeyword approach and give some remarks about the se-
curity of the system. Furthermore, here point out that the key item for this method is the
generation algorithm of the honeywords such that they shall be indistinguishable from the
correct passwords. Therefore, here propose a new approach that uses passwords of other
users in the system for honeyword sets, i.e., realistic honeywords are provided. Moreover,
this technique also reduces the storage cost compared with the honeyword method in [2].

Chapter 2
2 Problem Identiﬁcation
The honeyword mechanism works simply as follows: For each user ui, the sweetword
list Wi, is generated and is stored in the database. Here the storage requirement is very
large because each user account is associated with multiple password hashes. And the
effectiveness of the honeyword system directly depends on how Gen() ﬂatness is provided
and how it is close to human behaviour in choosing passwords. And also this honeyword
method is affected by various security issues. In this study, analyse the honeyword ap-
proach and give some remarks about the security of the system. Therefore, here propose
a new approach that uses passwords of other users in the system for honeyword sets, i.e.,
realistic honeywords are provided. Moreover, this technique also reduces the storage cost
compared with the honeyword method .

Chapter 3
3 Literature Survey
This chapter summarize the major works done in earlier times to generate the honey-
words for each user accounts. Here categorize the honeyword generation methods into
two groups. The first category consists of the legacy-UI (user interface) procedures and
the second one includes modified-UI procedures whose password-change UI is modified to
allow better password/honeyword generation. Take-a-tail method is given as an example of
the second category. According to this approach a randomly selected tail is produced for
the user to append this suffix to her entered password and the result becomes her new pass-
word. For instance, let a user enter password games01, and then system let propose ’413’
as a tail. So the password of the user now becomes games01413. Although this method
strengthens the password, but it is impractical some users even forget the passwords that
they determined. Therefore in the remaining parts, the analysis that we conducted is limited
with the legacy-UI procedures.
In a legacy-UI method, the password-change procedure asks the user for the new pass-
word (and perhaps asks her to type it again, for confirmation). The UI does not tell the user
about the use of honeywords, nor interact with her to influence her password choice. Here
discuss the four methods of honeyword generation [2].
• Chaffing-by-Tweaking
• Chaffing-with-a-Password-Model
• Chaffing with “Tough Nuts”
• Hybrid Method

3.1 Chaffing-by-Tweaking
In chaffing the password p i is picked, and then the honeyword generation procedure Gen(k,
p i ) or “chaff procedure” generates a set of k-1 additional distinct honeywords (“chaff”).
Note that the honeywords may depend upon the password p i . The password and the
honeywords are placed into a list W i , in random order. The value c(i) is set equal to the
index of p i in this list.
In the chaffing-by-tweaking, the user password seeds the generator algorithm which
tweaks selected character positions of the real password to produce the honeywords. The
honeywords are then obtained by tweaking the characters in the selected t positions: each
character in a selected position is replaced by a randomly-chosen character of the same
type: digits are replaced by digits, letters by letters, and special characters (anything other
than a letter of a digit) by special characters. The success of chaffing depends on the quality
of the Chaff generator ; the method fails if an adversary can easily distinguish the password
from the honeywords. The positions to be tweaked should be chosen solely on the pattern
of character types in the password, and not on the specific characters in the password,
otherwise the password might be easily determined as the only sweetword capable of giving
rise to the given list W i . Following some examples of chaffing by tweaking method.
3.1.1 Chaffing-by-tweaking-digits
In this method choosing the last digit position and the last special character position. With
“chaffing-by-tweaking-digits” the last t positions containing digits are chosen. (If there are
less than t digits in the password, then positions with non-digits could also be selected as
needed.) For example, by using the last technique for the password 42hungry and t=2, the
honeywords 20hungry and 32hungry may be generated. Many users have the propensity
to choose the numbers included in passwords related to a special date, e.g. birthday, an-
niversary or an important historical event. For example 3.6 percent of the hacked Adobe

password hints are related to a date [8]. In the light of this fact, it is highly possible that
such a password involves a digit sequence like 19xx, 20xx or xx where xx represents the
last two digits of the date. For those passwords by applying the chafﬁng-by-tweaking-digits
method, the date digits will be replaced with the randomly selected digits. Hence an ad-
versary who has W i of a user u i may easily identify honeywords and recover the correct
password. When examine publicly available leaked passwords hacked from RockYou web-
site (approximately 32 million entries) [9], it observe that the passwords of the numerous
users include such a pattern, e.g. junexxxx pattern is selected as a password by 1,244 users,
where xxxx is a date and starts with 19 or 20. Another example should be the password
alex1992 which is seen 47 times in the RockYou password list: Suppose the following
honeywords are generated with t= 4 and k = 9 for this password. Note that the digits in
the honeywords seem not relevant, but the correct password alex1992 makes sense for an
adversary.
alex6323 alex9058 alex1992
Many users prefer to append consecutive numbers to their password heads, like ’123’,
’1234’, due to the tendency of users to choose rememberable number patterns. By con-
sidering the RockYou leaked password database, realize that about 0:8 percent of all user
passwords—excluding the ones in the top 1,000—ends with ’123’ or ’1234’ and begins
with letters at least one length. The vulnerability issued with the date patterns described
above is also valid in those passwords, i.e., an adversary may distinguish the correct pass-
word from the sweetwords by just investigating the end digit patterns. Indeed, the chafﬁng
by tweaking method suffers from these types of passwords, because replacing characters
of the same type randomly will give the same hint to an adversary in extracting the cor-
rect password. Similar patterns and examples of user habits in digit selections can be ex-

tended. From a broader perspective, these examples show that users mostly do not choose
the digits or letters in passwords randomly, so a randomly replacing technique like this
model leads an adversary to make a natural selection. In particular, believe that deploying
the “chaffing-by-tweaking” model, it is hard to fulfill aims of the honeyword scheme, i.e.,
all the adversary needs to do is to have a human sense.
3.2 Chaffing-with-a-Password-Model
This method generates honeywords using a probabilistic model of real passwords; this
model might be based on a given list L of thousands or millions of passwords and perhaps
some other parameters. (Note that generating honeywords solely from a published list L as
honeywords is not in general a good idea: such a list may also be available to the adversary,
who could use it to help identify honeywords.). Unlike the previous chaffing methods, this
method does not necessarily need the password in order to generate the honeywords, and it
can generate honeywords of widely varying strength. It consists of mainly two methods
3.2.1 Modeling syntax
In this scheme, the password is parsed into a sequence of tokens, each representing a dis-
tinct syntactic element or a word, number, or set of special characters. For example, the
password,
mice3blind
Might be decomposed into the token sequence W4—D1—W5, meaning a 4-letter word
followed by a 1-digit number and then a 5-letter word. Honeywords are then generated by
replacing tokens with randomly selected values that match the tokens. For example, the
choice W4 gold, D1 5, W5 rings would yield the honeyword.
gold5rings

Replacements for word tokens are selected from a dictionary provided as input to the
generation algorithm.
3.2.2 simplex model
Simple model generates honeywords through a password list: First a password list L is built
by combining numerous real passwords and random passwords of varying lengths. Then a
random word is picked from the list with a length of d. Moreover, with a probability of 0.8
some honeywords are generated as ”tough nuts” which will be explained in the next part.
honeyword characters are created by replacing characters of randomly selected words of
L in a probabilistic manner Leaked password databases have shown that some passwords
have a well-known pattern. For example all of the following passwords are involved in the
list of 10,000 most common passwords [10].
bond007 007bond
james007 007007
Considering the modeling syntax method, one can conclude that the honeyword sys-
tem losses its effectiveness against such passwords, i.e., the correct password has become
noticeably recognized by an adversary. In fact, this problem seems an inherent weakness
of randomly replacement based honeyword methods. Since character groups or individ-
ual characters are replaced by a picked character/characters, the content integrity of such
passwords would be broken and the correct password becomes quite salient.
3.3 Chafﬁng with Tough Nuts
In this method, the system intentionally injects some special honeywords, named as tough
nuts, such that inverting hash values of those words is computationally infeasible, e.g. ﬁxed
length random bit strings should be set as the hash value of a honeyword. An illustrative ex-
ample for a tough nut is given in [9] as ’9,50PEe[KV.0?RIOtcL-:IJ”b+Wol !*]! NWT/pb’.

It is stated that the number and positions of tough nuts are selected randomly. By means
of this, it is expected that the adversary cannot seize whole sweetword set and some sweet-
words will be blank for her, thereby deterring the adversary to realize her attack. In such a
situation the adversary may pause before attempting login with cracked passwords.
Tough nuts are recommended to be used together with other methods to render the ad-
versary’s work more challenging and exhaust the attacker. Nevertheless, it has remained
an open question, what is the optimal strategy for an adversary when tough nuts are ex-
perienced. from this infer that “tough nuts” method is a double-edgedsword: Numerous
unknowns in the password list may discourage an adversary to proceed mounting her at-
tack. On the other hand, an adversary may suppose that most of the passwords made up
of simple words and digit combinations, not a tough nut. Hence, it is reasonable for this
adversary to conduct her classic attack with skipping tough nuts contrarily to authors’ ex-
pectations. Note that for this attack strategy, entropy contributed by the honeywords is
decreased, because the tough nuts are ignored by the adversary.
3.4 Chaffing with Hybrid Method
It is possible to combine the benefits of different honeyword generation strategies by com-
posing them into a “hybrid” scheme. Hybrid method is combining the strength of differ-
ent honeyword generation methods, e.g. chaffing-with a password-model and chaffing-
by-tweaking-digits. By using this technique, random password model will yield seeds
for tweaking-digits to generate honeywords. For example let the correct password be ap-
ple1903. Then the honeywords angel2562 and happy9137 should be produced as seeds to
chaffing-by-tweaking-digits. For t = 3 and k = 4 for each seed, the sweetword table given
below may be attained: Here is a simple hybrid scheme:
• Use chaffing-with-a-password-model on user-supplied password p to generate a set of a
( 2) seed sweetwords W’, one of which is the password. Some seeds may be “tough nuts.”

• Apply chafﬁng-by-tweaking-digits to each seed sweetword in W’ to generate b (( 2) 2)
tweaks (including the sweetword itself). This yields a full set W of k = a b sweetwords.
• Randomly permute W. Let c(i) be the index of p such that p = w c (i), as usual. Because
the hybrid method is legacy-UI, achieves excellent ﬂatness under reasonable assumptions,
and provides resistance to DoS attacks, it is recommended honeyword generation method.

Chapter 4
4 Achieving Flatness: Selecting the Honeywords from
Existing User Passwords
In order to handle the defects of honeyword generation method, a new method of se-
lecting the honeywords from existing user passwords are proposed. This for each account
k-1 existing password indexes, which we call honeyindexes, are randomly assigned to a
newly created account of u i , where k greter than or equal to 2. Moreover, a random index
number is given to this account and hash of the correct password is kept with the correct
index in a list. In another list u i is stored with an integer set which is consisted of the
honeyindexes and the correct index. The tentative password indexes hamper an adversary
to make a correct guess and she cannot be easily sure about which index is the correct one.
It is equivalent to say that to create uncertainty about the correct password, propose to use
indexes that map to valid passwords in the system. This involves different steps as follows.
4.1 Initialization
First, T fake user accounts (honeypots) are created with their passwords. Also an index
value between [1, N], but not used previously is assigned to each honeypot randomly. Then
k-1 numbers are randomly selected from the index list and for each account a honeyindex
set is built like X i =(xi,1,xi,2, . . . x,i,k) one of the elements in Xi is the correct index
(sugarindex) as ci. Now, use two password ﬁles as F1 and F2 in the main server: F1 stores
username and honeyindex set, (h u i, X i ) pairs as shown. Where h u i denotes a honeypot
account. Here each entry has two elements. The ﬁrst one is the username of the account
and the second element is honeyindex set for the respective account. F2 keeps the index
number and the corresponding hash of the password,(ci, H(pi) ), as depicted. Let SI

Figure 1: Password File F1 for the proposed model.
Figure 2: Password File F2 for the proposed model.
denote the index column and SH represent the corresponding password hash column of F2
. Then the function f(ci) that gives password hash value in SH for the index value ci can be
deﬁned as: f (ci) = H(pi)SH less than ci, H(pi) greater than stored pair of ui and ci SI.
4.2 Registration
After the initialization process, system is ready for user registration. In this phase, a legacy-
UI is preferred, i.e., a username and password are required from the user as ui , pi to register
the system. here use the honeyindex generator algorithm Gen(k,SI) ci ,Xi , which outputs
ci as the correct index for ui and the honeyindexes Xi =(xi,1,xi,2, . . . x,i,k). Gen(k,
SI )produces Xi by randomly selecting k- 1 numbers from SI and also randomly picking
a number ci .SI. so ci becomes one of the elements of Xi . One can see the generator

algorithm Gen(k, SI) is different from the procedure described in the previous method,
since it outputs an array of integers rather than a group of honeywords. Last, periodically
honeyindexes of each account should be regenerated.
Periodically honeyindexes of each account should be regenerated. As the number of users
in the system increases to provide uniform distribution of honeyindexes across SI , fresh
honeyindex set must involve numbers from this new larger list. Otherwise, passwords of
newly created accounts would not be used as honeywords in the system and it may give
a clue to the adversary to in guessing the correct password of these new accounts. Note
that within a uniform distribution each password is assigned as a honeyword about k times,
because there are N passwords but Nk honeywords are needed.
4.3 Honeychecker
The system may utilize an auxiliary secure server called the honeychecker” to assist with
the use of the honeywords. Since we are assuming that the computer system is vulnerable
to having the ﬁle F of password hashes stolen, one must also assume that salts and other
hashing parameters can also be stolen. Thus, there is likely no place on the computer
system where one can safely store additional secret information with which to defeat the
adversary. The honeychecker is thus a separate hardened computer system where such
secret information can be stored. assume that the computer system can com- municate with
the honeychecker when a login attempt is made on the computer system or when a user
changes her pass word. Assume that this communication is over dedicated lines and/or
encrypted and authenticated. The honeychecker should have extensive in- strumentation to
detect anomalies of various sorts. The auxiliary service honeychecker is employed to store
correct indexes for each account and assume that it communicates with the main server
through a secure channel in an authenticated manner. The role and primary process of
the honeychecker is ensure security. It is a hardened computer system where such secret

information can be stored. , except that ( i, c i )pair is replaced with ( u i , c i )pair in our
case. The honeychecker executes two commands sent by the main server:
Set: c i ,u i
Sets correct password index c i for the user u i
Check: u i , j Checks whether u i for u i is equal to given j Returns the result and if equality
does not hold, notifies system a honeyword situation.
Depending on the policy chosen, the honeychecker may or may not reply to the com-
puter system when a login is attempted. When it detects that something is a miss with the
login attempt, it could signal to the computer system that login should be denied. On the
other hand it may merely signal a silent alarm to an administrator, and let the login on the
computer system proceed. In the latter case, call the honeychecker a login monitor rather
than a honeychecker.
4.4 Login process
System first checks whether entered password, g, is correct for the corresponding user-
name u i .To accomplish this, first the Xi of the corresponding u i is attained from the F1
file. Then, the hash values stored in F2 file for the respective indices in Xi are compared
with H(g) to find a match. If a match is not obtained, then it means that g is neither the
correct password, nor one of the honeywords, i.e., login fails. On the other hand, if H(g)
is found in the list, then the main server checks whether the account is a honeypot. If it
is a honeypot, then it follows a predefined security policy against the password disclosure
scenario. Honeychecker controls whether j= c i and returns the result to the main server. At
the same time, if it is not equal, then it assured that the proffered password is a honeyword
and adequate actions should be taken depending on the policy.

Chapter 5
5 Security Analysis
Here discuss the security of the proposed model against some possible attack scenarios.
Suppose that the adversary can invert most or many of the password hashes in ﬁle F2. It
leads to the Introduction of a DoS attack sensitivity. In which an adversary deliberately
tries to login with honeywords to trigger a false alarm. Hence, the suggested policies given
below mostly focuses on minimizing the DoS vulnerabilities.
• When a user logins with a wrong password , If this wrong password is the password
of another account in the system and the same user hits this situation more than once the
system should turn on additional logging of the user’s activities to detect a possible DoS
attack and to attribute the adversary, besides the incorrect login attempt case proceeds as
usual.
• If a password, whose hash value is in the SH of the F2, is entered in wrong login attempts
for more than once, the system should take actions against a possible DoS alarm.
• In order to increase the number of unique passwords in the system, i.e., reduce common
passwords, users should be forced to adhere to a password-composition policy like basic8
(eight or more characters), in the password creation .The main reason behind this item is to
minimize the number of common passwords in the system.
• A username should not be correlated with its password.
• To avoid occurrence of a high number of common passwords in the system.
5.1 DoS Attack
Under this attack scenario the adversary does not have the password ﬁles and their contents.
Her main purpose is to trigger a false alarm and to raise a honeyword alarm situation, i.e.,

depending on the policy some or all parts of the system may be out of service or disabled
unnecessarily. Suppose that the adversary has knowledge m +1 username and respective
passwords in the system. Method for attacking the system is creating m accounts with
the same password as pz, while a single account, u y , has different password like py
and entering the system with the username uy and the password pz. If pz is assigned by
the system as a honeyword, then the adversary mounts a DoS attack by entering with the
system ( u y , p z ) pair LetP r(p z W y ) the probability that P z is assigned as one of
the honeywords for u y it is also the success probability of the adversary for this attack.
Since there are N-m passwords different from p 1 z and k honeywords are assigned to each
account. When a password in the system is entered incorrectly by the same username or
different usernames for more than once, the system suspects about a DoS attack and a DoS
alarm should be triggered depending on the next activities of the user. Hence, the adversary
cannot increase her chance by making more trials for the same known password p z without
being noticed by the system.
5.2 Password Guessing
In this attack, assume that the adversary has plundered password files F1 and F2 from the
main server and also obtained plaintext passwords by inverting the hash values. Extracted
F2 file (after inverting hashes) gives ( indexnumber; password ) pairs to the adversary, but
they are not directly connected to a specific username. By just analyzing this, she cannot
exactly determine which password belongs to which user. If the adversary randomly picks
an account from the list in F1 and then tries to login with a guessed password, then her suc-
cess will depend on: First, the selected account is not a honeypot account. Second guessing
the correct password pi out of k sweetwords. Otherwise, the adversary will be caught by
the system due to a honeyword or a honeyspot. Let Pr(success) represent the probability
that the adversary makes a correct guess for a randomly picked username. Below, express

the probability that the adversary, who makes random trials, is not detected by the system,
suppose the number of honeypots in the system is T: equation given as The attacker picks
password pi with 5 percent probability. Conversely, the adversary will caught by the system
in password guessing attack with a chance of 95 percent, as long as the password does not
carry any information about the username.
5.3 Brute-Force Attack
A honeypot account is attempted then system follows a strong policy e.g. demanding all
users to renew their passwords. From binomial distribution the probability that the adver-
sary hits at least one honeypot in her trials is P r(hit greter than 1) = 1 - (N - T /N ). Even
in this case our approach provides resistance against such an attack, because for T =1000,
N =1000000 values this probability tends to 0:5. It is equivalent to say that in brute-force
guess attack, it is likely that the adversary hits a honeypot and system detects the password
disclosure situation.
5.4 Same User in Multiple Systems
The attack scenarios such that a user reuse passwords on two different systems as A and
B are investigated. In [2], the attack scenarios such that a user reuse passwords on two
different systems as A and B are investigated. For example suppose A uses honeywords
and B has prevalent password storage techniques and a target user ui shares her password
across these two systems. In this case, if an adversary compromises B, it is apparent that
honeywords assigned for this user in A contributes nothing at all. Conversely, if the adver-
sary pilfers passwords from A, she can try all sweetwords of the common user u i in A to
verify which is the correct password by submitting to B. If a honeyword is entered to B, it
results in an incorrect password screen, while the adversary successfully logins in case of
the correct password.

This model is also vulnerable these scenarios: Indeed, if the password is not same but cor-
related for a user in two distinct domains, then ﬁrst scenario may be still valid. For example
a user has password bond007 in B which does not use honeywords. On the other side same
user has password james007 in domain A which assigns honeywords to these user. Then it
is highly possible that an adversary extracts the correct password from the sweetwords, if
she has knowledge of bond007. So, both of the original method and our approach cannot
provide resistance against such conditions, as long as users select same or highly correlated
passwords in different domains.

Chapter 6
6 Comparison of Password Generation Models
In this section, detail a comparison of the generation methods including the new model
with respect to storage cost, DoS resistance and flatness of each algorithm. In a tradi-
tional password based system with a number of N users, an adversary may have at most N
user/password pairs through a cracking process. Suppose that all cryptographic efforts in
recovering a plain text word from the respective hash string stored in password hash file is
represented as a single hash inversion operation. Then, by considering the traditional sys-
tem, for each user the attacker has to perform one hash inversion operation, while for the
whole system N operations must be executed. According to the Juels and Rivest’s method
[2] she needs to launch k operations for a specific user and kN operations for the whole
space, since for each user k sweetwords are assigned. Thus, one can see that the adversary
has to spend k times more effort for each case in this method. Now, archiving flatness,
if the attacker focuses on a specific user, she must still try k hash inversions to reveal all
possible passwords for this target user. However, since each honeyword is indeed password
of another user, revealing all passwords in the system requires N operations as in the case
of the traditional system. Therefore, taking the total password-cracking cost of the adver-
sary for the whole system into account, thus say that Juels and Rivest’s model requires
higher effort for an attacker in retrieving plaintext passwords. Notice that, this feature of
our model at worst reduces total hash inversion work to it was at the traditional model. This
paper discusses what can be done in terms of detecting the password disclosure, when the
whole plain text forms of the passwords are available to an adversary, rather than dealing
with making the adversary’s work harder in getting plain text passwords.

6.1 Storage Cost
A typical password file system requires hN plus storage for usernames, where N stands for
the number of users in the system and h denotes length of password hash in bytes. On the
other hand this is khN for , where k denotes the number of the sweetwords assigned to each
account. Notice that here ignored the storage cost stemmed from usernames, since it is
not changed after adaptation of the honeywords what can be done in terms of detecting the
password disclosure, when the whole plaintext forms of the passwords are available to an
adversary. For this approach assume that each index requires 4 bytes and the storage cost
becomes,
4kN + hN + 4N.
To measure the gain in storage compared to original method, give the ratio as:
(4kN + hN + 4N)/khN
6.2 DoS Resistance
Probability of a common password is assigned as a honeyword for a specific user will be
negligibly low.use of real passwords as honeywords does not cause a DoS weakness. This
proposed scheme uses password list in the system as honeywords of a user. However as
the adaptation of a strong password composition policy likely prevents occurrence of com-
mon passwords in high numbers, i.e., probability of a common password is assigned as a
honeyword for a specific user will be negligibly low. Although, an adversary may hit a real
password using a common password in the system, it is not necessarily a honeyword for the
corresponding account. Thus, use of real passwords as honeywords does not cause a DoS
weakness. Last but not least issue is that in achiving flatness in addition to honeywords,
honeypots are employed to detect a password disclosure. This facilitates showing a strong
response to actions of an adversary, because entering a honeypot account with one of its

sweetwords ensures occurrence of a password leakage.
6.3 Flatness
For the proposed model as described previously passwords of other users become honey-
words for a user. Hence, this model satisfies perfect flatness as long as the correct password
is not correlated with username as pointed in previously. And investigation of a target user
profile (age, gender, religion etc.) gives no advantage to an adversary in password guess-
ing. Comparing our method with the simple model, one can see that our method is better
than the latter in terms of flatness: The honeywords in the former carry all characteristics
of the real passwords in the same system, while the simple model generates honeywords
artificially despite using real passwords of different list.
For example, it is well known that users choose segregate their passwords for more- secure
and low-secure sites , it is presented that reuse rate of weaker passwords is higher than those
of stronger passwords, since the stronger ones are usually created for higher- security sites
e.g. banking accounts. Consequently, a password list from a lower-security site password
list which is used in the simple model for a higher-security site may not be natural.
6.4 Usability
In this part, compare the proposed approach with the simple model in terms of practicality
and ease of use. By considering the simple model whose password list is constructed with
composition of numerous real passwords and randomly generated passwords, one can argue
about how the real password source is provided. If the same resource of real passwords is
used in different sites, similar inherited weaknesses related to honeyword generation may
be observed. Nonetheless, if use of publicly available password lists is forbidden , then
it will not be easy to get required large number of real passwords. Conversely, proposed
approach does not need to use an external real password resource in honeyword generation.

7 Conclusion
In conclusion, In this study, analyzed the security of the honeyword system and ad-
dressed a number of flaws that need to be handled before successful realization of the
scheme. In this respect, point out the strength of the honeyword system directly depends
on the generation algorithm, i.e., flatness of the generator algorithm determines the chance
of distinguishing the correct password out of respective sweetwords.
Another point that would like to stress is that defined reaction policies in case of a
honeyword entrance can be exploited by an adversary to realize a DoS attack. This will be
a serious threat if the chance of an adversary in hitting a honey-word given the respective
password is not negligible. To combat such a problem, also known as DoS resistance,
low probability of such an event must be guaranteed. This can be achieved by employing
unpredictable honeywords or altering system policy to minimize this risk. Hence, noted that
the security policy should strike a balance between DoS vulnerability and effectiveness of
honeywords
This model satisfies perfect flatness as long as the correct password is not correlated
with username as pointed in previously. The honeywords in the former carry all char-
acteristics of the real passwords in the same system, while the simple model generates
honeywords artificially despite using real passwords of different list. If the same resource
of real passwords is used in different sites, similar inherited weaknesses related to honey-
word generation may be observed. proposed approach does not need to use an external
real password resource in honeyword generation, rather it just feeds itself. From the anal-
ysis the proposed method will claim that this approach is simpler and more practical for
implementation.

References
[1] Erguler, ” Achieving Flatness : Selecting the Honeywords from Existing User Pass-
words ,” in Proc. IEEE Transactions on Dependable And Secure Computing , 2016, Vol.
13, No. 2.
[2] A. Juels and R. L. Rivest, “Honeywords: Making password cracking detectable,” in
Proc. ACM SIGSAC Conf. Computer Communication. Security, 2013, pp. 145–160.
Comput. Linguistics. , vol. 35, no. 3, pp. 399-433, 2009.Department of Computer Sci-
ence, University of Arizona: Tucson, AZ, USA, 1994.
[3] H. Bojinov, E. Bursztein, X. Boyen, and D. Boneh, “Kamouﬂage: Loss resistant pass-
word management,” in Proc. 15th Eur. Conf. Res. Computer Security, 2010, pp. 286–302.
[4] F. Cohen, “The use of deception techniques: Honeypots and decoys,” Handbook In-
form. Security, vol. 3, pp. 646–655, 2006.
23

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