




































 

 

            ISSN : 2693 6356 

       2023 | Vol 6 | Issue 5 

 

Using a Circular Queue Data Structure for mLMS Application 

Security Analysis 

Dr.M.Shanmugapriya, 

Assistant Professor  , Shree Venkateshwara Arts and Science 

(Co-education) College, Gobichettipalayam. 

 

 
ABSTRACT 

The rapid development of mobile technology and its applications have transformed society into one that is 

increasingly interdependent in recent years. It helps individuals in several ways to improve the quality of 

their everyday lives. The introduction and widespread use of mobile technology has facilitated rapid 

communication, increased knowledge, and brought together previously isolated groups of people. In our 

suggested architecture, the Learning Management System (LMS) serves as the backend server for a 

Mobile Learning application (M-Learning) that prioritizes data protection. Today's students may choose 

from a plethora of M-Learning options, yet many of them are unsafe. Protecting information from 

malicious actors is an ongoing problem. It is crucial to have solid security in order to counteract the 

various hacker attempts. The data is protected from malicious users by using Circular Queue (CQ). Data is 

encrypted using logical and shift operations to produce an unbreakable ASCII binary representation. The 

Multiple Circular Queue Algorithm (MCQA) is 50% simpler, but this one is 50% simpler yet. The 

suggested approach is more versatile and may be used in any situation since it encrypts data in addition to 

using a Fibonacci structure and a variable format. 
Key terms: Security, Fibonacci numbers, binary numbers, MCQs, mobile learning platforms, and 

ASCII text. 
 

 

 

 

 

 

I. INTRODUCTION 

New learning modes have emerged thanks to the 

exponential growth of information technology. 

M-Learning allows for group study and 

productive dialogue between students and 

teachers. The M-Learning App makes it possible 

for students to study almost anywhere, at any 

time. Students are more engaged and inspired to 

take on a wider range of coursework. More 

benefits to the students are attributed to M-

Learning than to E-Learning in the areas of 

education, design, learning format, and a well-

thought-out User Interface (UI). While M-

Learning opens up exciting new avenues of 

study, it also raises serious security issues 

among educational institutions and their 

students. Due to the prevalence of mobile device 

usage among students using the M-Learning 

Application, concerns have been raised about the 

safety and confidentiality of student information. 

The primary focus of M-learning has been on the 

creation and distribution of courses, with little 

thought given to the safety and confidentiality of 

student information. It became clear from a 

review of the available information that 

researchers were unconcerned about the safety 

of M-Learning. The paper's contribution is a 

framework for protecting the M-Learning 

platform against malicious actors. Cryptography, 

stenography, watermarking, and data integrity 

methods are used to protect the data. 

Cryptographic algorithms are first categorized as 

either symmetric or asymmetric. When using a 

symmetric approach, both the sender and the 

recipient share the same secret key. The 

encryption and decryption keys in an 

asymmetrical algorithm are different. All 

sensitive data is encapsulated under a "cover 

variable" while using stenography. When data is 

accessed, watermarking verifies the legitimacy 

of the source. The data integrity tool serves to 

verify the accuracy of the data. Hash, Message 

Authentication Code (MAC), and Digital 

Signature are all included. These cryptographic 

protocols allow for secure data transit and user-

to-user interaction. Users' communications are 

protected in this way. In The approach we 

propose for protecting M-Learning data is based 

on a notion of a circular queue. It has several 

potential uses, including in M-Learning, 

networking, and messaging services, to name a 



 

 

few. As the world of information technology 

evolves rapidly, it opens up new learning modes. 

M-Learning allows for group study and 

productive dialogue between students and 

teachers. The M-Learning App makes it possible 

for students to study virtually anywhere, 

anytime. It piques their curiosity and encourages 

them to explore a wide range of subjects. More 

benefits to the students are attributed to M-

Learning than to E-Learning in the areas of 

education, design, learning format, and a well-

thought-out User Interface (UI). The educational 

institutions and students have serious worries 

about the risks and assaults over updating the 

data, despite the fact that M-Learning gives new 

learning techniques. Due to the prevalence of 

mobile device usage among students using the 

M-Learning Application, there is a potential risk 

to students' personal information and right to 

privacy. The primary focus of M-learning has 

been on the creation and distribution of courses, 

with little thought given to the safety and 

confidentiality of student information. By 

looking at the several studies, we can see that 

experts aren't too worried about safety concerns 

in M-Learning. The paper's contribution is a 

framework for protecting the M-Learning 

platform against malicious actors. Cryptography, 

stenography, watermarking, and data integrity 

methods are used to protect the data. The 

cryptographic algorithm is first categorized as 

either asymmetric or symmetric. The symmetric 

approach uses a shared secret key between the 

sender and the recipient. Two separate 

encryption and decryption keys make up the 

asymmetrical algorithm. For privacy reasons, 

stenography always places the sensitive data 

within the cover variable. When information is 

accessed, watermarking verifies the legitimacy 

of the source. Data integrity serves as a method 

for ensuring the correctness of data. Hash, 

Message Authentication Code (MAC), and 

Digital Signature are all included. These 

cryptographic protocols allow for secure user-to-

user data transfers and communications. It 

ensures the privacy of each user's 

communication. Our suggested model employs 

an approach based on a circular queue data 

structure to protect M-Learning information. It 

has a wide range of potential uses, including M-

Learning, networking, and messaging.The other 

sections of this study are structured as follows: a 

literature review in Section II, a detailed 

explanation of our suggested model in Section 

III, a discussion of the experimental findings and 

results in Section IV, and a summary and 

conclusion in Section V. 

 

II. LITERATURE REVIEW 

Matetic et al [1], the paper presents the 

discovery of knowledge about the process of 

learning using batch data analysis performed 

by Artificial Neural Networks (ANNs). 

ANNs are not a common method in the field 

of educational data mining. Although highly 

accurate, the resulting black-box model is 

not interpretable, which is a major 

drawback. For the opening of the ANN 

black-box model, as well as for other models 

of this type, several agnostic methods have 

appeared recently, some of which we 

illustrate in the LMS system analysis. Septia 

et al [2], where the development of ICT 

technology has delivered a significant impact 

on the development of the game industry. 

Currently, the gamification method is widely 

applied in the world of education. The 

Gamification learning method means 

applying a game into the learning process, to 

foster motivation to learn and change student 

behavior. It makes teachers more creative in 

designing the learning process. Some of the 

developers have learned more about 

psychology science or other sciences that 

study human motivation and behavior. The 

aim is to motivate students in the learning 

process and maximizing feelings of 

enjoyment and involvement with the learning 

process, besides this media can be used to 

capture the things that interest students and 

inspire them to keep learning. This study 

aims to propose a gamification model that is 

flexible in LMS, have good performance and 

can be improved student performance in their 

study. Information security to make the 

ciphered message more difficult to decipher. 

For instance, the authors of paper [5] 

developed an algorithm that uses the shifting 

and replacing operations of bi-column-bi-row 

for the circular queue to increase security. A 

random number was used in this algorithm to 

control the shifting between the row and 

column, eventually, this leads to an increase 

in 

the complexity of plaintext decryption. In the 

same vein, an elliptic curve algorithm was 

designed based on matrix scrambling using a 

circular queue [6]. This research also utilizes 

the shifting process to accomplish the 

encryption and the decryption of the text. 

Besides, a multiple circular arrays algorithm 

was developed to encrypt data using three 

circular arrays. This algorithm enabled the 

shifting (elements in the outer or inner array), 

swapping (elements among the circular 

arrays) and XOR function (for encrypting the 

text) based on generating random numbers 



 

 

[7]. In contrast, a double encryption double 

decryption technique is proposed, which 

means the transmitter encrypts the text two 

times that leads the receiver to decrypt the 

cipher text twice using public key [8]. Also, 

an elliptic curve algorithm is developed to 

produce a cipher text [9]. Actually, in this 

work, the text firstly formed into ASCII 

code, and then the prime number and random 

number are chosen and formed into binary 

format. Where the “0” representation of the 

prime number is responsible for shifting the 

row/column in upward and left respectively. 

Besides, a multiple access circular queues 

algorithm is proposed with variable length in 

[10]. In this work different numbers of 

rotations are applied to the circular queues, 

swapping the elements in the same queue and 

XORing the elements with generating key 

numbers. The authors recommended that 

these processes would make a secure 

plaintext over the transmission line. On the 

other hand, the Fibonacci sequence is mostly 

used for image encryption. A text to image 

encryption algorithm is designed using the 

Fibonacci sequence [11]. This algorithm 

firstly converts the plaintext using the 

Fibonacci sequence, and then the Unicode is 

converted to a hexadecimal number and an 

RGB matrix. Finally, a shuffling operation is 

made to obtain the image to be sent. 

 

PROPOSED METHOD 

The M-Learning proposed model concern securing the data of the client and server. 

 

Fig 1: Architecture Diagram of Proposed Model 

The architecture diagram of the 

proposed model is depicted in Fig 1. The 

Circular Queue algorithm is used to secure 

the client and server data from the attackers. 

The learning content is stored in the LMS. 

The admin can access the database based on 

the authentication role, if the authentication 

is failed then they can’t access the data from 

the database. The model constitutes three 

roles such as Admin, tutors, and learners. 

Admin is responsible for structuring the 

courses and maintaining the databases. The 

admin contains an authorized username and 

password for accessing the server. The role 

of tutors has to update the course materials, 

monitored the performance of the learners, 

conduct a test, and improving their learner's 

skills. Each tutor is provided with a unique 

username and password. At last, the learners 

can access the M-Learning Server if they get 

the authentication for login. We concern 

about securing user privacy by encrypting all 

the profile details and user data with the CQ 

algorithm. 

3.1 User Privacy 

 
In mLMS, it collects some personal 

data such as learner’s preference, personal 

details, assessment details, goals which help 

the system to collaboration the users and 

enhance collaborative learning. For example, 

the learners are learning some content from 

the mLMS, geographical location, browsing 

behavior are easily monitor by the 

Application which can be hacked by the 



 

 

attacker if there is less security. It is easy to 

gather information from the devices, it is 

essential to preserve the privacy of the users. 

To secure sensitive data such as IP address, 

International Mobile Equipment Identity 

(IMEI), call record, web browsing log files, 

security credential, etc.. 

 
3.2 Circular Queue Algorithm 

 
The Circular Queue provides encryption 

and decryption of the data and it is difficult 

to decrypt the original data. The factors of the 

Algorithm is represented below such as, 

 The size of the circular queue is 

variable 

 Beginning of the keywords is 

variable 

 The Fibonacci Format 

 
Encryption process 

 
The input data is distributed to the 

circular queue algorithm where the letters are 

converted into equivalent 8 bits ASCII code 

and XOR function with each other. Then it 

represented in the decimal format. 

 

 
 
 

Fig 3: Encryption Process 

At last, it demonstrated in Fibonacci 

format and sent as the cipher text and stored 

in a database which is represented in Fig 2. The 

Encryption algorithm flow is depicted in Fig 3. 

 

3.3 Decryption Process 

The reverse process of encryption is 

decryption to recover the original message. 

After receiving the Fibonacci format then the 

output value is XOR function with the key 

letters. At last, the original 

data is recovered which is depicted in Fig 4. 



 

 

 

 

 
Fig 4: Decryption Process 

 

 

 

 

 
 

3.4 Implementation of Proposed Model 

 
 

 
 

Fig 5 Authentication Process 

 

The mLMS application provides 

security to the user privacy thereby providing 

security to the learning content stored in the 

LMS. The PHP and MYSQL code is used to 

develop the mLMS Application. The tutors 

have to log in to the mLMS using their 

username and password. The system checks 

tutor id in the database by decrypt using the 

CQ algorithm. If matched find then it allows 

accessing the data which is depicted in Fig 5. 

 

 



 

 

 

Fig 6 Upload Encrypted Exams to server 

The tutors have to align the questions 

and appropriate options for it. He/she has to 

mention the subject name, duration, exam 

level such as easy, moderate and difficult, 

and percentage level or grade which is 

depicted in Fig 6. Then the tutor has to 

encrypt the exam content with the 

Corresponding exam Id and uploaded it to the 

server. Based on the client-server interface 

the questions will be integrated into the 

corresponding student labels. Each data will 

be stored in the encrypted format in LMS. 

After that, the teacher has to assign an exam 

Id for the different learners based on their 

skills. Once the student login to the 

Application they will access exam Id without 

constraint. The Learner can answer all the 

question and submit to the server where the 

mLMS generate the result. The result will 

display to the learners where the evaluated 

test results are automatically encrypted and 

stored in the server which is depicted in Fig 

7. 
 

Fig 7 Submit Answer to server 
 

III. RESULTS AND DISCUSSION 

In this section, we review the security level of the mLMS Application using the CQ Algorithm. 



 

 

 

Fig 8 Home Page of mLMS (Tutor) 

The authenticated tutor can log in to their account for updating the upcoming exams for the learners 

which are depicted in Fig 8. 

Fig 9 Adding Questions by Tutors 

 

 

 

 

Server 

Encrypted Question Id to 

server 

 

 

Fig 10 Uploaded the Question ID to Server 

The tutors can add numerous questions 

as per the question pattern. Once he/she 

completes the question pattern then they can 

update the question to the server which is 

depicted in Fig 9. The questions and answers 

are updated in the database where the 

question Id is encrypted using the CQ 

algorithm and send to the Server which is 

depicted in Fig 10. 

 

 



 

 

  
 

(a) (b) 

Fig 11 (a)mLMS menu (b)mLMS Home Page 

The students can install the mLMS application in their android Mobile which is depicted in Fig 

11(a). The authorized learner can log in into their account which is depicted in Fig 11 (b). 

 

(a) (b) 

Fig 12 (a) Learner Page (b) Test menu 

The learner can use the test menu to take the exam based on the courses which are depicted in Fig 

12 (a). The learner can answer the questions by selecting the options and then they can move on to the 

next questions. The exam allocated time is scrolled automatically at the starting of the exam which is 

depicted in Fig 12 (b). 
 

 

(a) (b) 

Fig 13 (a) Submitting Answer (b) Result Evaluation 

After answering all the questions the learner can submit their answers using the submit button 

which is depicted in Fig 13 (a). Once the answer sheet is submitted the evaluation takes place 

automatically which is depicted in Fig 13 (b). 



 

 

 

 
Fig 14 The Learner’s Score 

Based on the learner’s answers, the mLMS generate the result for the answers and displayed 

instantly which is depicted in Fig 14. The evaluated result will be stored in the application memory. 

 

 

 

CQ Encrypted 
 

Server 
 

 
Fig 15 Evaluated Results send to the server 

The Evaluated results are encrypted and stored in the Server which is depicted in Fig 15. The 

learner can only view their results and their upcoming exams. 

 

 
Our 

Algorithm 

Complexity MACQ 

Algorithm 

Complexity 

XOR O(n
2
) XOR O(n

2
) 

with  with  

keyword  inner  

  most  

  queue  

  XOR O(n
2
) 

  with  

  second  

  inner  

  most  

  queue  

Table 1 Complexity Evaluation 
 

From Table 1 we represent the complexity of two algorithms where n is a number of bits for the 

process. Compare to the MACQ algorithm, our algorithm is less complex and highly secure for 

transmission data. 

 



 

 

 

Fig 16 Survey on mLMS security 

We surveyed the mLMS security level 

where 160 volunteers were responded to the 

security-based questions. The answers are 

categorized into strongly agree, agree, 

disagree and strongly disagree. mLMS got 

positive feedback from the learner's regard’s 

the security level which is depicted in Fig 16. 

Due to the CQ algorithm, the system is 

highly secure and efficient for the users. 

IV. CONCLUSION 

We analyze the safety of user information and 

mLMS course materials in our suggested 

paradigm. We upgraded mLMS's security by 

relying on a brand-new data structure. The new 

information is encrypted using a combination of 

circular queues and Fibonacci numbers. High 

security for mLMS data is ensured by the use of 

a variety of variable parameters that make 

decryption challenging. When compared to the 

current method (MACQ), ours is far quicker. 

When compared to the current method, its 

complexity is modest. Therefore, the solutions 

we have described are a potential answer to 

efficiently protect the mLMS and user data. 

 
REFERENCE 

1) In the 2019 edition of the 42nd 

International Conference on 

Information and Communication 

Technology, Electronics, and 

Microelectronics (MIPRO), Metetic 

published "Mining Learning 

Management System Data Using 

Interpretable Neural Networks." 

2) Septia Redisa Sriratnasari and others 

published "Applying Innovative 

Learning Management System (LMS) 

with Gamification Framework" at an 

international symposium on the use of 

information and communication 

technology in education. 2019 

3) Thirdly, "Cryptography and Network 

Security" by Atul Kahate, published in 

2013 by Tata McGraw-Hill Education. 

4) 4) Ali J. Abboud, "Multifactor 

Authentication for Software 

Protection", Diyala Journal of 

Engineering Sciences, Volume 08, 

Number 04, Special Issue, 2015. 

5) Ali J. Abboud, "Protecting Documents 

with Visual Cryptography," 

International Journal of Engineering 

Research and General Science, 2015. 

6) Computer Security Division 

(Information Technology Laboratory), 

"Recommendation for Key 

Management-Part 1: General (Revision 

3)", 2016. 6) E. Barker, W. Barker, W. 

Burr, W. Polk, and M. Smid. 

7) 2012 IEEE International Conference on 

Network Security and Systems (JNS2), 

"An Elliptic Curve Cryptography based 

on Matrix Scrambling Method" by 

Amounas, Fatima. 

8) 8 ) S. S. D. Pushpa R. Suri, "A Cipher 

based on Multiple Circular Arrays", 

International Journal of Computer 

Science Issues (IJCSI), Volume 10, 

Issue 5, Pages 165-175 (2013). 

9) In 2015, Springer Science & Business 

Media published "Guide to Elliptic 

Curve Cryptography" by Darrel 

Hankerson, Alfred J. Menezes, and 

Scott Vanstone. 



 

 

10) 2016 Proceedings of the Second 

International Conference on 

Information and Communication 

Technology for Competitive Strategies, 

S. Phull and S. Som, "Symmetric 

Cryptography using Multiple Access 

Circular Queues (MACQ)". 

11) Data Encryption Using Fibonacci 

Sequence and Unicode Characters, by 

P. Agarwal, N. Agarwal, and R. 

Saxena, MIT International Journal of 

Computer Science and Information 

Technology, Volume 5, Issue 2, Pages 

79-82, August 2015. 


