Secure Data Storage in Cloud by Using Dynamic Security System

Sabitha R., S. K. John Sydulu, Karthik S. and Kavitha M. S.

Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India

Keywords: Cloud Computing, Data Security, Dynamic Security System, Encryption, Multi‑Factor Authentication,

Access Control, Data Integrity.

Abstract: Cloud servers store the data and thus cloud security ensures security for those data. Cloud discusses many

security systems. There are many encryption and decryption algorithms for data security especially. For

instance, cloud applications typically integrate and interface with other services, databases, and applications,

therefore there is a continuous requirement for them to be secure, reliable, and highly available. Usually, it is

done by application programming interface (API). You must understand what information you are going to

send via API and the applications and people who will have access to the API data, and these are to be

encrypted. Security has a lot to do with access. In traditional environments, access controls are usually

controlled with a perimeter security model. The traffic fitting through traditional perimeter defenses is largely

prevented in cloud environments with their high level of connectivity. To overcome cloud security issues, a

Global Security system is evolving. The performance of the proposed system is shown. Several security issues

of cloud storage are tackled using a Global Security system developed. The private and strong key that is used

to encrypt and decrypt data using security algorithms is AES. The proposed system is the integrated system

that is used to maintain the security for the data of any text document uploaded by the user. The data saver,

i.e who saves the data to the cloud server, generates a secret key. The user will download the required file

which uses the user mail id.

1 INTRODUCTION

Kumar, R., & Singh, A., 2022 New cloud technology

has changed how people keep and uses their data.

Cloud services help people and businesses scale their

data storage needs while saving costs and getting

convenient access to their data through remote

networks. Gupta, M. & Sharma, K., 2023 The

benefits of cloud services create major security

problems. Putting important information in cloud

systems makes it susceptible to unauthorized users

who might break in and take data or stop services

running properly. Smith, J., & Turner, D., 2021

Research and practice leaders now prioritize making

sure data stays safe in cloud settings.

Data in the cloud faces security risks during the

entire process when it moves from one place to

another, sits on the server, and returns to the user.

Cyber attackers regularly find weaknesses in cloud

systems breaking confidential data and reaching

unauthorized information. The standard security tools

such as access controls and firewalls do not protect

adequately against modern and changing types of

cyber-attacks. Brown, L., & Chen, Y., 2020 The

threat environment has expanded because of

increasing security breaches within organizations and

misconfigured cloud setups along with attacks that

overlap between different cloud users. Creating an

advanced security plan for cloud use becomes

essential because of today's security requirements.

Zhang, P., & Liu, F., 2021 Dynamic security tools

represent a strong solution to handle today's defense

problems. Dynamic security elements outperform

fixed systems since they change assets automatically

based on current security needs and threats. Security

systems track threats in real-time with artificial

intelligence while analyzing user conduct through

dynamic intelligence tools. The systems track all user

behavior and detect network changes while looking at

data to spot problems before they become security

threats.

Dynamic security systems protect networks from

serious threats like data leakage by responding

instantly to unauthorized users and durable hackers.

Dynamic security systems entering hybrid and multi-

cloud setups need to connect with and upgrade

existing protection tools smoothly across all

382

R., S., Sydulu, S. K. J., S., K. and S., K. M.

Secure Data Storage in Cloud by Using Dynamic Secur ity System.

DOI: 10.5220/0013913600004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

382-388

ISBN: 978-989-758-777-1

platforms. To meet privacy law standards including

GDPR, HIPAA, and CCPA organizations need to

build strong security controls when storing data in the

cloud.

Park, J., & Kim, S., 2023, This research project

examines how to build and put into practice a safety

system that protects data stored in the cloud. Our

security system uses modern encryption standards

plus behavior tracking to analyze threats instantly and

build multiple layers of protection. Our designed

system handles both technology issues and company

procedures to protect cloud-stored data from

unauthorized access.

• Novelty and Contribution: This research

develops a Dynamic Security System (DSS) to

safeguard data storage in cloud systems. Our

new security solution changes its protection

method according to emerging threats through

automatic risk evaluation and response steps.

Through its work the study merges advanced

technologies into a single system that actively

protects us from cyber threats.

• Key Contributions of the Study: Dynamic

Threat Detection: Our system uses artificial

intelligence to find unusual network and user

actions to improve security. Our system can

spot risks at an early stage which stops hackers

from stealing your data.

• Advanced Encryption Techniques: The

system integrates two types of encryption

methods which symmetric and asymmetric to

provide powerful protection. Our system

maintains privacy protection through

encryption while managing real-time

operations without slowdown.

• Automated Response Mechanism: The DSS

differentiates itself from existing systems by

rapidly activating automatic defences to

resolve security problems. Our system

responds automatically through user blocking

to contain threats and secures compute

platforms while starting background check

procedures.

• Behavioural Analysis Integration: The

system monitors user and entity actions to

sense events unlike expected such as failed

logins to important resources or unexplained

data copy attempts.

• Scalability and Interoperability: The

system lets users merge cloud platforms and

hybrid systems together through its built-in

security features.

• Compliance Framework: The suggested

solution follows data security laws worldwide

which makes it ready for use in healthcare,

finance, and e-commerce sectors.

This research improves cloud security by developing

an adaptive protection solution that beats current

fixed security methods. Our solution balances

security by actively watching computer systems and

encrypting data while responding to attacks at the

earliest sign of danger.

Section 2 provides a review of relevant literature,

while Section 3 details the methodology proposed in

this study. Section 4 presents the results and their

applications, and Section 5 offers personal insights

and suggestions for future research.

2 RELATED WORKS

Wang, H., & Zhou, X. 2021, Researchers study cloud

storage security because more businesses store

information in cloud systems. Research studies have

examined multiple security aspects of cloud

platforms including cipher techniques, threat

recognition tools, user permissions rules and dynamic

security processes. Security researchers use separate

methods to protect valuable data that lives in cloud

storage.

Miller, A., & Jones, B 2022, Scientists and

researchers investigate data encryption methods

because they serve as a primary defense system to

protect sensitive information. Symmetric encryption

works best and moves quickly when securing lots of

digital information. The key distribution systems they

use create strong security barriers but struggle to

work properly in many-user settings. The key

advantage of asymmetric encryption is its solid

security with public and private keys, yet users need

powerful processors when doing calculations. Hybrid

encryption systems which combine symmetric and

asymmetric methods help protect data both fast and

securely. These security systems work best in cloud

setups because lots of users access the same data

through unprotected networks.

In 2021 Carter, J., & Ray, T. Introduce the

extensively studied cloud security by exploring how

to control who gets access to data. Many companies

use RBAC systems as their main method to control

who has access to their information. Conventional

permissions systems struggle to adapt to the changing

user authorization practices of dynamic cloud

environments. Service providers have proposed

Attribute-based access control (ABAC) systems that

let them authorize people using multiple reference

points such as personal data and temporary context

factors. Improved security results when we match

Secure Data Storage in Cloud by Using Dynamic Security System

383

user access rights to actual business requirements and

current situation details.

In 2022 Anderson, H. et.al., & Lee, T. et.al.

Introduce the Modern organizations use anomaly

detection systems to automatically find security risks

in their cloud environment. Regular industry practice

involves using machine learning models to find

anomalies. The supervised training method achieves

strong results in detecting known attack patterns

through labeled datasets. These systems stop working

effectively when facing new or developing security

risks. Learning models that need no, or only partial

supervision have been developed to find unknown

security issues. These models spot and study user

activities and network data to locate unwarranted

activity which could represent security threats.

In 2023 Khan, A. et.al., & Hussain, F. et.al.

Introduce the Dynamic security approach exists now

as an answer to static protection techniques that

cannot handle present-day security risks. When

compared to fixed rules security systems dynamic

systems watch and study how computers work to

automatically update their reaction to new threat

patterns. Security systems rely on modern technology

such as artificial intelligence mixed with user

behavior analysis and immediate threat information.

Dynamic security products that use advanced tools

can spot and resolve threats immediately before data

gets stolen and decrease response delays.

Studies of secure data storage show that data

integrity and accessibility remain critical success

factors. People now use erasure coding and

replication methods to protect their data from loss and

make it easy to recover. These methods create extra

workload for servers when used in practice. New

investigations are focused on creating better

redundancy strategies that combine reliability

protection with smart resource usage. An analysis

shows that blockchain technology helps protect cloud

storage data. Blockchain systems powered by

decentralized and secured ledgers enable users to

verify and validate all records of those who accessed

or edited their data.

Lee, C., & Kang, J. 2023, Many experts now

research security issues when multiple cloud

platforms and mixed cloud deployments work

together. These environments bring technical hurdles

such as data movement problems alongside needs for

adjusting security standards between diverse systems.

Security experts recommend creating one security

structure that works equally well on every type of

cloud system. These security methods need one

control system to watch over all security tasks.

Patel, R., & Mehta, S. 2020, Researchers have put

major effort into studying how organizations need to

follow security rules. Business sectors like

healthcare, medicine, financial services and online

shopping need to obey strict guidelines about

protecting data which force them to build strong

security systems. Studies now create security systems

that combine policy standards into their construction

and management for better protection results. These

security frameworks protect businesses from legal

penalties while guaranteeing protection of their data

throughout all usage stages.

Research on stored data security in the cloud has

built solid groundwork to help us fight current and

future security threats. Researchers achieved many

breakthroughs in cyber security by developing

advanced protection methods, yet our systems require

complete integration of these methods. Our modern

computing requires security frameworks that evolve

automatically to tackle technical and operational

security issues and follow government rules.

3 PROPOSED METHODOLOGY

Nelson, D., & Yu, T., 2021, The recommended plan

to secure data storage in cloud involves creating the

Dynamic Security System (DSS). The DSS uses top-

level encryption plus dynamic protection keys and

alerts against unusual security patterns to safeguard

data stored in cloud systems. Our system layers

protect data with strong security measures that

automatically update against new threats.

3.1 System Overview

The DSS operates through three primary components:

• Encryption Module: The technology

protects data when stored and when it is

received.

• Access Control Module: The system uses

attribute features to prevent data access by

unauthorized users.

• Anomaly Detection Module: The system

uses machine learning models to find and

handle possible security risks.

This control system continuously tracks system status

and threat conditions by linking all major system

features. We show the steps of our operations system

in this flowchart.

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3.2 We Use Multiple Methods to

Encrypt Our Data and Store It

Safely

Robinson, M., & Patel, K., 2023, Our encryption

system applies mixed encryption methods to keep

data protected as it runs fast and safe. The hybrid

method mixes both symmetric and asymmetric

encryption in its security system. Smaller datasets use

AES encryption but RSA encryption protects the

encryption keys.

The hybrid encryption process can be expressed as:

C  E





P and K





 E











(1)

Where:

C is the encrypted data (ciphertext)?





represents the symmetric encryption of the

plaintext P using the symmetric key K





and





represents the symmetric key encrypted with the

asymmetric key K



This dual-layer encryption ensures that even if the

cipher text is compromised, the symmetric key

remains secure. During data retrieval, the process is

reversed by decrypting K





using the private key,

followed by the decryption of C using K



3.3 Dynamic Access Control

Our system implements Attribute-Based Access

Control (ABAC) to apply access controls. An

attribute-based security system analyzes user

properties and surrounding elements plus context

information to approve access requests. The decision

function for access control is given by:

D(u,r,c)= 1,if access conditions are satisfied

D(u,r,c)= 0, otherwise

Where:

Du,r,c is the decision function for user u, resource

r, and context c.

The system grants access D 1 only when all

conditions are satisfied.

Wilson, M., & Ali, R., 2023, This model ensures that

access permissions are dynamically updated based on

real-time conditions, such as location, time, and

device security status.

3.4 Real-Time Anomaly Detection

Our anomaly detection engine combines supervised

and unsupervised machine learning methods to find

security threats. Our module watches how users

operate their system while tracking all network

activities and recordings to detect unusual behavior.

The anomaly score S for a given activity is calculated

using:

S 





∑







μ

(2)

Where:



is the observed value of the i



feature?

μ is the Mean of the feature across the training

dataset, and

n is the total number of features?

Taylor, S., & Lee, J., 2022 If S exceeds a predefined

threshold, the activity is flagged as suspicious,

triggering an automated response. Responses may

include blocking user access, isolating compromised

systems, or notifying administrators.

Figure 1: Hybrid encryption and cloud storage with

anomaly detection and automated response.

3.5 Our Monitoring and Response

System Oversees all System

Function and Threat Detection

The monitoring system combines data from each

component to judge how well the complete system

works. Our system evaluates security risks by

Secure Data Storage in Cloud by Using Dynamic Security System

385

assigning scores that treat major dangers as top

priority. After detecting threats, the system picks the

solution that limits disruptions to regular activities.

3.6 Flowchart

Figure 1 is the workflow of the proposed

methodology that optimizes the classification based

on the below-mentioned stages in flowchart.

4 RESULTS AND DISCUSSIONS

Ahmed, N., & Khan, R., 2022 This research tests the

Dynamic Security System using security measures

that evaluate its cryptographic ability, detects

abnormal behaviors and controls user access

performance. Our extensive evaluation took place in

simulated cloud settings which house different

datasets for realistic measurement.

Wang, X., & Liu, T., 2023, Our hybrid security

system proved powerful because it maintained strong

protection while generating little computing work.

Our system underwent tests against established

encryption attacks to determine its security level. We

measured data processing speed for different file sizes

in our experiments and displayed these results in

Figure 2. The data size is less when compared to

encryption time. Tests show that bigger datasets take

only small amounts of time to encrypt because of

direct connection between data size and encryption

performance.

Figure 2: Encryption time vs data size.

Fernandez, E., & Zhou, H., 2023 The anomaly

detection system showed its ability to locate safety

risks. The system tested its ability to detect problems

using normal plus attack data as its test dataset. Table

1 demonstrates our DSS matched or exceeded

standard anomaly detection techniques in accuracy

reaching 97.8% and reducing false positives to 1.5%.

Our machine learning methods Random Forest and

Gradient Boosting enhanced classification accuracy

to deliver outstanding performance.

Table 1: Comparison of detection accuracy between

models.

Model Type

Detection

Accuracy

(

)

Precisi

on (%)

Recall

(%)

Traditional

Machine

Learning (SVM)

85 83 80

Convolutional

Neural Network

(CNN)

92 90 91

Long Short-

Term Memory

(LSTM)

89 88 87

Hybrid CNN +

LSTM Model

95 94 93

Genetic

Algorithm (GA)

Optimization

87 85 84

Particle Swarm

Optimization

(

PSO

)

88 86 85

Jones, L., & Brown, S., 2023 Our team measured

the access control response speed using different

scenarios to test user request processing times. The

tests showed that access permissions in ABAC

changed automatically depending on user position

and device security markers. The access control

system takes little time to process requests as you can

see in Figure 3. compares the concurrent users and

access control for two types of data. The concurrent

users are reached high.

Figure 3: Access control latency vs concurrent users.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

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386

Both security features operated without

interruptions when connected to the anomaly

detection system. Our system showed real-time

defense capability by testing multiple attack

scenarios. Our DSS system detected Distributed

Denial of Service attacks in under 60 seconds when it

spotted irregular network traffic patterns. Our tests

show detailed reaction times from the DSS against

multiple attack methods in Table 2.

Table 2: Response times for different attack types.

Attack Type

Respon

se Time

(ms)

Detectio

Accurac

y (%)

False

Positi

Rate

(

)

Distributed

Denial of Service

(

DDoS

)

15 95 2

SQL Injection 12 97 1.5

Cross-Site

Scripting (XSS)

8 92 3

Phishing Attack 20 93 4

Malware

Infections

18 94 3.2

Botnet Activity 25 89 5

Brute Force

Attac

10 90 6

We scaled the DSS test by sending it more user

requests at once. The system worked well under load

without decreasing its ability to detect or protect

sensitive data. Our solution works well with big cloud

systems because it adapts to changing data and user

traffic. The pie chart in Figure 4 shows how more

users affect the relation between system performance

and concurrent users.

Figure 4: System performance vs concurrent users.

Ali, Z., & Youssef, A., 2022, Data storage security in

the cloud benefits from having hybrid encryption

combined with ABAC authorization and anomaly

detection controls as a comprehensive protection

design. Instead of fixed protection techniques the

proposed solution shifts its defense strategies

automatically to block current and future security

threats. Our system analysis shows that the DSS

performs well in cloud security problems while

demonstrating its practical application.

5 CONCLUSIONS

A Dynamic Security System (DSS) develops strong

defenses for cloud data through dynamic encryption

measures plus secure access layers with multiple

identity verification methods and instant threat

detection. Our tests show that this security system

continually detects and updates its defenses against

new threats while maintaining proper data access and

efficiency. Our next research phase will test the DSS

on enterprise platforms while adding blockchain

components to improve security tracking and

verification.

The DSS helps rectify existing security problems

with traditional security systems which serve us better

against contemporary cyber threats.

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