Unauthorized Access Research Articles

The Critical Role of Two-Factor Authentication (2FA) in Mitigating Ransomware and Securing Backup, Recovery, and Storage Systems

In today’s digital landscape, ransomware attacks have become one of the most significant cybersecurity threats. Securing backup, recovery, and storage systems is paramount to ensuring business continuity. Two-Factor Authentication (2FA) has proven to be a key strategy in defending against ransomware attacks by adding an extra layer of security. By requiring two forms of authentication, 2FA reduces the risk of unauthorized access to critical systems and data. This paper explores how 2FA enhances the security of backup systems, recovery processes, and storage devices, with a particular focus on its role in mitigating ransomware risks. It further discusses its impact on compliance with data protection regulations and operational efficiency.

Enhancing Privacy in the Management of Library Resources: A Novel Approach Utilizing FRS and ABE-CP Algorithm for Improved Protection

Libraries contain sensitive resources that users can access. The vital challenges faced include unauthorized access, privacy violations, malicious attacks, etc. Researchers have explored several ways to curb these challenges including encryption, policies, anti-malware etc. An improved design to secure library resources based on Fragmentation Redundancy Scattering (FRS) and Attribute-Based Encryption Cipher text Policy (ABE-CP) was presented. The core idea of FRS is to fragment confidential information to produce insignificant fragments and then scatter the pieces across the distributed system. As such, the scheme provides a new technique to manage network components and exchange encryption keys. By comparing the proposed scheme with other schemes, the proposed scheme prevents unauthorized access to users’ data and protects user’s privacy. The scheme in addition becomes resistant to common attacks like replay and man in the middle. According to our findings, the scheme is safe, practicable, and comprehensive with high security for both encrypting and decrypting users’ data within the least possible time.

Enhancing Operational Effectiveness in Security and Defence within the UAE: The Strategic Role of Artificial Intelligence

Purpose: The purpose of the study was to examine operational effectiveness in security and defence within the UAE: The strategic role of artificial intelligence. Methodology: The study applied both Qualitative and quantitative approach. For the qualitative component of this research, a complete literature analysis will be done to investigate the current body of scholarship, studies, and policy papers pertaining to the deployment of artificial intelligence (AI) in security and defense within the United Arab Emirates (UAE). Thematic analysis will be used for a qualitative data analysis method. This study will also apply quantitative approach. Data was collected using structured questionnaires and a structured survey distributed to 50 professionals to stakeholders within the UAE's security and military industries to obtain quantitative data on different areas of AI acceptance, usage, obstacles, and perceived influence on operational performance. Statistical software like Excel and Python will be used to perform quantitative data analysis. Charts, graphs, and plots will be used to visually present the survey results. Findings: The statistical findings reveal a significant relationship between AI adoption and enhanced decision-making capabilities. A strong positive correlation (r = 0.78, p < 0.001) between AI usage and improved situational awareness underscores AI’s capacity to process real-time data effectively. In cybersecurity, the analysis identifies a moderate positive correlation (r = 0.64, p < 0.01) between AI implementation and threat mitigation success. The participants have consistently emphasized ability of AI for analysis of vast datasets in the real time ensuring more informed and quick decision. It aligns with the global trends where the defense organizations would leverage AI for interpretation of satellite imagery and prediction of threats. The advanced machine learning algorithms have been proven effective for detection of anomalies like unauthorized access and phishing attack attempts. The participants are praising the technologies for improving the operational efficiency and reducing the human risks. Unique Contribution to Theory, Practice and Policy: Study highlights existing theories where AI improves decision making through real time insights. However, it challenges the deterministic views for AI as the infallible further emphasizing need for the hybrid models which integrates with the human judgement. The defense organizations need to adopt for hybrid approach where the AI is supporting instead of replacing the human decision making. The training programs need to be designed for equipping personnel with skills for interpreting the AI outputs critically. With the growing adversarial of AI, the investment across counter AI technologies has been imperative. It includes development of systems which could neutralize and identify the AI driven cyberattacks and foster collaborations with the global cybersecurity experts for staying ahead in emerging threats. The strengthening of partnerships with the international defense organizations could also help the UAE for leveraging the cutting-edge counters for AI solutions while sharing the threat intelligence effectively.

FLEET INTELLIGENCE REDEFINING VECHICLE COORDINATION SOLUTION

A data breach happens when sensitive information is accessed without permission. This research proposes a machine learning model to protect websites by learning from past attacks. Built with Django, it collects data from e-commerce sites, secures it, and prevents unauthorized access.

Development of Heuristic Strategy With Hybrid Encryption for Energy Efficient and Secure Data Storage Scheme in Blockchain‐Based Mobile Edge Computing

ABSTRACTInternet of Things (IoT) devices is extensively employed to collect physiological health data and provide diverse services to end‐users. Nevertheless, in recent applications, cloud computing‐based IoT proves beneficial for standard data storage and ensuring high‐security information sharing. Due to limitations in battery capacity, storage, and computing power, IoT devices are often considered resource‐constrained. Consequently, data signing by IoT devices, aimed at ensuring data integrity and authentication, typically demands significant computational resources. Unsafe data storage and high latency are considered as the major issues in the IoT‐based data storage mechanism for duplicating and misusing the information while it is stored in the cloud database. Hence, blockchain technologies are needed to provide high security over the stored data. Hence, the research aimed to implement an efficient blockchain‐based data storage system in mobile edge computing, safeguarding data from unauthorized access. In this approach, it contains four layers that are cloud layer, the entity layer, the block‐chain layer, and the edge computing layer. The user's data are stored in the optimal location in the entity layer, where the data storing location is find out using the proposed Hybrid Battle Royale with Archimedes Optimization Algorithm (HBRAOA). In the edge computing layer, an optimal key‐based homomorphic encryption algorithm using Elliptic Curve Cryptography (ECC) is introduced to encrypt data with the most optimal key, ensuring secure storage. This encryption method leverages the same HBRAOA to enhance the efficiency. Next, the digital signature is demonstrated to give the authorization of the user, and it is distributed to the blockchain layer. Thus, the indexes of the shared data are stored in the blockchain layer to avoid fault tolerance and tamper‐proofing. Finally, the cloud layer receives the valuable encrypted data, and the authenticated users with known encrypted keys are able to access the data by decrypting them. The result analysis shows that the performance of the developed model, which attains 27%, 98%, 35%, and 18% enhanced than Particle Swarm Optimization (PSO)‐ECC, Black Widow Optimization (BWO)‐ECC, Battle Royale Optimization (BRO)‐ECC and Archimedes Optimization Algorithm (AOA)‐ECC. The efficiency of the proposed blockchain‐based mobile edge computing scheme with the optimization strategy is validated by conducting several similarity measures over the conventional methods.

Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection

The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investigates the application of CHERI (Capability Hardware Enhanced RISC Instructions) to enhance the security of Intrusion Detection Systems (IDSs) in automotive networks. By leveraging CHERI’s fine-grained memory protection and capability-based access control, the IDS ensures the robust protection of rule configurations against unauthorized access and manipulation. Experimental results demonstrate a 100% detection rate for spoofed IP packets and unauthorized rule modification attempts. The CHERI-enabled IDS framework achieves latency well within the acceptable limits defined by automotive standards for real-time applications, ensuring it remains suitable for safety-critical operations. The implementation on the ARM Morello board highlights CHERI’s practical applicability and low-latency performance in real-world automotive scenarios. This research underscores the potential of hardware-enforced memory safety in mitigating complex cyber threats and provides a scalable solution for securing increasingly connected and autonomous vehicles. Future work will focus on optimizing CHERI for resource-constrained environments and expanding its applications to broader automotive security use cases.

Blockchain enabled policy-based access control mechanism to restrict unauthorized access to electronic health records

Blockchain enabled policy-based access control mechanism to restrict unauthorized access to electronic health records

Hybrid Encryption using Advanced Encryption Standard and Arnold Scrambling for 3D Color Images

Digital security ensuring the confidentiality and integrity of visual data remains a paramount challenge. The escalating sophistication of cyber threats necessitates robust encryption methods to safeguard sensitive information from unauthorized access and manipulation. Despite the development of various encryption techniques, inherent vulnerabilities exist within conventional methods that can be exploited by attackers. Therefore, this research aims to investigate the effectiveness of the combined approach of Arnold Scrambling and Advanced Encryption Standard (AES) in mitigating these vulnerabilities and providing a more secure solution. The primary goal of this research is to enhance the security of digital images by mitigating vulnerabilities associated with conventional encryption methods. Arnold Scrambling introduces chaotic mapping to disperse pixel values, while Advanced Encryption Standard (AES) provides robust cryptographic strength through its substitution-permutation network. By combining these methods in an ensemble fashion, the encryption process achieves heightened resilience against various cryptographic attacks. The proposed methodology was evaluated by using standard metrics including Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and entropy analysis. Results indicate consistent performance across multiple test images, namely: Lena, Mandrill, Cameraman, and Plane with Unified Average Changing Intensity (UACI) averaging 33.6% and Number of Pixels Change Rate (NPCR) nearing 99.8%. Entropy values approached maximum, affirming the efficacy of the encryption in generating highly randomized outputs.

Penerapan Super Enkripsi Algoritma Autokey Cipher dan El-Gamal File Gambar

Autokey Cipher is a classic encryption technique that uses substitution methods, providing a higher level of security compared to other classical techniques such as Caesar and Vigenère ciphers. It generates keys that match the length of the plaintext, ensuring that each character of the original data has a corresponding key character, which is crucial for effective encryption.on the other hand, the ElGamal algorithm is an asymmetric encryption method that uses different keys for encryption and decryption, relying on prime numbers and discrete logarithms to secure messages. This algorithm was chosen because of its strong security features, making it difficult for unauthorized users to decrypt information without the right key. The combination of these two algorithms aims to create a more secure encryption system. Autokey Cipher contributes complex substitution techniques, while ElGamal adds an additional layer of security through an asymmetrical key structure. This dual approach is very beneficial for protecting sensitive data, such as image files, from unauthorized access. highlighting the potential of these combined methods to provide stronger defenses against a wide range of security threats in the digital landscape

A Screenlock Application Using Yoruba Characters for Android Devices

A lock screen is a user interface that appears on a mobile device when it is in a locked state. The purpose of a lock screen is to prevent unauthorized access to the device and its content, such as personal contacts, photos, and other sensitives information. A lock screen typically requires the user to enter a password, pattern or use biometric authentication such as fingerprint recognition to unlock the device. English characters and patterns were often used for screen lock on mobile devices because English language is the most official language in some country most especially in Nigeria and other African countries, However, most illiterate and adult that are not familiar with these languages find it difficult to use. To combat this problem, there is need to develop a user-friendly Yoruba lock-screen app that takes Yorùbá characters as input and also allow users to unlock their android devices with this newly developed application. Moreover, Yorùbá is one of the largest ethnic groups in Nigeria. and also, there is need to promote our local language. The system was built using various programming languages and comparative study of different technique is done as per stages. The system is made flexible and versatile and application has a user-friendly screen. The application has been tested by various student (who uses Android APP) and has provided a successful result

BACKEND API DATA PROTECTION MENGGUNAKAN JWT TOKEN DAN ALGORITMA AES 256-BIT DENGAN BAHASA PEMROGRAMAN GOLANG

Data security is a critical concern in the development of web-based applications and backend APIs, particularly in protecting sensitive information from unauthorized access. This research aims to integrate Advanced Encryption Standard (AES) 256-bit for data encryption and JSON Web Token (JWT) for user authentication, developed using the Go programming language. The methodology employed is Research and Development (R&D), encompassing stages of requirement analysis, system design, implementation, and testing. The results show that combining AES 256-bit and JWT provides a dual-layer security mechanism, ensuring data integrity and confidentiality during storage and transmission. This solution proves effective for high-security applications such as e-commerce, healthcare, and online examinations. Additionally, the system offers seamless integration with third-party applications, promoting ease of adoption without compromising security. The study concludes that this integrated approach enhances data protection and facilitates secure API interactions across various platforms

Technique for Assessing the Effectiveness of the Functioning of Web Backdoor Detection Systems

Currently, there is a significant increase in information security incidents related to attacks on web resources. Obtaining unauthorized access to web resources remains one of the main methods of penetration into corporate networks of organizations and expanding the capabilities of intruders. In this regard, many studies are aimed at developing web backdoor detection systems (WBDS), but there is a need to assess the effectiveness of these systems. The purpose of this study is to develop an objective approach to assess the effectiveness of the WBDS functioning. In this work, it was found that the effectiveness of web backdoor detection systems is objectively manifested in the process of their use, therefore, testing of such systems should be carried out in conditions as close as possible to real ones. In this regard, the article proposes a new technique for assessing the effectiveness of WBDS. It is based on the calculation of three groups of specific indicators characterizing the potency, resource intensity and responsiveness of the detection tool, as well as the calculation of a generalized effectiveness indicator. Based on an analysis of research in this area, a classification of web backdoors embedded by an attacker into the source code of web applications has been developed. This classification is used when generating test datasets to calculate specific potency indicators. The developed methodology is applicable to tools that work based on the analysis of the source code of web pages. Additionally, its use requires a number of initial data, such as permissible maximum errors of frequent potency indicators and the probability of them being within the confidence interval, as well as weighting coefficients of specific potency indicators, which are selected by expert methods. This work may be useful for information security specialists and researchers who want to conduct a more objective assessment of their WBDS.

Fraud Detection in Banking using Key Agreement and Face Authentication

Fraud detection in banking is a critical domain for financial security, driven by the rising frequency and sophistication of cyber-attacks. This paper introduces a dual-layered fraud detection framework combining key agreement protocols and face authentication. The system ensures secure communication through cryptographic techniques, such as Diffie-Hellman and Elliptic Curve Cryptography, while leveraging real-time facial recognition powered by TensorFlow and OpenCV for biometric authentication. These technologies collectively address identity theft, unauthorized access, and data breaches in banking operations. Implementation results demonstrate a high accuracy rate in user verification, efficient transaction processing, and robust protection against cyber threats. This paper outlines the system's architecture, implementation, and performance while addressing associated challenges such as privacy concerns, algorithmic biases, and scalability. The findings highlight the transformative potential of integrating cryptography and biometric authentication for modern banking security.

Data Governance for Emerging Technologies: A Conceptual Framework for Managing Blockchain, IoT, and AI

As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.

ESMIoTHD: ENHANCED BLOCKCHAIN SECURITY AND MANAGEMENT FOR IOT-BASED HEALTHCARE DATA, A ROBUST FRAMEWORK FOR TRUST AND INTEGRITY

The primary focus of the study, entitled “Enhanced Blockchain Security and Management for IoT-Based Healthcare Data: A Robust Framework for Trust and Integrity”, was to determine the performance of four encryption algorithms, SADBTM, ECSBFQL, HBDMS, and ESMIoTHD, regarding their encryption and decryption times, average latency, and packet delivery ratio on different data sizes. It can be seen that employing blockchain security and management increases the security of IoT-based healthcare data. Consequently, this ensures that healthcare data can be linked to the confidentiality, integrity, and availability that remains central in today’s digitally interconnected world. Since the blockchain is a decentralized medical data-sharing tool that is impossible to alter, it is more secure due to guaranteed no potential unauthorized access to the data. Additionally, as indicated in the results during the performance evaluation of the blockchain systems in IoT healthcare networks, it was possible to improve patient data privacy, make data access easier, and ensure the trustworthiness of the healthcare systems. For instance, according to the study’s results, the encryption and decryption times had been computed in terms of milliseconds. The range of the data size was from 10 kilobytes to 100 kilobytes. When 100 data points had been encrypted, the ESMIoTHD had the lowest encryption time, namely, 12363 m, as compared to its encryption peers which include 13232 m, 13854 m, and 14376 m SADBTM, ECSBFQL, and HBDMS, respectively. Conversely, the results regarding the decryption times revealed a similar pattern to the encryption times. That is there was no significant deviation between the three encryption algorithms with the values being 13232 m, 13854 m, and 14376 m; however, ESMIoTHD had a decryption time of 13232 m. The average latency had been calculated in terms of milliseconds (m), whereby the results showed that ESMIoTHD has equivalent performance and its average latency was closest to its peers, such as 808 m for 100 data points. The packet delivery ratio had been computed in terms of percentages. Both the encryption and decryption algorithms had a similar pattern as they had been assessed based on the three results. However, the results show that ESMIoTHD had the highest PDR values in all data sizes, for example, the PDR was 98.896% for a 100 kb data size, which was far much higher than its peers. Based on the results of ESMIoTHD being the most efficient and reliable, particularly in terms of high throughput and low latency, these outcomes show that it is one of the leading encryption algorithms.

Crayfish optimization based pixel selection using block scrambling based encryption for secure cloud computing environment

Cloud Computing (CC) is a fast emerging field that enables consumers to access network resources on-demand. However, ensuring a high level of security in CC environments remains a significant challenge. Traditional encryption algorithms are often inadequate in protecting confidential data, especially digital images, from complex cyberattacks. The increasing reliance on cloud storage and transmission of digital images has made it essential to develop strong security measures to stop unauthorized access and guarantee the integrity of sensitive information. This paper presents a novel Crayfish Optimization based Pixel Selection using Block Scrambling Based Encryption Approach (CFOPS-BSBEA) technique that offers a unique solution to improve security in cloud environments. By integrating steganography and encryption, the CFOPS-BSBEA technique provides a robust approach to secure digital images. Our key contribution lies in the development of a three-stage process that optimally selects pixels for steganography, encodes secret images using Block Scrambling Based Encryption, and embeds them in cover images. The CFOPS-BSBEA technique leverages the strengths of both steganography and encryption to provide a secure and effective approach to digital image protection. The Crayfish Optimization algorithm is used to select the most suitable pixels for steganography, ensuring that the secret image is embedded in a way that minimizes detection. The Block Scrambling Based Encryption algorithm is then used to encode the secret image, providing an additional layer of security. Experimental results show that the CFOPS-BSBEA technique outperforms existing models in terms of security performance. The proposed approach has significant implications for the secure storage and transmission of digital images in cloud environments, and its originality and novelty make it an attractive contribution to the field. Furthermore, the CFOPS-BSBEA technique has the potential to inspire further research in secure cloud computing environments, making the way for the development of more robust and efficient security measures.

A study on analysing the role of blockchain in supply chain data security and developing a conceptual diagram

This research explores the role of blockchain technologyin enhancingdata securitywithin modernsupply chains. As global supply chains become increasingly complex and vulnerable to risks such as fraud, counterfeiting, and data breaches, blockchain offers a promising solution to address these challenges. By leveraging its core features blockchain can improve transparency, trust, and traceability in supply chain operations. This study examines how these blockchain features can secure critical data elements, such as product provenance, inventory management, and financial transactions, ensuring their integrity and authenticity. The research also discusses the technical benefits of blockchain, including its ability to prevent unauthorized access, reduce fraud, and enhance collaboration among supply chain participants. Despite its advantages, the study identifies several limitations, including adoption barriers, scalability issues, and regulatory concerns. The findings suggest that while blockchain has the potential to revolutionize supply chain security, further research is needed to overcome these challenges and explore its broader applicability across various industries. Future studies should focus on improving scalability, exploring sector-specific implementations, and addressing the legal frameworks necessary for blockchain adoption.

Lightweight Noise Robust Spoofing Attack Detection using Cochleagram and ResNet Amalgamated Features

Abstract Speaker verification, like other biometric technologies, is vulnerable to spoofing attacks. An attacker impersonates a specific target speaker using impersonation, replay, Text-to-Speech (TTS), or Voice conversion (VC) techniques to gain unauthorized access to the system. The work in this paper, proposes a solution that uses an amalgamation of Cochleagram and Residual Network (ResNet) to implement the frontend feature extraction phase of an Audio Spoof Detection (ASD) system. Cochleagram generation, feature extraction-dimensionality reduction and classification are the three main phases of the proposed ASD system. In the first phase, the recorded audios have been converted into Cochleagrams by using Equivalent Rectangular Bandwidth (ERB) based gammatone filters. In the next phase, three variants of Residual Network (ResNet), ResNet50, ResNet41 and ResNet27, one by one, have been used for extracting dynamic features and Resnet50, ResNet41 that are fed to Linear Decrement Analysis (LDA) for dimensionality reduction. At last, in the classification phase, the LDA reduced features have been used for training four different machine learning classifiers such as Random Forest, Naïve Bayes, K-Nearest Neighbour (KNN), and eXtreme Gradient Boosting (XGBoost), individually. The proposed work in this paper concentrates on synthetic, replay, and deepfake attacks. The state-of-the-art ASVspoof 2019 Logical Access (LA), Physical Access (PA), Voice Spoofing Detection Corpus (VSDC) and DEepfake CROss-lingual (DECRO) datasets are utilised for training and testing the proposed ASD system. Additionally, we have assessed the performance of our proposed system under the influence of additive noise. Airplane noise at different SNR rate (0, dB 5dB, 10dB and -5dB) has been added to training and testing audios for the same. From the obtained results, it can be concluded that combination of Cochleagram and ResNet50 with XGBoost classifier outperforms all other implemented systems for detecting fake audios under noisy environment.

Securing Retrieval-Augmented Generation Pipelines: A Comprehensive Framework

Retrieval-Augmented Generation (RAG) has significantly enhanced the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate external knowledge sources, thereby improving response accuracy and relevance. However, the security of RAG pipelines remains a paramount concern as these systems become integral to various critical applications. This paper introduces a comprehensive framework designed to secure RAG pipelines through the integration of advanced encryption techniques, zero-trust architecture, and structured guardrails. The framework employs symmetric and asymmetric encryption to protect data at rest and in transit, ensuring confidentiality and integrity throughout the data lifecycle. Adopting zero-trust principles, the framework mandates continuous verification of all entities within the data flow, effectively mitigating unauthorized access and lateral movement risks. Additionally, the implementation of guardrails, such as immutable system prompts and salted sequence tagging, fortifies the system against prompt injection and other malicious attacks. A detailed lifecycle security continuum is presented, illustrating the application of these security measures from data ingestion to decommissioning. Case studies across healthcare, finance, retail, and education sectors demonstrate the framework’s effectiveness in maintaining high performance and scalability without compromising security. This work provides a foundational model for future research and practical implementation, emphasizing the necessity of robust security protocols in the deployment of RAG-based applications.

EFFECTIVENESS OF MULTIFACTOR AUTHENTICATION TECHNOLOGY FOR PROTECTING STUDENT PRIVACY : A SYSTEMATIC LITERATURE REVIEW

Student privacy is a major concern in the digital era, especially with the increasing use of educational technologies such as Learning Management Systems (LMS) and IoT devices. Traditional authentication systems are considered inadequate in the face of complex security threats. Therefore, this study evaluates the effectiveness of Multi-Factor Authentication (MFA) in protecting student privacy in an educational environment. This study uses a Systematic Literature Review (SLR) design based on 86 relevant articles selected from Scopus indexed journals (2020–2024). The articles were analyzed thematically using PRISMA Flow Diagram to identify patterns, trends, and gaps related to MFA implementation. The findings show that MFA can improve student privacy protection by reducing the risk of data leaks by up to 80%, lowering unauthorized access, and increasing user trust. Key technologies include biometrics, OTP, and blockchain, which have proven to be effective in improving security. However, MFA implementation faces obstacles such as cost, system complexity, and biometric data privacy concerns. This research makes an important contribution to academic literature and educational data security practices. Recommendations for further research include the exploration of behavior-based authentication methods, studies on user perception, and the implementation of MFA in educational environments with limited infrastructure. The practical implications include strengthening technology security policies in educational institutions and educating users to raise awareness of the importance of data privacy protection. Keywords: Student privacy, Multi-Factor Authentication (MFA), Educational Data Security, Biometric Technology