Advanced Encryption Standard Research Articles

Evaluating AES Security: Correlation Power Analysis Attack Implementation using the Switching Distance Power Model

Cryptographic circuits play a critical role in safeguarding confidential information and ensuring secure communication, contributing to the resilience of digital infrastructure under SDG 9 (Industry, Innovation, and Infrastructure). These circuits store encryption keys for the Advanced Encryption Standard (AES) algorithm, including AES-128, AES-192, and AES-256, which are widely used in applications such as online banking and secure messaging platforms. This paper examines the effectiveness of Correlation Power Analysis (CPA), a side-channel attack technique that exploits power consumption patterns in cryptographic circuits, to highlight the challenges of implementing secure encryption systems. The study illustrates the CPA attack procedure against AES implemented on the SASEBO-GII FPGA platform. Experimental results reveal that while the CPA attack based on the Hamming Weight (HW) power consumption model fails to extract the encryption key, the Switching Distance (SD) power consumption model successfully recovers the entire key with a 100% success rate using approximately 4000 power traces. These findings underscore the vulnerability of cryptographic circuits to advanced side-channel attacks and emphasize the need for robust countermeasures to ensure secure data protection, thereby advancing secure and sustainable digital environments under SDG 11 (Sustainable Cities and Communities).

Pioneering the Security of EHRs Using an Immersive Blockchain Conceptual Framework

This study develops a conceptual framework to enhance the security and functionality of Electronic Health Records (EHRs) in response to advancing healthcare needs. Objectives include strengthening data protection against both traditional and quantum cyber threats, increasing system resilience, and improving user experience and operational efficiency. Methods/Analysis involve a novel combination of Advanced Encryption Standard (AES) and quantum cryptographic algorithms CRYSTALS-Dilithium and CRYSTALS-Kyber within a hybrid blockchain architecture to secure EHRs. Decentralized Autonomous Organizations (DAOs) are incorporated to decentralize control and reinforce security, while artificial intelligence and metaverse integration facilitate user engagement and streamline operations. Findings indicate that this hybrid blockchain model, enhanced with quantum-resistant cryptography and decentralized governance, significantly improves EHR security. AI and the metaverse contribute to user interaction and operational flow. Novelty/Improvement lies in integrating hybrid blockchain, quantum cryptography, AI, and the metaverse into a unified framework, effectively addressing current and future healthcare data management challenges. This multi-layered approach represents a significant advancement over existing systems by bolstering EHR security, user engagement, and operational capabilities. Doi: 10.28991/ESJ-2025-09-01-010 Full Text: PDF

Penerapan Algoritma Kriptografi Advanced Encryption Standard 128 Pada Laporan Transaksi Di Kopi Tuku

Data security is an important aspect in information management, especially in business transactions involving sensitive data such as financial reports. This study aims to apply the Advanced Encryption Standard (AES) 128-bit cryptographic algorithm to transaction report data at Kopi Tuku. AES 128-bit was chosen because of its ability to provide strong and efficient data encryption, with a high level of security and adequate processing speed. In this study, transaction data was taken from the Kopi Tuku sales system and encrypted using AES 128-bit to protect sensitive information from unauthorized access. The encryption and decryption processes were tested to ensure data integrity and security. The results showed that the implementation of AES 128-bit successfully maintained the confidentiality and security of transaction data without reducing system performance. This study concludes that the AES 128-bit algorithm is an effective solution to improve transaction data security in the coffee industry, especially in small and medium-sized businesses such as Kopi Tuku.

Internal Re-keying Based Modified AES

Objectives: Masking and re-keying are two major countermeasures against the Power Side Channel Analysis attacks. Re-keying has either secret sharing overhead or needs to be used in synchronization. The Advanced Encryption System (AES) has been modified in the proposed scheme and uses re-keying without the need for secret random sharing or need for synchronized communication. The research proposed a modified AES scheme and validated its effectiveness with the AES. Methods: This study proposes modifying AES and then implementing it as software encryption using Python. As the AES has been modified, the proposed scheme should also match its strength to be further used against the Power Analysis Attack. Avalanche parameter is used to check the proposed solution strength. A data set of 10000, 50000, and 100000 records were generated to test the avalanche effect. The avalanche of existing AES and modified AES are then compared. Findings: The results indicate that the avalanche effect for both AES and the modified AES remains equivalent for the supplied dataset. To analyze further, the avalanche distribution is analyzed and randomness is checked using the Shannon entropy and found that the modified AES provides 0.5% more randomness against the AES. Hence, the modified AES fulfills the benchmark criteria to further check its strength against the Power Side Channel Analysis attacks. Novelty: The algorithm uses a part of the plain text to generate the round key making the resultant key unique and as it can be re-generated from the ciphertext and original key schedule on the decryption side, the random sharing is not needed. Keywords: Masking, Random Sharing Overhead, Differential Power Analysis (DPA), AES, Avalanche Effect, Re-keying

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.

Analysis Of The Development Of An Early Detection System For Cryptographic-Based Ransomware Attacks In A Cloud Environment

In the digital era, ransomware attacks have become a significant threat to security systems, especially in cloud computing environments. These attacks encrypt victim data and demand ransom, causing considerable financial and operational losses. This Study aims to develop a cryptography-based early detection system for ransomware attacks to protect data in cloud environments. Using the Systematic Literature Review (SLR) approach, this Study analyzes literature related to ransomware attacks, cryptographic algorithms, and cloud security. Data are obtained from indexed journals, books, and conferences. The Study's results showed that the implementation of cryptographic algorithms, such as Advanced Encryption Standard (AES), can improve the efficiency and effectiveness of ransomware detection. This system managed to reduce detection time by 48.28%, increase the success rate of data protection from 60% to 95%, and almost double the amount of data protected. This implementation strengthens data security, minimizes the impact of ransomware, and ensures the continuity of cloud user operations. The implications of this Study support the existing literature on the importance of cryptography in mitigating digital security threats while providing practical guidance for organizations in adopting this technology. Further research is recommended to integrate cryptographic algorithms with technologies such as blockchain to increase the scale and complexity of data protection in a broader cloud environment.

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

Medical Data Security using Deep Learning based Key Generation, Quantum Key Exchange and Modified AES

Medical data security refers to the protection and safeguarding of sensitive patient data in the healthcare domain. It encompasses various measures and protocols designed to ensure the confidentiality, integrity, and availability of medical information. This includes not only medical imaging data but also electronic health records (EHRs), medical test results, patient demographics, and other personally identifiable information (PII). Medical data security is of utmost importance due to several reasons like patient privacy, preventing unauthorized access or disclosure of personal health information, risk of data breaches and identity theft. This paper presents a medical data security framework using deep learning based key generation, quantum key exchange and modified Advanced Encryption Standard (AES). Deep learning-based random number generation for encryption key is an approach that utilizes deep learning models to generate secure and unpredictable sequences of numbers that can be used as encryption keys. Traditional random number generators rely on algorithms or physical processes to generate randomness. Deep learning, on the other hand, offers an alternative approach by leveraging the power of neural networks to learn patterns and generate seemingly random sequences. This generated random number is used as key for quantum key exchange. The proposed framework uses BB84 protocol the utilizes principles of quantum mechanics to achieve secure key exchange. This ensures the confidentiality and integrity of the transmitted data. The AES algorithm is a widely used symmetric encryption algorithm that is known for its high level of security and efficiency. The conventional mix column operation is replaced with low complex algorithm for better performance.

Application of Advanced Encryption Standard (AES) Algorithm in E-Commerce Login System for User Data Security

E-commerce becomes an electronic media that uses a login system used by users. User data in the form of usernames and passwords is vulnerable to hacking. One technique to improve user security is the implementation of AES algorithms on login systems in E-Commerce applications. The purpose of this study is to apply the AES algorithm in the login system of e-commerce websites and analyze the improvement of information security for users after the implementation is carried out. The research method used is an experiment with the application of the use of the AES algorithm before and after. Therefore, the application of the AES algorithm on the login system of e-commerce websites can be used as a solution to improve user data security. Testing using Wireshark and Burpsuite tools. The results obtained are that AES successfully secures the username and password on the e-commerce login system .

Dynamic Key Replacement Mechanism for Lightweight Internet of Things Microcontrollers to Resist Side-Channel Attacks

5G technology and IoT devices are improving efficiency and quality of life across many sectors. IoT devices are often used in open environments where they handle sensitive data. This makes them vulnerable to side-channel attacks (SCAs), where attackers can intercept and analyze the electromagnetic signals emitted by microcontroller units (MCUs) to expose encryption keys and compromise sensitive data. To address this critical vulnerability, this study proposes a novel dynamic key replacement mechanism specifically designed for lightweight IoT microcontrollers. The mechanism integrates Moving Target Defense (MTD) with a lightweight Diffie–Hellman (D-H) key exchange protocol and AES-128 encryption to provide robust protection against SCAs. Unlike traditional approaches, the proposed mechanism dynamically updates encryption keys during each cryptographic cycle, effectively mitigating the risk of key reuse—a primary vulnerability exploited in SCAs. The lightweight D-H key exchange ensures that even resource-constrained IoT devices can securely perform key exchanges without significant computational overhead. Experimental results demonstrate the practicality and security of the proposed mechanism, achieving key updates with minimal time overhead, ranging from 12 to 50 milliseconds per encryption transmission. Moreover, the approach shows strong resilience against template attacks, with only two out of sixteen AES-128 subkeys compromised after 20,000 attack attempts—a notable improvement over existing countermeasures. The key innovation of this study lies in the seamless integration of MTD with lightweight cryptographic protocols, striking a balance between security and performance. This dynamic key replacement mechanism offers an effective, scalable, and resource-efficient solution for IoT applications, particularly in scenarios that demand robust protection against SCAs and low-latency performance.

An Energy-efficient Parallel ASIC Implementation of Advanced Encryption Standard (AES) Algorithm Robust against Side-channel Attacks

Encryption becomes more crucial than ever in an increasingly interconnected world. Advanced Encryption Standard (AES) is still considered secure after more than 20 years thanks to its mathematical properties. However, side-channel attacks (SCA) threaten improper AES implementations. In this paper, different AES implementations are introduced, and their resistances against power SCA, namely Correlation Power Analysis (CPA) attack, are shown. For energy efficiency, the increase in power consumption due to the extras added for countering SCA was minimized by register-level organizations and process-related optimizations. Different AES implementations were constructed and processed through Cadence ASIC flow (TSMC 65 nm LP technology). SCA resistance was evaluated using the ChipWhisperer platform operating on realistic power consumption values obtained after RTL-to-GDSII flow. The results demonstrate that pipelining and unrolling the AES rounds increase the SCA resistance at the expense of a minimal reduction in energy efficiency. The proposed implementations are suitable for use with different side-channel attack countermeasures.

Aplikasi Enkripsi Pesan E-Mail Menggunakan Hybrid Cryptosystem AES dan RSA

The security of communication via e-mail is increasingly important in today's digital era, where cyber attacks are increasing. These threats require the development of effective solutions to protect the confidentiality and integrity of information in e-mail. This study presents the development of an e-mail messaging application that employs a hybrid cryptosystem combining Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms. The aim is to improve the security of e-mail communications by utilizing the strengths of both symmetric and asymmetric encryption methods. AES is used for its efficiency in encrypting large amounts of data, while RSA ensures secure key exchange. The application tested with blackbox testing to verify its functionality and brute force testing to assess the encryption's robustness. The results show that the application successfully passed the blackbox testing, demonstrating that it functions as intended. The encryption results of the application are also excellent, as evidenced by brute force testing which shows that the encryption cannot be broken. This hybrid approach, by integrating AES and RSA, provides a practical solution for enhancing e-mail security.

Multi-layer access control for cloud data using improved DBSCAN, AES, homomorphic encryption, and RMAAC for text, image, and video

With the growing demand for cloud storage solutions that can handle diverse data types such as text, images, and videos, ensuring robust access control and security becomes important. This paper proposes a novel multi-layer access control framework for cloud environments, incorporating advanced Clustering Filtering Techniques with an Improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to enhance search efficiency across multiple data layers. This clustering approach enables quick and accurate retrieval of text, image, and video content by efficiently organizing data based on similarity. To ensure data privacy and security, employs a hybrid encryption approach, combining Advanced Encryption Standard (AES) for data at rest and Homomorphic Encryption (HE) for data in use, allowing secure data manipulation without compromising confidentiality. The access control mechanism is further strengthened by introducing a Role-based Multi-Attribute Access Control (RMAAC) model, which grants permissions based on a user’s role, attributes, and the sensitivity level of the data being accessed. This fine-grained control restricts unauthorized access while supporting flexible policies for different data types. Simulation results demonstrate that the proposed framework significantly improves data retrieval speed, security, and clustering performance, making it an effective solution for cloud storage systems handling diverse media formats.

Blockchain Based Cryptographic Algorithm for Data Protection in Electronic Voting System

The development of digital world created advancement of technology, by which the data services are electronically transacted. Electronic voting is used to improve the election system in several ways. But technical issues associated with protection of data, is considered as a major concern leading to illegal activities and threats. Some of the conventional methods used in securing e-votes are matching finger prints of individual, control unit accessibility, which provides decreased capability of attaining proper authenticity and security. Even several cryptographic methods are used for data security, but produced limited accuracy. So, to improve the reliability and accuracy of data protection in e-voting, combination of AES (Advanced Encryption System) and RSA (Rivest, Shamir, Adleman) combined with blockchain technology is implemented. AES uses larger key size which makes the data privacy robust. But encryption and decryption time produces decreases computational speed. So, AES is combined with RSA, as it uses shorter key length and easy to implement with block-chain algorithm, it is deployed to decrease encryption and decryption time, thus producing highly sensitive data security. The performance of the system is validated by calculating the encryption and decryption time, block size and verification time of original data with output data.

VLSI Implementation of AES Block Cipher Based Data Hiding in Image Processing

A common symmetric key block cypher for protecting electronic data is called the Advanced Encryption Standard (AES). The implementation of the AES algorithm using Very-Large-Scale Integration (VLSI) is examined in this study. Cryptographic algorithms can be realised in hardware thanks to VLSI, which has advantages over software implementations in terms of increased processing speed and security. In this work, an AES block cypher core designed and implemented with VLSI techniques is described. We investigate various architectural designs to maximise performance indicators such as area, power consumption, and throughput. The trade-offs between different implementation options and design concerns for important components such as S-boxes will be covered. To illustrate the performance and usefulness of the implemented AES core, simulation results will be given. This paper examines the VLSI implementation of the Advanced Encryption Standard (AES), with a focus on image processing applications, even though it is essential for general data security. Effective on-chip encryption and decryption can be included into image processing systems by implementing the AES algorithm into specialised hardware circuits. Benefits of this VLSI design include enhanced performance over software-based solutions on resource-constrained image processing devices, the ability to encrypt images in real-time for secure transmission or storage, and the potential for reduced power usage for battery-powered applications.

In-memory encryption using the advanced encryption standard.

Encryption and decryption of data with very low latency and high energy efficiency is desirable in almost every application that deals with sensitive data. The advanced encryption standard (AES) is a widely adopted algorithm in symmetric key cryptography with numerous efficient implementations. Nonetheless, in scenarios involving extensive data processing, the primary limitations on performance and efficiency arise from data movement between memory and the processor, rather than data processing itself. In this article, we present a novel in-memory computing (IMC) approach for AES encryption and key-expansion, and experimentally validate it on an IMC prototype chip based on phase-change memory (PCM) technology. We leverage operators stored in PCM crossbar arrays to achieve the flexibility to tune performance at runtime based on the amount of free storage available in the memory system. In addition, we introduce a method for parallel in-memory polynomial modular multiplication and evaluate the potential of intrinsic stochastic properties of PCM devices for random key generation. We show how to further improve efficiency with minimal additional auxiliary circuitry. To evaluate the performance within a custom-built large-scale in-memory AES system, we design and implement a cycle-accurate simulator that integrates parameters from Spice simulations for detailed latency and energy consumption analysis of the AES algorithm. Our evaluations indicate that our IMC-based AES approach outperforms state-of-the-art methods, achieving speed factor improvements of up to 19.7 at equivalent energy efficiency.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.

Comparative Analysis Between Advanced Encryption Standard and Fully Homomorphic Encryption Algorithm to Secure Data in Financial Technology Applications

This research discusses the comparison between two encryption algorithms, namely Advanced Encryption Standard (AES) and Fully Homomorphic Encryption (FHE), in the context of data security in Financial Technology (Fintech) applications. The main aim of this research is to analyze the speed and efficiency of the two algorithms to provide information and motivation to Fintech Application business actors to determine the right algorithm for securing data. The research results show that AES is faster and more efficient in terms of encryption and decryption compared to FHE. For encryption, the AES algorithm is 1,100 times faster than the FHE algorithm. For decryption, the AES algorithm is 581 times faster than the FHE algorithm. For arithmetic processing, AES is 132 times faster than FHE. CPU consumption for AES encryption is 35.93% lower CPU usage than FHE. In AES decryption 10.31% lower than FHE for CPU usage. In the arithmetic process AES is 9.33% lower in usage than FHE. For memory usage in the FHE encryption process, it has an advantage, namely 2.3 times lower than AES for memory usage. During decryption, AES memory usage is superior with memory consumption 54 times lower than FHE. For the arithmetic process, AES uses 4.3 times lower memory than FHE. Overall AES provides speed and low resource consumption, this makes AES very suitable for use in Fintech applications that require speed and efficiency. Even though FHE has advantages in memory usage during encryption alone, this is not enough because it takes a long time to carry out the encryption process. This research suggests that further research will attempt to make the FHE algorithm more efficient and faster in processing data, this is considering the potential of FHE which is able to process encrypted data

Design of Malicious Hardware Trojans in AES Crypto system

Hardware Trojans pose a significant threat to the security and integrity of cryptographic systems, particularly in Advanced Encryption Standard (AES) implementations, which are widely used in securing sensitive data. These malicious modifications to integrated circuits (ICs) can compromise the confidentiality and reliability of AES cryptographic operations by introducing covert backdoors, information leakage channels, or functional disruptions. Hardware Trojans are typically designed to evade detection during design-time validation and post-manufacturing testing, often activating only under specific triggers such as rare input patterns or environmental conditions. In AES systems, Trojans can manipulate the encryption process by leaking secret keys through side-channel information such as power consumption, timing variations, or electromagnetic emissions. Some Trojans directly alter the encryption algorithm, weakening the cryptographic strength and rendering encrypted data vulnerable to attacks. Attackers may also embed Trojans at the Register Transfer Level (RTL) or gate-level design, leveraging the inherent complexity of AES circuits to conceal their presence. Detection and mitigation of hardware Trojans in AES implementations are challenging due to their stealthy nature. Techniques such as side-channel analysis, functional verification, and static code analysis have been developed, but sophisticated Trojans often bypass these methods. Advanced countermeasures include runtime monitoring, hardware obfuscation, and Trojan-resilient design methodologies. This paper explores the implications of hardware Trojans in AES cryptographic systems, analysing their design, potential attack vectors, and impacts on data security. Furthermore, it discusses state-of-the-art detection and prevention techniques, highlighting gaps and future research directions. Given the critical role of AES in securing financial, military, and consumer data, addressing the hardware Trojan threat is paramount to ensuring trust in cryptographic hardware and safeguarding against adversarial exploitation.

ADVANCED ENCRYPTION STANDARD (AES) IMPLEMENTATION EFFICIENCY USING JAVA AND NODE.JS PLATFORMS

The rapid advancement of communication technologies, such as satellite networks, mobile, internet, and terrestrial communications, has created an urgent need to protect sensitive data from potential attacks. This is particularly crucial as photos transmitted through unreliable channels may contain sensitive or confidential information. This study evaluates the effectiveness of the Advanced Encryption Standard (AES) algorithm implemented in Java and Node.js, focusing on their performance in data encryption and decryption. The research employs AES in Cipher Block Chaining (CBC) mode, using 128-bit keys for Java and 256-bit keys for Node.js. It utilizes the Java Cryptography Architecture (JCA) and Java Cryptography Extension (JCE) to create an optimized runtime environment with advanced cryptographic libraries. The result indicate that Java's AES-128 implementation is more efficient than Node.js's AES-256, particularly in terms of speed and data processing capabilities as seen in figure 11 taking Java 2.00ns to encrypt and decrypt before the Node.js algorithm that couldn’t complete the process but remain at 0.75ns. Suggesting that specific use case and requirements should be considered when choosing between the two platforms for AES encryption. Java generally outperforms Node.js in efficiency, but Node.js provides essential cryptographic functions through its built-in 'crypto' module. Overall, the research underscores the advantages of using the AES algorithm across these platforms while demonstrating the varying performance characteristics between them.

Design and Implementation of FPGA Based Optimized Multicore Processors

The rapid development of system-on-chip (SoC) architectures has led to increasingly complex systems facing issues such as multi-threading failures and potential failures. To address these issues, the project announced the development of a multi-student design that combines machine learning algorithms and strong cryptographic techniques to improve performance and security. Data integrity, authentication, and encryption/decryption using private and public cryptographic keys. Use advanced cryptographic algorithms such as Secure Hash Algorithm (SHA- 256) and Advanced Encryption Standard (AES) to strongly protect sensitive data. These measures ensure data integrity while also preventing unauthorized access. Exchange data on multi-core processor architecture and peripheral devices. This setting optimizes system performance, making the system suitable for real-time and mission-critical applications. Using these resources, the architecture can be efficient in machine learning while maintaining high performance. This integration allows for detection of data changes between hardware and software to ensure seamless operation and interoperability within the system. The best in security, high performance, and flexibility across the enterprise. Keywords-AHB protocol, clock register, FPGA, machine learning, support vector machine, automotive electronics design.