Currently, valuable information requires security because cyber threats have escalated. The CIA Triad is one of the core concepts in information security which defines three main objectives of any security program: Confidentiality, Integrity, and Availability. Each component of the triad covers a different aspect to ensure proper protection and management of information. Confidentiality ensures that sensitive details are only obtained by authorized personnel or corporations. Maintaining integrity involves ensuring data accuracy and reliability by preventing unauthorized changes. Availability guarantees that information and related systems can be used as needed. This paper presents an innovative hybrid form of security system aiming at selecting the best cryptographic and steganographic techniques. Additionally, the Huffman encoding scheme is employed to increase the embedding capacity of the proposed mechanism. Thus, cryptography and steganography are taken as measures in the field of communication and information security. Cryptography and steganography are two different but connected areas within the broader information security domain. Both methods share some common features in securing information although they differ in their functions and performance characteristics. In this paper, an integrated method that combines Cryptography, compression, steganography, and the InterPlanetary File System has been presented as a basis for information security. Consequently, this system is implemented by using a Python Tkinter module that makes it possible to be used in real-life situations without much difficulty. This application enables its users to encrypt messages, compress them, and hide them inside other files like they were of no significance at all. The interface design ensures that users can move around different functions easily without demanding technological expertise or knowledge. A flexible framework is offered through which sensitive data can be secured against various digital landscapes subjecting to current threats in data security. In general, this paper demonstrates how cryptography, compression, LSB steganography, and IPFS may be combined thus showing the practicality and benefits of such a unified approach for safeguarding valuable digital records. The novelty of this work lies in the unified implementation of ECC-based key exchange, AES-GCM encryption, Huffman compression, LSB steganography, and IPFS decentralized storage in a real-world deployable GUI framework. Unlike prior studies that address these methods separately, this system integrates them into a streamlined pipeline that enhances embedding efficiency, encryption robustness, and secure data retrieval over decentralized platforms. The holistic approach and practical usability make it distinct from existing security models. In general, this paper demonstrates how cryptography, compression, LSB steganography, and IPFS may be combined, thus showing the practicality and benefits of such a unified approach for safeguarding valuable digital records.

Data security in village administration service systems is a critical concern, as these systems manage sensitive personal information such as full names, National Identity Numbers (NIK), Family Card (KK) numbers, addresses, and supporting documents. Many existing village administration systems store such data without adequate protection, increasing the risk of data leakage, identity theft, and unauthorized access. This study implements the Advanced Encryption Standard (AES-256) algorithm to enhance data security in a village administration services website. The research methodology includes system requirements analysis, system design, AES-256 implementation for personal data and document encryption, and functional and performance evaluation. The system automatically encrypts all sensitive data before storing it in the database and decrypts it only for authorized administrators. In addition, the system supports secure processing of statistical Excel files to facilitate the Desa CANTIK program. Experimental results show that the proposed system provides strong data protection with minimal performance overhead, averaging less than 120 ms for encrypting and decrypting 5 MB files, while maintaining acceptable CPU and memory usage. These results indicate that AES-256 can be effectively integrated into village administration services to improve data security without compromising system efficiency.

Biometric ticketing systems utilizing fingerprint recognition provide enhanced security and convenience for passenger identification in public transportation. However, the transmission of fingerprint templates over wireless networks without adequate cryptographic protection exposes the system to interception attacks and privacy breaches. This research implements AES-128 encryption in Cipher Block Chaining (CBC) mode to protect fingerprint templates transmitted within an ESP32-based biometric ticketing system. The implementation leverages the ESP32’s integrated mbedTLS library with hardware acceleration to achieve efficient cryptographic operations. Experimental evaluation using 10 fingerprint template samples demonstrates a 100% success rate for encryption-decryption operations. Performance measurements indicate an average encryption latency of 2.30 ms and decryption latency of 2.10 ms, with a data size overhead of 32 bytes (6.25%) due to Initialization Vector (IV) and PKCS7 padding. The results confirm that the proposed encryption scheme effectively secures biometric data transmission while maintaining system responsiveness suitable for real-time applications.

The deployment of Product Lifecycle Management applications such as Teamcenter has traditionally been characterized by manual, time-intensive processes that introduce configuration inconsistencies, limit scalability, and constrain organizational agility in responding to dynamic business requirements. This article presents a comprehensive cloud-native deployment framework that integrates AWS CloudFormation for declarative infrastructure provisioning with Chef configuration management to automate the complete PLM deployment lifecycle. The framework adopts a layered architecture implementing Infrastructure-as-Code principles, enabling consistent and repeatable provisioning of multi-tier PLM infrastructure while eliminating manual configuration steps through automated cookbook-based application installation and configuration. Implementation validation conducted within a large-scale manufacturing enterprise environment demonstrates substantial improvements in deployment efficiency, with dramatic reductions in deployment cycle times and near-complete elimination of manual intervention requirements. The article reveals superior consistency, predictability, and scalability characteristics of automated deployments relative to traditional manual approaches, while cost analysis establishes compelling return on investment through personnel time savings and operational expense optimization. The framework addresses critical security, compliance, and governance requirements through layered security architectures, encryption implementations, and comprehensive audit logging capabilities. Beyond immediate deployment efficiency gains, the article establishes foundational competencies in cloud-native practices that support broader digital transformation initiatives and position organizations for accelerated innovation cycles. The article identifies critical success factors, including executive sponsorship, cross-functional collaboration, and phased implementation approaches, while acknowledging limitations related to network connectivity dependencies, regulatory constraints, and application-specific hardware requirements that may constrain applicability in certain contexts.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

The paper presents the Integrated Cybersecurity Evaluation and Risk Mitigation Framework (ICERMF), which utilizes wavelet-based cryptographic transforms in conjunction with AES-256 encryption to enhance data security and improve computational efficiency. The framework utilizes multi-resolution wavelet analysis to generate adaptive keys, resulting in a 30% decrease in encryption overhead compared to traditional methods while maintaining NIST-certified robustness. The combination of these innovations with Zero Trust Architecture and blockchain validation, and AI-driven monitoring enables ICERMF to fulfill SDG 9 (Industry, Innovation, and Infrastructure) through scalable security solutions and SDG 16 (Peace, Justice, and Strong Institutions) by protecting essential digital infrastructure. The wavelet-optimized encryption demonstrates a 92.4% correlation (ℓ; p < 0.01) between risk scores and breach reduction according to empirical validation. The results demonstrate its ability to improve data security and transaction integrity and real-time threat detection capabilities.

The proliferation of consumer electronics (CE), particularly IoT devices such as smart home hubs and in-vehicle systems, is generating massive amounts of user data, creating an urgent need for secure and manageable cloud storage solutions tailored for consumer technology environments. While cloud storage alleviates the burden on local devices and enables convenient data access, it introduces significant security concerns for consumer applications: potential data loss from cloud servers and unauthorized access during data sharing. Existing attribute-based encryption (ABE) implementations often suffer from low efficiency, which in turn leads to substantial storage overhead, while existing schemes seldom account for the organizational structures common in consumer scenarios—such as a family unit or a fleet of interconnected devices. This paper proposes an efficient data sharing mechanism with integrity auditing functionality for cloud storage. Specifically designed for such consumer-grade IoT environments, the mechanism combines flexible access control with multi-replica cloud storage to reduce the risk of data loss. We design a group manager (GM)-driven upload management subsystem to efficiently handle data uploads from organizational entities. Finally, security and performance analyses were conducted on the integrated system designed in this article. The results indicate that the proposed scheme has good security and strong performance advantages.

Smart homes have experienced rapid development with the adoption of Internet of Things (IoT) technology. The use of IoT in the context of home security provides the ability to control, monitor, and secure the home environment more efficiently and effectively. However, the increasing connection of these devices also brings significant security risks, such as threats to personal data, privacy, and system integrity. This study explores the latest approaches and technologies in securing IoT-based homes. The main focus is on identifying and mitigating security risks that arise from the connectivity of devices such as CCTV cameras, door sensors, alarm systems, and other control devices. The research method involves analyzing the communication protocols used, access rights management, and the implementation of encryption and other security techniques. The overall system success percentage is 85%, which shows quite good performance. However, to improve the quality of the system, further attention is needed to the stability of the internet network used by NodeMCU. In conclusion, the integration of IoT in smart homes promises convenience and comfort, but must be accompanied by serious efforts to ensure user security and privacy are maintained.

This study provides a comprehensive exploration of Super APIs and their advantages, specifically focusing on their Cross-Chain Communication Protocol. The primary objective is to enhance the performance of Super API applications, emphasizing speed, decentralization, privacy, security, and scalability. The research encompasses data sources, data collection methodology, and performance evaluation metrics. Comparative analysis is conducted to evaluate the performance of Super APIs in comparison to other Cross-Chain solutions. The research primarily focuses on the development of our Super API, addressing accuracy, speed, security, and scalability, particularly within financial transactions. Through thorough data analysis and optimization, significant advancements have been achieved in Super APIs. Performance improvements include reduced response times, enhanced accuracy, and increased security through the implementation of AES encryption. JWT is employed for authentication and encryption, while Hypercube effectively manages Super API nodes. Furthermore, our Super API is designed to handle a wide range of data scales, accommodating both small and big data.

Data and image security are critical concerns in modern digital communication. The Advanced Encryption Stan- dard (AES) is widely used for secure data transmission due to its robustness and efficiency. This paper presents an FPGA-based implementation of AES encryption and decryption using Verilog for secure data and image transmission. The proposed design is implemented on a Xilinx FPGA platform to ensure high- speed encryption with minimal hardware resource utilization. The AES algorithm is optimized using pipeline architecture to enhance performance, supporting 128-bit encryption and decryption. Experimental results demonstrate the systems ability to encrypt and decrypt both textual and image data efficiently. Power consumption, resource utilization, and timing performance are analyzed, showing that FPGA-based AES provides a secure and efficient solution for real-time data protection in embedded systems. The proposed approach is suitable for applications in secure communication, cloud storage, and military-grade encryption.

This paper proposes a new FPGA-based architecture for secure image encryption, utilizing chaotic maps for permutation and substitution mechanisms. Specifically, Duffing map is employed to generate permutation addresses, and the Henon map for value substitution; both techniques produce pseudo-random sequences with strong sensitivity to initial conditions. The hardware design implemented using Xilinx System Generator and deployed on an Artix-7 FPGA supports both grayscale and RGB image formats. A comprehensive performance analysis, utilizing Mean Squared Error, Histogram analysis, Correlation Coefficient Analysis, Number of Pixels Change Rate, and Unified Average Changing Intensity, demonstrates perfect image recovery and robust encryption resistance. The keys used in the encryption system are passed using the NIST STS randomness test. The single-round and multi-round designs deliver a processing acceleration ranging from 2.11 to 3.92 for image sizes 128×128 to 1024×1024 pixels, highlighting their effectiveness and practicality for real-time encryption in low-latency environments.

The integration of cyber-physical systems (CPS) with the Internet of Things (IoT) in smart city transportation systems presents promising opportunities, however poses challenges in data privacy and security. This paper proposes a novel approach to address these challenges by incorporating Elliptic Curve Cryptography (ECC) for heightened data security in CPS-IoT integrated transportation. ECC serves as a robust cryptographic solution, safeguarding data confidentiality and integrity during exchanges between IoT devices and infrastructure. Through an in-depth examination of privacy threats and vulnerabilities inherent in such systems, the paper highlights ECC’s potential to mitigate risks. Furthermore, it explores the implementation of ECC-based encryption and decryption mechanisms to ensure secure communication and data exchange. Additionally, the paper discusses the adoption of Transport Layer Security (TLS) as a secure data communication protocol and enhancing the overall security and efficiency of CPS-IoT transportation systems. To validate the effectiveness of our framework, we compare it with an assumed framework that utilizes Differential Privacy method and SSL, highlighting the advantages of our approach in terms of security, efficiency, and practicality. By fostering a secure data environment, ECC and TLS contribute to informed decision-making, resource optimization, and the advancement of sustainable transportation infrastructure, thus fostering a circular economy.

Homomorphic encryption is well known to researchers, yet its application in image processing is scarce. The diversity of image processing algorithms makes homomorphic encryption implementation challenging. Current research often uses the CKKS algorithm, but it has core bottlenecks in image encryption, such as the mismatch between image data and the homomorphic operation mechanism, high 2D-structure-induced costs, noise-related visual quality damage, and poor nonlinear operational support. This study, based on image pixel characteristics, analyzes homomorphic encryption via pixel scrambling algorithms. Using magic square, Arnold, Henon map, and Hilbert curve transformations as starting points, it reveals their homomorphic properties in image processing. This further explores general pixel scrambling algorithm homomorphic encryption properties, offering valuable insights for homomorphic encryption applications in image processing.

In recent times, the amount of data exchanged between the cloud storage and the users has proliferated. The security of that data is also critical. To secure that data and to enhance its integrity, it should be encrypted before being uploaded into the cloud. In this work, a side-channel attack-secured additive homomorphic encryption is implemented using elliptic curve cryptography on an FPGA platform. An elliptic curve scalar multiplication, which is the critical component of elliptic curve cryptography, is designed in the general prime field using standard projective coordinate representation and implemented for 192, 224, and 256 bits as per the left-to-right double-and-add algorithm using radix-4 Booth-encoded modular multipliers in both FPGA devices and the ASIC platform. A minimum of 8242 slices is required to implement the proposed 256-bit elliptic curve scalar multiplication in the Virtex-6 FPGA device. The area of the proposed 192, 224, and 256-bit elliptic curve scalar multiplication is estimated as 149.225K, 208.178K, and 266.981 KGE in the ASIC using Cadence gpdk-45 nm technology libraries. A correlation power analysis attack is mounted on the FPGA implementation of the proposed elliptic curve scalar multiplication with an 8-bit data size to determine the value of scalar ‘n’. The attack is successful with a minimum of 2301 traces, and a high correlation coefficient value is obtained. Scalar randomization is proposed and integrated with the design as a countermeasure part to thwart the correlation power analysis attack, which is successful, and hence the left-to-right double-and-add algorithm used to determine elliptic curve scalar multiplication is made secure against side-channel attacks. This secured hardware implementation of elliptic curve cryptography is utilized to encrypt the data uploaded to the cloud, where additive homomorphic encryption is employed to process the data. Hence, additive homomorphic encryption becomes side-channel attack resilient, and cloud computations are secured. • The hardware implementation of 192, 224, and 256-bit elliptic curve scalar multiplication over the general prime field in standard projective coordinate representation is optimized to achieve minimum area compared to the existing works in the literature. • An 8-bit ECSM of the proposed method is implemented on the SAKURA-G FPGA board, and the power traces are collected during the corresponding execution. A correlation power analysis attack is mounted on the SAKURA-G FPGA board, and the scalar value is extracted successfully, utilizing the hypothetical power values of 8-bit elliptic curve scalar multiplication and the captured power traces to demonstrate the vulnerability present in the left-to-right double-and-add algorithm used to determine elliptic curve scalar multiplication. • Scalar randomization using the number of points in the subgroup formed by the chosen base point of the curve is implemented as a novel countermeasure to overcome the correlation power analysis attack and also validated. • Hence, the left-to-right double-and-add algorithm used to determine elliptic curve scalar multiplication is made secure against correlation power analysis attacks, simple power analysis attacks, and timing attacks. Then a secured EC-ElGamal scheme is implemented to perform elliptic curve cryptography-based encryption before the data is uploaded to cloud storage. • Hence, the additive homomorphic encryption employed to process the cloud data, which is encrypted using secured elliptic curve cryptography, also becomes side-channel attack-tolerant.

The widespread adoption of encryption technologies has raised concerns about the protection and vulnerability of digital data. Protocol reverse engineering (PRE) is a critical methodology for evaluating and validating encryption implementations. It involves analyzing network traffic, message logging, and model checking processes. The key security properties of encryption include confidentiality, integrity, availability, and non-repudiation. However, the dual-use nature of encryption presents challenges for digital forensics and law enforcement investigations. Malicious actors can exploit encryption to conceal criminal activities and obstruct justice. Digital investigators and forensic specialists must develop expertise in specialized decryption tools, steganographic detection methods, and advanced analytical techniques to uncover hidden or obfuscated data. Cryptographic service implementations vary significantly in performance characteristics and security effectiveness, with key size, algorithm type, encryption rounds, algorithm complexity, and data size influencing performance.

This paper presents an in-depth technical analysis of the Akira ransomware family, which has emerged as a prominent threat in the cybersecurity landscape between 2023 and 2025. Known for its advanced encryption techniques, anti-analysis mechanisms and targeted extortion campaigns, Akira demonstrates a sophisticated evolution of modern ransomware.The study applies both static and dynamic analysis methodologies to deconstruct Akira’s behavior and internal structure. Static analysis using tools such as Ghidra, PEStudio and FLOSS is used to extract key artifacts, analyze its PE structure and examine its encryption implementation and obfuscation techniques. Complementary dynamic analysis is performed in controlled sandbox environments using Any.Run, Procmon and Wireshark, revealing the ransomware's real-time activities, including file encryption behavior, registry modifications and potential network communications with command-and-control (C2) infrastructure. The research identifies critical indicators of compromise (IOCs), analyzes the encryption flow involving ChaCha20 and RSA and documents Akira’s ransomware note deployment and persistence mechanisms. The findings aim to support the development of more effective detection, classification and response frameworks in malware analysis and threat intelligence operations. This paper highlights how hybrid analysis techniques can uncover both surface-level and deeply embedded functionalities of emerging ransomware variants like Akira.

Artificial intelligence emerges as a transformative force in cybersecurity, revolutionizing how organizations protect sensitive data from increasingly sophisticated malicious attacks. The evolution from traditional rule-based systems to advanced AI-powered detection frameworks enables identification of subtle patterns and anomalies invisible to conventional security approaches. Through behavioral analytics, machine learning algorithms establish dynamic baselines of normal activity, allowing security systems to distinguish between legitimate variations and genuine threats with unprecedented precision. AI enhances data protection through optimized encryption implementation, intelligent masking strategies, and privacy-preserving computation methods that fundamentally alter the security-utility balance. Adaptive authentication frameworks leverage behavioral biometrics and risk-based models to provide continuous identity verification throughout user sessions, while AI-driven privilege management systems enforce least privilege principles dynamically across complex environments. The integration of these technologies with zero trust architectures creates comprehensive security frameworks capable of protecting sensitive data across distributed infrastructures where traditional perimeter defenses have become increasingly ineffective.

As the world increasingly depends on digital technologies, end-to-end data security has become paramount for protecting information throughout its lifecycle. This article examines the principles, challenges, and technologies associated with securing data in storage and network infrastructures. The evolution from perimeter-based security to comprehensive lifecycle protection is explored, highlighting the importance of integrated approaches encompassing encryption, access control, and continuous monitoring. Key management emerges as critical, with centralized and distributed architectures offering different benefits for resilience and administrative control. Performance considerations reveal trade-offs between security strength and operational efficiency across various encryption implementations. Advanced technologies including quantum-resistant cryptography, homomorphic encryption, and zero-trust architectures demonstrate promising capabilities for addressing emerging threats. The effectiveness of layered defense mechanisms implementing multiple complementary controls shows significant advantages over single-layer protection approaches. Case studies across financial services, healthcare, and public sectors illustrate successful implementations through executive leadership, clear governance structures, comprehensive data classification, and risk-based approaches. Ethical considerations emerge regarding monitoring scope, transparency, and privacy protection as security capabilities grow increasingly sophisticated.

Relational databases, an essential component in modern information systems, are vulnerable to various security threats, both internal such as abuse of access rights, and external such as SQL injection, malware, and hacking. Given these conditions, how can relevant mitigation strategies be implemented to protect data security in relational databases? This article aims to identify the main threats to relational database security and map out relevant mitigation strategies. The method used is a literature review of various recent scientific journals that discuss aspects of data security in the context of relational databases. The results of the review indicate that threats such as SQL injection can be overcome by strict input validation, abuse of access rights can be prevented through role-based access control (RBAC), malware attacks can be detected using an intrusion detection system (IDS), and hacking actions can be minimized through the implementation of data encryption. This study is expected to be a reference in designing effective security strategies to protect data in relational databases.

In today's digital era, the development of information technology has revolutionized various aspects of human life, including in the health sector. The use of electronic medical records in hospitals has become a common standard, allowing for more efficient storage of patient data and faster access for medical personnel. However, along with this progress, legal challenges have also emerged, especially related to the protection of patient personal data. The issues discussed are how the implications of cyber criminal law affect the protection of electronic medical records in hospitals, how the obstacles and barriers are in protecting electronic medical records at Mitra Medika Premier Hospital Medan and how cyber crime prevention efforts are in protecting electronic medical records at Mitra Medika Premier Hospital Medan. The research used empirical juridical by using data collection methods through field research and library research. Library studies are obtained by reading, studying and reviewing books, laws and regulations, journals or data in the form of library materials while field studies are conducted using interview methods. Some forms of violations that may occur in the management of electronic medical records at Mitra Medika Premier Hospital Medan are Unauthorized Access: When the login of an absent health worker is entrusted to another health worker to replace data entry and Data Retrieval: Patient data is used by unauthorized parties, for example laboratory result photos. Efforts made by Mitra Medika Premier Hospital Medan in protecting Health data include: Strengthening Technology Infrastructure: Implementation of firewalls, antiviruses, and data encryption, Routine Audits: Ensuring the system runs according to security standards, Increasing Staff Awareness: Regular training related to data protection and Updates/development of information technology systems, especially related to data security.