Data encryption standard based protection method of privacy data on Internet of Things

The article examines methods and information technologies aimed at ensuring the secure integration of the Ethereum blockchain with Internet of Things (IoT) systems. The relevance of the study is driven by the rapid development of IoT, which is accompanied by increasing cybersecurity threats, including unauthorized data access, man-in-the-middle (MITM) attacks, device identifier spoofing, and the low transparency of centralized systems. The use of Ethereum blockchain technology, particularly smart contracts, opens new opportunities for creating decentralized security management models for IoT devices, enhancing trust levels, automating processes, and minimizing third-party interference risks. The “Problem statement” section outlines the key challenges in securing IoT networks, including the vulnerabilities of centralized solutions, limited computational resources of devices, and the need to develop autonomous access control systems. The “Analysis of Recent Research and Publications” section summarizes modern approaches to integrating Ethereum blockchain into the IoT field, including tokenized identification mechanisms, access control, and transaction processing using smart contracts. It is noted that leading researchers suggest Layer-2 solutions (state channels, zk-rollups, Plasma) aimed at reducing the load on the main blockchain and improving scalability. The aim of the article is to systematize modern methods of integrating the Ethereum blockchain with IoT and develop recommendations for their implementation, considering the limited resources of devices. The “Research results” section presents a secure IoT system architecture concept based on decentralized account management, local storage of cryptographic keys on devices, the use of optimized transaction signing algorithms, and the introduction of a hybrid data storage model based on IPFS. The proposed model minimizes the risk of unauthorized access, increases transparency in the interaction between IoT devices, and reduces computational resource costs. The “Conclusions and prospects for further research” section emphasizes that the implementation of the Ethereum blockchain in IoT promotes the development of secure decentralized platforms. However, it requires addressing issues such as energy consumption, transaction costs, and optimizing client applications for low-performance devices. Future research should focus on developing effective scalability tools, adapting smart contracts to IoT specifics, and improving integration with cloud computing platforms for storing large data sets.

The integration of wireless sensor nodes (WSNs) in the Internet of things (IoT) may generate new security challenges for establishing secure channels between low-power sensor nodes and Internet hosts. It includes a bunch of challenges right from redefining the existing ones from scratch of legacy codes to the current trend of proposing and designing new key establishment and authentication protocols. This paper is the successful attempt in resource constraint environment like wireless sensor network (WSN) to integrate cipher authentication and lightweight key management solutions in one bundle so that it can be deployed in IoT domains WSN is also a part of IoT. The cipher should be authenticated, lightweight, and should be flexible to use in the cryptanalysis. The cipher gives the better authentication techniques and performance but limited to the flexibility. Lightweight block cipher (LBC) uses less computation and secures the data with low computational cost and it is used for many purposes like radio-frequency identification (RFID) tags and sensor network nodes. Advanced Encryption Standard is applied in many devices for the encryption, but it cannot be applied to the smaller embedded devices like wearable devices, etc. LBC works for the many devices with low resources, and research on the LBC has been increased. In this research, Data Encryption Standard (DES) and analyzed algorithm are used to increase the efficiency in terms of solving time and Improved Fault Analysis (IAFA) is used to increases the security of the LBC in wireless sensor node for IoT.

The security problem of the Internet of things (IOT) system has caused high attention in the information security (IS) industry. This paper studies the IS of the IOT based on perception and interaction technology. In the research, we put forward an IS protection method of the IOT based on the perception and interaction technology, and tested the method and the traditional IS protection method on the industrial IS attack and defense drill platform respectively. The test results show that the response time of the IOT IS measures based on perception and interaction technology proposed in this paper is 27 μs, while the traditional method needs 83 μs. From the perspective of false positive rate (FPR) and false negative rate (FNR), there are obvious differences between them. The FPR and FNR of the traditional method are 12.7% and 7.9% respectively, and the FPR and FNR of the method in this paper are 5.4% and 3.1%. The test results show that the IOT IS protection means based on perception and interaction technology can effectively ensure the IS of the IOT.

Minimizing the quantity of transmitted data and protecting the devices from the adversary environment are the main challenges hindering the advancement of Internet of Things (IoT) network. Based on the theorem of unique prime factorization that any natural number can be expressed as the product of powers of prime numbers, we introduce a new scheme in this paper for secure and efficient gathering of the IoT data. The proposed scheme consists of three phases: Setup, Secure data gathering, and Data reconstruction. In the Setup phase, the properties of the prime numbers to create an efficient clustering and routing tree algorithm are utilized to reduce the power consumption during data transmission process. The Secure data gathering phase includes secure intra and inter communication procedures. During secure intra-communication procedure, the Compressive Sensing (CS) based method of data compression and encryption is used. Moreover, the prime number properties and the Bezier curve function are used to solve the CS problems. In the secure inter-communication procedure, the security performance of the IoT based Wireless Sensor Networks (WSNs) is improved by combining Private and Public key algorithms with the consideration of the power limitation of IoT devices. Finally, the proposed scheme introduces an adept reconstruction algorithm which is a hybrid algorithm of Bees and Genetic Algorithm to successfully restore the actual sensor data from the compressed samples, which improves the reconstruction process.

A secure data transmission framework for IoT enabled healthcare

AI is going to change the face of healthcare through the bridge with the Internet of Medical Things (IoMT) by diagnosing problems, guaranteeing information security, and providing personalized care. This framework applies hybrid cloud-edge architecture for the realization of real-time diagnostics while reducing latency and leveraging AI, generative AI, and IoMT. Deep learning models enable better cancer detection and mainstream advancements in imaging, drug discovery, and treatment options enabled by generative AI. Federated learning guarantees privacy of data as data is retained at the edge while ensuring efficiency and has improved security, covered with a chaotic map-based encryption algorithm for safe data even in resource-constrained IoMT devices. The system offers real-time patient monitoring by adopting personal adaptable treatments and is scalable in healthcare environments, providing high diagnostic precision, enhanced security, and responsive medical care, making it a breakthrough in digital health in pursuit of a more tailored patient solution.

The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.

The incredible advancements in data transmission technology have opened up more potentials for data security than ever before. Numerous methods for data protection have been developed during the previous decades, including steganography and cryptography. The security and integrity of medical data have emerged as major barriers for healthcare service systems as the Internet of Things has evolved dramatically in the healthcare business. Communication between two devices securely is a difficult problem. Numerous cryptographic algorithms are already available, including data encryption standard (DES), Rivest–Shamir–Adleman (RSA), and advanced encryption standard (AES). In this paper, we present a hybrid security model for the protection of diagnostic text data contained in medical photographs. The proposed model is built by combining a proposed hybrid encryption system with either a 2D Discrete Wavelet Transform 1 Level (2D-DWT-1L) or a 2D Discrete Wavelet Transform 2 Level (2D-DWT-2L) steganography technique. The suggested model encrypts secret data and hides them using 2D-DWT-3L. As text covers, color and grayscale images are employed. The suggested system’s performance was tested using PSNR, SSIM, MSE, and Correlation. Associated to state-of-the-art approaches, the proposed model masked personal patient data with high capacity, imperceptibility and minimum deterioration in the received stego-image. We use MATLAB to build the proposed mechanism, and measures such as throughput and execution time are used to assess performance.

In recent years, data security has been a challenging endeavor, especially when the data is being transmitted every second. Internet of Things (IoT) involves continuously sending data over public networks, making the data vulnerable to various security threats. Therefore, ensuring the secure end-to-end communication of IoT data is critical. Cryptography and steganography have proven effective in providing secure connectivity for IoT devices. However, challenges in existing approaches include scalability, computational complexity, implementation, key management, trade-offs, evolving threats, and hyperparameter tuning. Therefore, in this paper, we propose EGCrypto, an efficient and secure model for IoT networks. EGCrypto utilizes a low-complexity elliptic Galois cryptography approach along with matrix XOR steganography to enhance security. To optimize its performance, we employ the zoning evolution of control attributes and adaptive mutation based self-adaptive differential evolution with fitness and diversity ranking. These techniques are utilized to fine-tune the hyperparameters of EGCrypto, enhancing its effectiveness and efficiency. The confidential IoT data is encrypted using low-complexity elliptic Galois cryptography. Following encryption, the encrypted data is embedded or hidden into cover blocks of an image, which are selected using the optimization algorithm. This ensures secure data communication in IoT architectures, as the encrypted data is transferred safely and can be easily recovered and decrypted at the receiving end. The experimental results demonstrate that EGCrypto outperforms competitive models with improvements of 1.8473% in peak signal to noise ratio (PSNR), 1.5490% in strutural similarity index metric (SSIM), 1.7682% in normalized root mean square error (NRMSE), 1.3829% in carrier capacity, and 1.9372% in embedding efficiency.

The rapid expansion of the Internet of Things (IoT) has introduced critical security challenges due to heterogeneous device capabilities, constrained computational and energy resources, large-scale connectivity, and dynamic network environments. Existing security solutions are often fragmented and fail to provide seamless protection across the entire IoT data lifecycle. This paper proposes a comprehensive end-to-end security architecture designed to protect IoT systems from the device layer to the application layer. The proposed framework integrates lightweight mutual authentication, efficient key management, secure communication protocols, data encryption at rest and in transit, fine-grained access control, and optional decentralized trust mechanisms. In addition, edge-level security functions such as intrusion detection and secure firmware updates are incorporated to enhance system resilience. The architecture ensures essential security properties including confidentiality, integrity, availability, authentication, and non-repudiation while maintaining low computational and communication overhead suitable for resource-constrained IoT devices. The framework is evaluated using performance metrics such as end-to-end latency, energy consumption, scalability, and resistance to common cyberattacks. Experimental results demonstrate improved resilience against routing attacks, replay attacks, impersonation, and man-in-the-middle attacks, with minimal impact on system performance, making the proposed approach suitable for secure and scalable IoT deployments.

Data security is the focus of information security. As a primary method, file encryption is adopted for ensuring data security. Encryption algorithms created to meet the Data Encryption Standard (DES) and the Advanced Encryption Standard (AES) are widely used in a variety of systems. These algorithms are computationally highly complex, thus, the efficiency of encrypting or decrypting large files can be drastically reduced. To this end, we propose an optimized algorithm that efficiently encrypts and decrypts large files by parallelizing processing tasks on a single heterogeneous many-core processor in the Sunway TaihuLight computer system. Firstly, we convert the serial DES and AES programs to our experimental platform. Then we implement a task assignment strategy to test the converted algorithms. Finally, in order to optimize parallelized algorithms and improve data transmission performance, we apply the master-slave communication optimization, the three-stage parallel pipeline, and vectorization. Extensive experiments demonstrate that our optimized algorithm is faster than the state-of-the-art open-source implementations of DES and AES. Compared with the serial processing algorithms, our parallelized DES and AES perform nearly 40 times and 72 times faster, respectively. The work described in this paper leverages existing methods and provides a sound basis for the direction of future research in data encryption.

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

Cryptography is defined as the analysis of encryption or secretive writing of information with the use of mathematical & logical concepts in order to prevent data from being compromised. Because of the growing security issues around Internet of Things (IoT) & artificial intelligence (AI) based applications, this method has gained in significance in computer technologies for banking & healthcare systems, transportation, as well as other implementations. Although each cryptographic strategy is designed to have its own unique strength, the application of a single cryptographic strategy into a systems has certain drawbacks, as will be discussed below. For example, the symmetric key encryption approach is a cost-effective way of protecting information that does not sacrifice security in the process. The distribution of the private key, on the other hand, is a significant issue. The asymmetrical method, on either side, overcomes the problem of private key transmission; nevertheless, the independent approach is slower and uses more computer resources as comparing to symmetric encryption. While a hash function, on the other hand, produces a distinctive & fixed-length signatures for a communication in order to ensure information security, the technique is just a one-way function that is not possible to reverse. As an option to addressing the security flaws of individual cryptographic schemes, the inclusion of many cryptographic schemes, also known as the hybridization approach, is being suggested, which has the advantage of increasing the efficiency of information security while also discussing the problem of key transfer. Existing IoT & AI domains that have adopted hybrid methods have been recognized, and a study has been carried out as per classification of the domain under consideration. The security of the networks and the data sent over the network is a top priority for network providers or network operators. As a result, cryptographic methods are used to protect the data throughout the data exchange process and during different interactions. Traditional cryptographic methods, on either side, are well-known, because hackers are aware of the answer to the problem. As a result, a fresh type of cryptographic method is needed, one that increases the security & complexities of the data encryption while maintaining its simplicity. An innovative hybrid cryptographic approach for enhancing data security throughout networks transmission is presented in this article, and the consequences of its implementation and evaluation are discussed. Throughout a comparative performance study, the suggested cryptographic method was able to identify the most efficient & enhanced encrypted message.

Issues of network threat directly affects the network security in any organization. One of the most prominent network threats is the intrusion of information. Government Information and Communications Technology Security Policy should be should be given a serious emphasis by every government organization. Internet of Things (IoT) is the latest technology that allows devices to establish connection with other devices through the Internet. The practicality of IoT devices causes many public and private institutions to adopt IoT technology to assist teaching and learning. In addition, one of the key feature of IoT device is to provide users with fast response through warning or notification. The purpose of this project is to design and develop a framework based on the IoT network to mitigate the threat and to design a threat management model for the computer laboratory at METrO Polytechnic Tasek Gelugor. Research tools used are literature study, questionnaire on threats and information security levels involve students and polytechnic staff and expert interviews. The interview is to validate the developed framework and model are involving ICT experts in the polytechnic. The preliminary survey on the network threats found that students are exposed to some of the issue of the network threat and demonstrated lack of awareness when it comes to network security. The network framework and threat management model of the IoT are developed to monitor the threats in the network. Efficient and robust monitoring systems such as the IoT network threat management model are expected to enhance the network security system capability in METrO Polytechnic Tasek Gelugor.

Interfacing the world with innovations has made a huge difference. Applications of the Internet of things (IoT) have been utilized in biomedical framework; however, the IoT has been larger than simply adding the network to the current items and administrations such that the items have moved toward becoming administrations themselves, and these have turned out to be progressively shrewd. Furthermore, telemedicine requires the fast, protected, enduring, and versatile arrangement of correspondence, so as to accomplish the point in which the specialists and patients can communicate continuously. In the future, IoT will drive another period of the medicine, and the hospitalization technique for the patients will likewise have a progressive change. Intelligent meeting represents a live discussion that utilizes proficient video conferencing frameworks for remote medicinal specialists and doctors as well as patients “up close and personal.” In this connection, nonintuitive counsel is a nonlive interview 32through which the specialists concentrate the review information and other data of the patients exchanged through the system. It consolidates answers for the utilization of telemedicine and would altogether be able to lessen the time and cost of transporting patients. Moreover, many specialists can break the point of confinement of the geographical degree, while sharing the case history and diagnostic. In general, enormous extending telemedicine applications can incredibly decrease the boundaries to patients accepting therapeutic consideration on the grounds that land segregation is never again a restorative unfavorable obstruction. This has incorporated segments, procedures, and security such as fighting against pharmaceutical problem and counterfeit items that identify with the restorative utilization of an IoT framework. By applying IoT, we can make smart chair that is helpful for blind people and work on information security, node mobility management in large-scale network, and many real-time problems such as kidnapping of newborn babies, and by applying global positioning system (GPS), we will be able to be aware about many diseases such as heart and brain tumors. The chapter discusses about the executed framework and how it functions with the assistance of test system and equipment stages. It answers a few issues related with IoT and shows how they work in the actualized framework in distinctive stages. The outcomes demonstrate the framework capacities easily on both test system condition and equipment stages, having the ability to be executed in an emergency clinic condition. Beginning from the medication observation and the execution to digitization of clinics and telemedicine care, we discussed each conceivable zone in which IoT innovation can upgrade the procedures.