Search Results (87)

Modern security models for public-key cryptography, such as one-way encryption under chosen plaintext attack (OWE-CPA) or indistinguishability under chosen plaintext attack (IND-CPA), rely on reductions between the security of cryptographic schemes and well-studied hard problems, such as integer factorization, discrete logarithm, quadratic residuosity, or learning with errors. The reduction can go from the hard problem to the security property under study, or vice versa, or in both directions (in which case we say there is an equivalence). Equivalences fundamentally tie the security property to the hard problem, thus offering multiple benefits. But obtaining an equivalence between a security property and a computational hard problem can be challenging, as is the case with the equivalence between the OWE-CPA security of the textbook RSA cryptosystem and the integer factorization problem. In this paper, we introduce a new computational problem, namely, distinguishing the Jacobi symbols of the solutions of a quadratic congruence modulo an RSA modulus (JSP(QC)). We show that this problem is at least as hard as the quadratic residuosity problem. Then, we show that the IND-CPA security of two public-key encryption schemes due to Cocks is equivalent to JSP(QC). We then specialize JSP(QC) to roots of quadratic residues and establish several computational indistinguishability results. Full article

This study proposes the Huffman Tree and Binary Conversion (HTB) which is a preprocessing algorithm to transform the Huffman tree into binary representation before the encryption process. In fact, HTB can improve the structural readiness of plaintext by combining the Huffman code with a deterministic binary representation of the Huffman tree. In addition, binary representation of the Huffman tree and the compressed information will be encrypted by standard cryptographic algorithms. Six datasets, divided into two groups (short and long texts), were chosen to evaluate compression behavior and the processing cost. Moreover, AES and RSA are chosen to combine with the proposed method to analyze the encryption and decryption cycles. The experimental results show that HTB introduces a small linear-time overhead. That means, it is slightly slower than applying only the Huffman code. Across these datasets, HTB maintained a consistently low processing cost. The processing time is below one millisecond in both encoding and decoding processes. However, for long texts, the structural conversion cost becomes amortized across larger encoded messages, and the reduction in plaintext size leads to fewer encryption blocks for both AES and RSA. The reduced plaintext size lowers the number of AES encryption blocks by approximately 30–45% and decreases the number of encryption and decryption rounds in RSA. The encrypted binary representation of the Huffman tree also decreased structural ambiguity and reduced the potential exposure of frequency-related metadata. Although HTB does not replace cryptographic security, it enhances the structural consistency of compression. Therefore, the proposed method demonstrates scalability, predictable overhead, and improved suitability for cryptographic workflows. Full article

The Internet of Medical Things (IoMT) improves healthcare delivery through many medical applications. Because of medical data sensitivity and limited resources of wearable technology, privacy and security are significant challenges. Traditional encryption does not provide secure computation on encrypted data, and many blockchain-based IoMT solutions partially rely on centralized structures. IoMT with dynamic encryption is an innovative privacy-preserving system that combines sensitivity-based classification and advanced encryption to address these issues. The study proposes privacy-preserving IoMT framework that dynamically adapts its cryptographic strategy based on data sensitivity. The proposed approach uses a hybrid SDAIPA (SDAIA-HIPAA) classification model that integrates Saudi Data and Artificial Intelligence Authority (SDAIA) and Health Insurance Portability and Accountability Act (HIPAA) guidelines. This classification directly governs the selection of encryption mechanisms, where Advanced Encryption Standard (AES) is used for low-sensitivity data, and Fully Homomorphic Encryption (FHE) is used for high-sensitivity data. The Whale Optimization Algorithm (WOA) is used to maximize cryptographic entropy of FHE keys and improves security against attacks, resulting in an Optimized FHE that is conditionally used based on SDAIPA outputs. This proposed approach provides a novel scheme to dynamically align cryptographic intensity with data risk and avoids the overhead of uniform FHE use while ensuring strong privacy for critical records. Two datasets are used to assess the proposed approach with up to 806 samples. The results show that the hybrid OHE-WOA outperforms in the percentage of sensitivity of privacy index with dataset 1 by 78.3% and 12.5% and with dataset 2 by 89% and 19.7% compared to AES and RSA, respectively, which ensures its superior ability to preserve privacy. Full article

In the booming digital economy, data circulation—particularly for massive multimodal data generated by IoT sensor networks—faces critical challenges: ambiguous ownership and broken cross-domain traceability. Traditional property rights theory, ill-suited to data’s non-rivalrous nature, leads to ownership fuzziness after multi-source fusion and traceability gaps in cross-organizational flows, hindering marketization. This study aims to establish native ownership confirmation capabilities in trusted IoT-driven data ecosystems. The approach involves a dual-factor system: the collaborative extraction of text (from sensor-generated inspection reports), numerical (from industrial sensor measurements), visual (from 3D scanning sensors), and spatio-temporal features (from GPS and IoT device logs) generates unique SHA-256 fingerprints (first factor), while RSA/ECDSA private key signatures (linked to sensor node identities) bind ownership (second factor). An intermediate state integrates these with metadata, supported by blockchain (consortium chain + IPFS) and cross-domain protocols optimized for IoT environments to ensure full-link traceability. This scheme, tailored to the characteristics of IoT sensor networks, breaks traditional ownership confirmation bottlenecks in multi-source fusion, demonstrating strong performance in ownership recognition, anti-tampering robustness, cross-domain traceability and encryption performance. It offers technical and theoretical support for standardized data components and the marketization of data elements within IoT ecosystems. Full article

Industrial control systems (ICS) are increasingly vulnerable to evolving cyber threats due to the convergence of operational and information technologies. This research presents a robust cybersecurity framework that integrates machine learning-based anomaly detection with advanced cryptographic techniques to protect ICS communication networks. Using the ICS-Flow dataset, we evaluate several ensemble models, with XGBoost achieving 99.92% accuracy in binary classification and Decision Tree attaining 99.81% accuracy in multi-class classification. Additionally, we implement an LSTM autoencoder for temporal anomaly detection and employ the ADWIN technique for real-time drift detection. To ensure data security, we apply AES-CBC with HMAC and AES-GCM with RSA encryption, which demonstrates resilience against brute-force, tampering, and cryptanalytic attacks. Security assessments, including entropy analysis and adversarial evaluations (IND-CPA and IND-CCA), confirm the robustness of the encryption schemes against passive and active threats. A hardware implementation on a PYNQ Zynq board shows the feasibility of real-time deployment, with a runtime of 0.11 s. The results demonstrate that the proposed framework enhances ICS security by combining AI-driven anomaly detection with RSA-based cryptography, offering a viable solution for protecting ICS networks from emerging cyber threats. Full article

Battery Energy Storage Systems (BESSs) are critical to smart grid functioning but are exposed to mounting cybersecurity threats with their integration into IoT and cloud-based control systems. Current solutions tend to be deficient in proper multi-class attack classification, secure encryption, and full integrity and power quality features. This paper proposes a comprehensive framework that integrates machine learning for attack detection, cryptographic security, data validation, and power quality control. With the BESS-Set dataset for binary classification, Random Forest achieves more than 98.50% accuracy, while LightGBM attains more than 97.60% accuracy for multi-class classification on the resampled data. Principal Component Analysis and feature importance show vital indicators such as State of Charge and battery power. Secure communication is implemented using Elliptic Curve Cryptography and a hybrid Blowfish–RSA encryption method. Data integrity is ensured through applying anomaly detection using Z-scores and redundancy testing, and IEEE 519-2022 power quality compliance is ensured by adaptive filtering and harmonic analysis. Real-time feasibility is demonstrated through hardware implementation on a PYNQ board, thus making this framework a stable and feasible option for BESS security in smart grids. Full article

Ensuring data privacy and security in sensitive domains such as healthcare remains a critical challenge. Homomorphic Encryption (HE) offers a promising approach by enabling computations directly on encrypted data, but the diversity of available schemes requires careful evaluation before practical adoption. This work conducts a comparative study of six representative HE schemes: BGV, TFHE, Paillier, RSA without padding, BFV, and CKKS. It is adopted a five-step strategy, encompassing preprocessing, cryptographic setup, encryption, homomorphic execution, and decryption, applied to a healthcare dataset. Overall, the comparative analysis underscores that no single scheme is universally optimal. The choice of an HE scheme must be guided by the nature of the required operations, acceptable precision levels, and computational constraints of the target healthcare scenario. Full article

This study surveys some cryptographic algorithms in a detailed manner; it mainly focuses on symmetric key cryptography and asymmetric key cryptography with hash functions following them. Regarding the importance of cryptography for securing communications and data integrity in the digital era, we show—using practical examples with Python 3.10 and Crypto 2 tool—how a few implementations of such encryption techniques work. To clarify this further, Caesar Cipher represents a very simple varying key, and each round of stream ciphers or block ciphers exhibits highly advanced symmetric techniques. Then, we discuss asymmetric cryptography using RSA encryption with public–private key pairs for a secure communication. Furthermore, research has been conducted into the hash functions SHA-1 and SHA-2, which form unique digital fingerprints of the information provided. This approach allows us to highlight all the positive and negative aspects of the above tools and to identify the comparative characteristics of their degree of security. This fact is highly important in determining the applicability of the security tools described above, depending on the conditions of work and threats. Full article

Developing a new drug costs approximately one to three billion dollars and takes around ten years; however, this process has only a ten percent success rate. To address this issue, new technologies that combine artificial intelligence (AI) and quantum computing can be leveraged in the pharmaceutical industry. The RSA cryptographic algorithm, developed by Rivest, Shamir, and Adleman in 1977, is one of the most widely used public-key encryption schemes in modern digital security. Its security foundation lies in the computational difficulty of factoring the product of two large prime numbers, a problem considered intractable for classical computers when the key size is sufficiently large (e.g., 2048 bits or more). A future application of using a detailed structural model of a protein is that digital drug design can be used to predict potential drug candidates, thereby reducing or eliminating the need for time-consuming laboratory and animal testing. Knowing the molecular structure of a possible candidate drug can provide insights into how drugs interact with targets at an atomic level, at significantly lower expenditures, and with maximum effectiveness. AI and quantum computers can rapidly screen out potential new drug candidates, determine the toxicity level of a known drug, and eliminate drugs with high toxicity at the beginning of the drug development phase, thereby avoiding expensive laboratory and animal testing. The Food and Drug Administration (FDA) and other regulatory bodies are increasingly supporting the use of in silico to in vitro/in vivo validation methods and assessments of drug safety and efficacy. Full article

A secured communications system is a structure or infrastructure that is intended to ensure the confidentiality, integrity, and authenticity of data being exchanged between entities. Such systems use various security technologies to guarantee that communications are not tampered with, read, or accessed by unauthorized parties. The intractability of factoring huge composite numbers is a prerequisite for RSA’s security. With big enough key sizes, it is still computationally infeasible for attackers to defeat RSA encryption with today’s technology. Efficient domination is an idea based on graph theory, specifically in the investigation of domination in graphs. Although it has many applications in problems in computation, it is only for cryptography in contexts involving efficient algorithms, combinatorial structures, and optimization for security that it finds uses. This idea provides minimal redundancy in domination, similar to the optimization of resources in a cryptographic scenario. In this work, we concentrate on enhancing the complexity of secure systems using a mathematical model that is based on fuzzy graph networks. The suggested model combines efficient domination, the RSA algorithm, and triangular fuzzy membership functions. By integrating these optimized parameters, we construct a very secure mathematical fuzzy graph network that can efficiently protect secret information. Full article

This study investigates the integration of smart card readers into vehicle ignition systems as a multifaceted solution to enhance security, regulatory compliance, and road safety. By implementing real-time driver verification, encryption protocols (AES-256, RSA), and multifactor authentication, the system significantly reduces unauthorized vehicle use and improves accident prevention. A critical advancement of this research is the incorporation of automated drug and impairment detection to prevent driving under the influence of substances, including illicit drugs and prescription medications. Risk models estimate that drug-related accidents could be reduced by 7.65% through the integration of these technologies into vehicle ignition systems, assuming high compliance rates. The study evaluates drug applications leveraging the same sensor-based monitoring technologies as used for impairment detection. These systems can facilitate the real-time tracking of medication intake and physiological responses, offering new possibilities for safety applications in medical transportation and assisted driving technologies. High-performance polymers such as polyetheretherketone (PEEK) enhance the durability and thermal stability of smart card readers, while blockchain-based verification strengthens data security and regulatory compliance. Despite challenges related to cost (USD 100–300 per unit) and adherence to ISO standards, these innovations position smart card-based ignition systems as a comprehensive, technology-driven approach to vehicle security, impairment prevention, and medical monitoring. Full article

The security of a network is based on the foundation of confidentiality, integrity, and availability, often referred to as the CIA triad. The privacy of data over a network, maintained by confidentiality, has long been one of the major issues in network settings. With the decoupling of the data plane and control plane in the software-defined networking (SDN) environment, this challenge is significantly amplified. This paper aims to address the challenges of confidentiality in SDN by introducing a genetic algorithm-based hybrid encryption network policy to secure messages across the network. The proposed approach achieved an average entropy of 0.989, revealing a significant improvement in the strength of the encryption with the hybrid mechanism. However, the method exhibited processing overhead, significantly increasing the transmission time for encrypted messages compared to unencrypted transmission. Compared to standalone AES, DES, and RSA, this approach shows better encryption randomness, but a trade-off between security and network performance is evident in the absence of load-balancing techniques. Full article

Multilevel security (MLS) is particularly intended to secure information against unauthorized access. An MLS security DBMS allows users with different security levels to access and share a database. For this purpose, the study creates a model that includes a restricted access authentication prototype with multilevel security in a database management system (MLS/DBMS). Accordingly, the model has been designed to emphasize the highest level of authorized security. The system ensures that users can only access information that they are permitted to view, fully adhering to the newly established MLS framework. In addition, the model also integrates cryptographic algorithms, such as RSA and AES, to enhance its functionality and demonstrate the scalability and security of the model. These criteria are defined based on the perspective of the database provided to users, determined by their respective authorization levels. An informal security framework for a multilevel secure DBMS is defined. It includes a classification strategy and explains the implementation of operations like insertion and deletion, addressing the complexity of models with novel methods. The metric evaluation of this model assesses the performance of the authentication process and how operations are implemented across three authentication group types. It also calculates the key generation time and encryption types in cryptographic algorithms. The results confirm that the RSA model requires less time for evaluation while maintaining multilevel security. Furthermore, the type 2 authentication group is more complex and requires more memory and time for generation. Based on the classification, the results highlight notable differences, which designers should consider when selecting authentication methods. Lastly, the study presents various conclusions, explores possible future directions, and discusses its limitations. Full article

This paper presents a blockchain-enabled Multi-Agent System (MAS) for real-time power management in Virtual Prosumer (VP) Networks, integrating Plug-in Electric Vehicles (PEVs) and Renewable Energy Sources (RESs). The proposed framework addresses critical challenges related to scalability, security, and operational efficiency by developing a hierarchical MAS architecture that optimizes the scheduling and coordination of geographically distributed PEVs and RESs. Unlike conventional business management systems, this study integrates a blockchain-based security mechanism within the MAS framework, leveraging Proof of Authority (PoA) consensus to enhance transaction security while addressing scalability and energy consumption concerns. The system further employs advanced Particle Swarm Optimization (PSO) to dynamically compute optimal power set-points, enabling adaptive and efficient energy distribution. Additionally, hierarchical aggregation of transactions at lower MAS layers enhances computational efficiency and system resilience under high-traffic and partial network failure conditions. The proposed framework is validated through large-scale simulations spanning four major cities in Greece, demonstrating its scalability, reliability, and efficiency under diverse operational scenarios. Results confirm that the system effectively balances energy supply and demand while maintaining secure and transparent transactions. Despite these advancements, practical deployment challenges remain, including synchronization delays in geographically distributed agents, legacy system integration, and blockchain energy consumption. Future research directions include investigating more advanced consensus mechanisms (e.g., Proof of Task), machine learning-driven predictive optimization, real-world large-scale testing, and federated learning models for decentralized decision-making. The proposed framework offers a scalable, secure, and efficient solution for decentralized real-time energy management in Virtual Prosumer Networks. Full article

Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), enables immersive experiences across various fields, including entertainment, healthcare, and education. However, its data-intensive and interactive nature introduces significant cybersecurity and privacy challenges. This paper presents a detailed adversary model to identify threat actors and attack vectors in XR environments. We analyze key risks, including identity theft and behavioral data leakage, which can lead to profiling, manipulation, or invasive targeted advertising. To mitigate these risks, we explore technical solutions such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), and Elliptic Curve Cryptography (ECC) for secure data transmission, multi-factor and biometric authentication, data anonymization techniques, and AI-driven anomaly detection for real-time threat monitoring. A comparative benchmark evaluates these solutions’ practicality, strengths, and limitations in XR applications. The findings emphasize the need for a holistic approach, combining robust technical measures with privacy-centric policies, to secure XR ecosystems and ensure user trust. Full article