6G secure quantum communication: a success probability prediction model

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The emergence of 6G networks initiates significant transformations in the communication technology landscape. Yet, the melding of quantum computing (QC) with 6G networks although promising an array of benefits, particularly in secure communication. Adapting QC into 6G requires a rigorous focus on numerous critical variables. This study aims to identify key variables in secure quantum communication (SQC) in 6G and develop a model for predicting the success probability of 6G-SQC projects. We identified key 6G-SQC variables from existing literature to achieve these objectives and collected training data by conducting a questionnaire survey. We then analyzed these variables using an optimization model, i.e., Genetic Algorithm (GA), with two different prediction methods the Naïve Bayes Classifier (NBC) and Logistic Regression (LR). The results of success probability prediction models indicate that as the 6G-SQC matures, project success probability significantly increases, and costs are notably reduced. Furthermore, the best fitness rankings for each 6G-SQC project variable determined using NBC and LR indicated a strong positive correlation (rs = 0.895). The t-test results (t = 0.752, p = 0.502 > 0.05) show no significant differences between the rankings calculated using both prediction models (NBC and LR). The results reveal that the developed success probability prediction model, based on 15 identified 6G-SQC project variables, highlights the areas where practitioners need to focus more to facilitate the cost-effective and successful implementation of 6G-SQC projects.Peer reviewe

Optimization of secure wireless communications for IoT networks in the presence of eavesdroppers

The problem motivates this paper is that securing the critical data of 5G based wireless IoT network is of significant importance. Wireless 5G IoT systems consist of a large number of devices (low-cost legitimate users), which are of low complexity and under strict energy constraints. Physical layer security (PLS) schemes, along with energy harvesting, have emerged as a potential candidate that provides an effective solution to address this issue. During the data collection process of IoT, PHY security techniques can exploit the characteristics of the wireless channel to ensure secure communication. This paper focuses on optimizing the secrecy rate for simultaneous wireless information and power transfer (SWIPT) IoT system, considering that the malicious eavesdroppers can intercept the data. In particular, the main aim is to optimize the secrecy rate of the system under signal to interference noise ratio (SINR), energy harvesting (EH), and total transmits power constraints. We model our design as an optimization problem that advocates the use of additional noise to ensure secure communication and guarantees efficient wireless energy transfer. The primary problem is non-convex due to complex objective functions in terms of transmit beamforming matrix and power splitting ratios. We have considered both the perfect channel state information (CSI) and the imperfect CSI scenarios. To circumvent the non-convexity of the primary problem in perfect CSI case, we proposed a solution based on the concave-convex procedure (CCCP) iterative algorithm, which results in a maximum local solution for the secrecy rate. In the imperfect CSI scenario, we facilitate the use of S-procedure and present a solution based on the iterative successive convex approximation (SCA) approach. Simulation results present the validations of the proposed algorithms. The results provide an insightful view that the proposed iterative method based on the CCCP algorithm achieves higher secrecy rates and lower computational complexity in comparison to the other algorithms

Achievable secrecy enchancement through joint encryption and privacy amplification

In this dissertation we try to achieve secrecy enhancement in communications by resorting to both cryptographic and information theoretic secrecy tools and metrics. Our objective is to unify tools and measures from cryptography community with techniques and metrics from information theory community that are utilized to provide privacy and confidentiality in communication systems. For this purpose we adopt encryption techniques accompanied with privacy amplification tools in order to achieve secrecy goals that are determined based on information theoretic and cryptographic metrics. Every secrecy scheme relies on a certain advantage for legitimate users over adversaries viewed as an asymmetry in the system to deliver the required security for data transmission. In all of the proposed schemes in this dissertation, we resort to either inherently existing asymmetry in the system or proactively created advantage for legitimate users over a passive eavesdropper to further enhance secrecy of the communications. This advantage is manipulated by means of privacy amplification and encryption tools to achieve secrecy goals for the system evaluated based on information theoretic and cryptographic metrics. In our first work discussed in Chapter 2 and the third work explained in Chapter 4, we rely on a proactively established advantage for legitimate users based on eavesdropper’s lack of knowledge about a shared source of data. Unlike these works that assume an errorfree physical channel, in the second work discussed in Chapter 3 correlated erasure wiretap channel model is considered. This work relies on a passive and internally existing advantage for legitimate users that is built upon statistical and partial independence of eavesdropper’s channel errors from the errors in the main channel. We arrive at this secrecy advantage for legitimate users by exploitation of an authenticated but insecure feedback channel. From the perspective of the utilized tools, the first work discussed in Chapter 2 considers a specific scenario where secrecy enhancement of a particular block cipher called Data Encryption standard (DES) operating in cipher feedback mode (CFB) is studied. This secrecy enhancement is achieved by means of deliberate noise injection and wiretap channel encoding as a technique for privacy amplification against a resource constrained eavesdropper. Compared to the first work, the third work considers a more general framework in terms of both metrics and secrecy tools. This work studies secrecy enhancement of a general cipher based on universal hashing as a privacy amplification technique against an unbounded adversary. In this work, we have achieved the goal of exponential secrecy where information leakage to adversary, that is assessed in terms of mutual information as an information theoretic measure and Eve’s distinguishability as a cryptographic metric, decays at an exponential rate. In the second work generally encrypted data frames are transmitted through Automatic Repeat reQuest (ARQ) protocol to generate a common random source between legitimate users that later on is transformed into information theoretically secure keys for encryption by means of privacy amplification based on universal hashing. Towards the end, future works as an extension of the accomplished research in this dissertation are outlined. Proofs of major theorems and lemmas are presented in the Appendix

Analysis and Transformation of Configurable Systems

Static analysis tools and transformation engines for source code belong to the standard equipment of a software developer. Their use simplifies a developer's everyday work of maintaining and evolving software systems significantly and, hence, accounts for much of a developer's programming efficiency and programming productivity. This is also beneficial from a financial point of view, as programming errors are early detected and avoided in the the development process, thus the use of static analysis tools reduces the overall software-development costs considerably. In practice, software systems are often developed as configurable systems to account for different requirements of application scenarios and use cases. To implement configurable systems, developers often use compile-time implementation techniques, such as preprocessors, by using #ifdef directives. Configuration options control the inclusion and exclusion of #ifdef-annotated source code and their selection/deselection serve as an input for generating tailor-made system variants on demand. Existing configurable systems, such as the linux kernel, often provide thousands of configuration options, forming a huge configuration space with billions of system variants. Unfortunately, existing tool support cannot handle the myriads of system variants that can typically be derived from a configurable system. Analysis and transformation tools are not prepared for variability in source code, and, hence, they may process it incorrectly with the result of an incomplete and often broken tool support. We challenge the way configurable systems are analyzed and transformed by introducing variability-aware static analysis tools and a variability-aware transformation engine for configurable systems' development. The main idea of such tool support is to exploit commonalities between system variants, reducing the effort of analyzing and transforming a configurable system. In particular, we develop novel analysis approaches for analyzing the myriads of system variants and compare them to state-of-the-art analysis approaches (namely sampling). The comparison shows that variability-aware analysis is complete (with respect to covering the whole configuration space), efficient (it outperforms some of the sampling heuristics), and scales even to large software systems. We demonstrate that variability-aware analysis is even practical when using it with non-trivial case studies, such as the linux kernel. On top of variability-aware analysis, we develop a transformation engine for C, which respects variability induced by the preprocessor. The engine provides three common refactorings (rename identifier, extract function, and inline function) and overcomes shortcomings (completeness, use of heuristics, and scalability issues) of existing engines, while still being semantics-preserving with respect to all variants and being fast, providing an instantaneous user experience. To validate semantics preservation, we extend a standard testing approach for refactoring engines with variability and show in real-world case studies the effectiveness and scalability of our engine. In the end, our analysis and transformation techniques show that configurable systems can efficiently be analyzed and transformed (even for large-scale systems), providing the same guarantees for configurable systems as for standard systems in terms of detecting and avoiding programming errors

Estimation and detection with chaotic systems

Includes bibliographical references (p. 209-214).Supported by the U.S. Air Force Office of Scientific Research under the Augmentation Awards for Science and Engineering Research Training Program Grant. F49620-92-J-0255 Supported by the U.S. Air Force Office of Scientific Research. AFOSR-91-0034-C Supported by the U.S. Navy Office of Naval Research. N00014-93-1-0686 Supported by Lockheed Sanders, Inc. under a U.S. Navy Office of Naval Research contract. N00014-91-C-0125Michael D. Richard

Algorithms for secure communication

The design of algorithms for sending confidential messages (i.e. messages that no one can read, except the intended receiver) goes back to the beginning of our civilization. However, before the widespread of modern computers, cryptography was practiced by few people: soldiers, or diplomats, or scientists fascinated by the problem of confidential communication. Cryptography algorithms designed in the past were ingenious transformations but were lacking a sound mathematical basis. Recently, the development of computers and of the Internet has opened up new applications of cryptography in business and society. To answer these needs, new algorithms have been developed that use sound mathematical techniques and have produced surprising results, which have opened up impressive possibilities that were considered unrealistic before.We will see examples of algorithms that use modular arithmetic (in which operations are performed modulo an integer) that are based on using functions that are easy to compute but difficult to invert. © Springer-Verlag Berlin Heidelberg 2013. All rights reserved

Exploring Cryptographic Techniques for Data security in Resource-Constrained Wireless Sensor Networks:Performance Evaluation and Considerations

Wireless sensor networks (WSNs) play a crucial role in environmental monitoring and data collection. However, ensuring data security in WSNs poses challenges due to the vulnerabilities of wireless communication channels. In this paper, we address this concern by exploring the application of cryptographic techniques to enhance data security in WSNs. Considering the limited sensor power, computing power, and storage resources, we propose a novel approach that evaluates the suitability of symmetric and asymmetric cryptographic algorithms in WSNs. Through performance comparisons based on computation power and storage capacity requirements, we identify key insights for selecting appropriate encryption algorithms in WSNs. Our findings emphasize the importance of considering the specific requirements and constraints of WSN applications, highlighting the efficiency of symmetric key-based encryption algorithms in resource-constrained environments and the stronger security and key distribution mechanisms provided by ECC-based asymmetric encryption algorithms for secure communication among multiple nodes. This research contributes to the existing knowledge by offering an effective solution to enhance data security in WSNs while considering computational and storage limitation