Secure Transmission Research Articles

Secure physical layer transport strategies in mobile edge computing

Abstract In the past, mobile edge computing faced problems such as information leakage and eavesdropping during offloading of computing resources. To bolster the comprehensive security of the mobile edge computing system, a physical layer secure transmission strategy in mobile edge computing is proposed. First, a drone-mobile edge computing system is studied, which consists of M legitimate drones, N user devices, and K eavesdropping drones. Second, a multi-intelligence deep deterministic policy gradient algorithm is used to optimize the flight path, transmit power, and offloading ratio of the legitimate UAVs in combination with a prioritized empirical replay mechanism. Finally, under the premise of guaranteeing offloading, it realizes that the user’s confidential information is not stolen by eavesdroppers, safeguards the overall security of the system, and maximizes the efficiency of security computing. In this paper, the proposed algorithm enhances security transmission efficiency by 50% compared to other benchmark schemes. This demonstrates that the algorithm is effective.

Secure Transmission via Hybrid Active-Passive RIS Against Eavesdropping and Jamming

Secure Transmission via Hybrid Active-Passive RIS Against Eavesdropping and Jamming

Robust and secure data transmission using artificial intelligence techniques in Ad-Hoc networks

Robust and secure data transmission using artificial intelligence techniques in Ad-Hoc networks

Learning-Based Reliable and Secure Transmission for UAV-RIS-Assisted Communication Systems

Learning-Based Reliable and Secure Transmission for UAV-RIS-Assisted Communication Systems

Security and Privacy for Smart Transportation Management using Big Data Analytics

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

Optimization of multi-user fairness in RIS-enhanced UAV secure transmission systems

This paper proposes a reconfigurable intelligent surface (RIS)-enhanced unmanned aerial vehicle (UAV) secure communicating scheme with multiple mutually-untrusted ground users (GUs). To resist eavesdropping, a pre-defined artificial noise (AN) is added to the intended signal at UAV for scheduled GUs in each time slot. The GU scheduling schemes, UAV trajectories and transmit power allocations in different time slots are jointly designed to ensure communication security for all GUs. To boost secrecy rate of the system while ensuring secure fairness among GUs, we conduct a joint optimization process, involving optimizing UAV trajectories, RIS phase shifts, GU scheduling schemes, and UAV transmit power splitting factors, adhering to the constraints of UAV mobility, RIS phase shifts and other related constraints. To solve this non-convex optimization, we utilize the block coordinate descent (BCD) method for problem decomposition, coupled with an iterative algorithm to optimize the resulting sub-problems. To further solve the non-convex sub-problems, we employ the variable substitution to convexify the transmit power allocation, the semi-deterministic relaxation (SDR) to convexify the RIS passive beamforming and successive convex approximation (SCA) to convexify UAV mobility constraints. Simulation results show the convergence and effectiveness of the proposed scheme, compared to the benchmark schemes, the average worst-case secrecy rate increases by 32.33%, 60.38% and 80.09‘% respectively.

Design and performance analysis of manchester coder-based body channel communication using FPGA

In general, the Body Channel Communication (BCC) is used to transmit human physiological signals over vast distances. Also, the Error-correcting codes can detect and rectify faults in long-range data transmission. Data is encoded in a predetermined manner for secure transmission. In this paper, a Manchester encoding-based high throughput architecture is proposed for a body channel communication system with a low-power operation mode. Further, the Manchester encoder is designed for high throughput performance with a low complexity architecture, and an analog front-end processing circuit which includes two stage pre-amplifiers. Also, the transmission rate on the BCC transceiver side is boosted by the maximum Consecutive Identical Digit (CID), which is limited by Manchester codes. The Manchester code format is generated by encoding the sensed data. Additionally, a full gain buffer is used to reduce data loss and hardware overhead. Results demonstrate that the suggested Manchester encoder is implemented in 90 nano meter (nm) technology, and the 10 Megabits per second (Mbps) data rate is obtained with a configurable operating frequency ranging from 1 to 100 Megahertz (MHz).

Image encryption algorithm based on a memcapacitor‐based hyperchaotic system and DNA coding

AbstractIn today's digitally connected world, the protection of privacy and data security in open communication media (OCM) is paramount. As digital devices become more widespread and increase in global connectivity, the significance of image encryption has reached unprecedented levels. Traditional encryption techniques often fall short when applied to images due to their unique properties such as high correlation and large data quantities. To address these challenges, this paper presents a novel image encryption method that integrates principles from deoxyribonucleic acid (DNA) rules, the logistic map, and a hyperchaotic model with memcapacitors. The intricate and unpredictable nature of the memcapacitor‐based hyperchaotic system, coupled with deficiencies in current image encryption algorithms, serves as a catalyst for the development of this new algorithm. The proposed encryption strategy leverages the encoding properties of DNA and the complex dynamics of hyperchaotic systems to achieve enhanced image encryption. Through simulations and evaluations using reference images, the effectiveness of the method is demonstrated. It surpasses current techniques in terms of statistical robustness, resistance to brute‐force attacks, and protection against differential attacks. The research concludes that combining DNA principles, logistic maps, and memcapacitor‐based hyperchaotic systems significantly improves image encryption. This approach enhances data security for image transmission in open communication channels, addressing the limitations of traditional encryption techniques. As digital connectivity expands globally, the demand for robust image encryption is expected to increase, making this research pivotal in establishing standards for future advancements in image encryption algorithms.

Correction to: Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Correction to: Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

[formula omitted] filtering for uncertain discrete-time singular switched positive systems with time delay and output quantization

This paper focuses on the design problem of an l1 filter for singular switched positive systems with time delay and uncertainty. In order to enhance resource efficiency and information transmission security, the quantization mechanism is introduced when designing the filter. Furthermore, sufficient conditions for causality, regularity, positivity, and exponential stability of the filtering error system are provided under the constraint of mode-dependent minimum dwell time (MDMDT). These conditions are obtained by selecting an appropriate co-positive Lyapunov function and are presented in the form of linear programming (LP). Subsequently, the disturbance attenuation performance is further analyzed, and a design methodology for the corresponding filter is outlined. Finally, a numerical example is presented to verify the validity of the theoretical results.

A blockchain‐based resilient and secure framework for events monitoring and control in distributed renewable energy systems

AbstractThe rapid and green energy transition is essential to deal with the fast‐growing energy needs in both public and industrial sectors. This has paved the way to integrate distributed renewable energy resources () such as solar, hydro, wind, and geothermal into the power grid (). Wind and solar are free, zero‐carbon emission, and everlasting power sources that contribute 5% and 7% of global electricity generation, respectively. Therefore, the fast, secure, and reliable integration of these green is critical to achieve the instant energy demands. Smart grid due to inherited characteristics such as intelligent sensing, computing, and communication technologies can effectively integrate the . However, the existing smart grid communication architecture faces various cyberattacks, resulting in poor integration, monitoring, and control of . In this respect, blockchain technology can provide fast, secure, and efficient end‐to‐end communication between in the smart grid. In this study, the authors propose a blockchain‐based resilient and secure scheme called for wireless sensor networks ‐based events monitoring and control in . Experimental studies and performance analyses are carried out to predict the efficiency of the proposed scheme by considering numerous standard metrics. The extensive numerical results demonstrated that the proposed scheme is significant in terms of secure, resilient, and reliable information transmission for in .

Visually secure traffic image encryption scheme using new two-dimensional Sigmoid-type memristive chaotic map and Laguerre transform embedding

In intelligent transportation system, unprotected bare data transmission faces serious security threats and challenges. To this end, this paper proposes a visually secure traffic image encryption scheme that combines a newly designed two-dimensional Sigmoid-type memristive chaotic map (2D-SMCM) with two-dimensional compressive sensing (2D-CS) and Laguerre transform (LT) embedding to provide services for secure transmission of private images. Specifically, first, the 2D-SMCM is used to generate pseudo-random sequences for subsequent compression, encryption and hiding operations. Second, the 2D-CS is utilized to compress the plain image to reduce the amount of data transmission. Then, encryption is completed by modifying the data values and their positions through index permutation and bidirectional diffusion. Finally, the encrypted data is embedded in the LT-processed public carrier medium for covert transmission. Experiments and performance analysis illustrate that the proposed scheme has good security, imperceptibility and reconstruction performance, with the average PSNRs of the cipher images and decrypted secret images up to 45.90 dB and 34.85 dB, respectively, using 500 grayscale images from the database BOWS2.

Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive data related to the patients and their disclosure has a negative impact on their right to privacy as well as legal ramifications for hospitals. In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images. Initially, medical images are pre-processed by adding the speckle noise using discrete ripplet transform before encryption and are removed after decryption for more security. In the Deep-KEDI model, the zigzag generative adversarial network (ZZ-GAN) is used as the learning network to generate the secret key. The proposed ZZ-GAN is used for secure encryption by generating three different zigzag patterns (vertical, horizontal, diagonal) of encrypted images with its key. The zigzag cipher uses an XOR operation in both encryption and decryption using the proposed ZZ-GAN. Encrypting the original image requires a secret key generated during encryption. After identification, the encrypted image is decrypted using the generated key to reverse the encryption process. Finally, speckle noise is removed from the encrypted image in order to reconstruct the original image. According to the experiments, the Deep-KEDI model generates secret keys with an information entropy of 7.45 that is particularly suitable for securing medical images.

A hybrid encryption approach for efficient and secure data transmission in IoT devices

Security is a crucial concern in the Internet of Things (IoT) ecosystem. Due to IoT devices' constrained processing and storage resources, providing reliable security solutions is challenging. Encryption is one of the most commonly used techniques to secure user data against unauthorized access. Therefore, it is essential to develop encryption solutions that have minimal impact on the performance of IoT devices. This study introduces a hybrid encryption approach that combines symmetric blowfish encryption with asymmetric elliptic curves. Blowfish encryption is used to encrypt large volumes of data, which could otherwise affect the execution time.In contrast, elliptic curve cryptography is utilized to ensure the security of the private key, which has a small size and does not increase the execution time significantly. The suggested approach provides advantages of both asymmetric and symmetric encryption methods, leading to an improvement in throughput and a reduction in execution time. The proposed approach was evaluated, yielding promising results in comparison to other cryptographic algorithms. The results show the optimization of more than 15% in the execution time and the efficiency increase by the proposed solution. This improvement represents security with the least impact on processing resources.

Wireless sensor network security management based on virtual resource scheduling model

ABSTRACT For lofting the performance of wireless sensor networks, a lightweight gradient elevator algorithm model is improved using a virtual resource scheduling model and sparrow search algorithm particle swarm optimization algorithm. A new network intrusion detection method (SSAPSO-LightGBM) has been proposed to improve detection accuracy and efficiency. Then, the data security transmission level and user historical priority resource scheduling model (SDT-HRU-VRSM) are used to schedule resources to ensure network resource revenue. The results show that the SSAPSO LightGBM algorithm has a detection accuracy of 99.61% for Normal, 98.42% for R2L, 97.03% for U2R, 96.01% for Probe, and 98.41% for DoS. The SDT-HRU-VRSM model not only meets the needs of secure data transmission, but also takes into account the stickiness of increasing high-quality users. And according to security requirements, the number of virtual resource scheduling is increased, reducing the number of service network slicing instances and improving security. This model can effectively increase the attack cost of malicious users and increase network revenue, which is crucial for ensuring the security and efficient operation of wireless sensor networks.

Secure transmission cryptographic approach for remote-sensing image based on discrete memristor-coupled Rulkov neuron map and TIMG

Secure transmission cryptographic approach for remote-sensing image based on discrete memristor-coupled Rulkov neuron map and TIMG

Age of Information-Inspired Data Collection and Secure Upload Assisted by the Unmanned Aerial Vehicle and Reconfigurable Intelligent Surface in Maritime Wireless Sensor Networks

This paper proposes an age of information (AoI)-inspired secure transmissions strategy for secure transmission from the maritime wireless sensor network to the onshore base station (BS) with the assistance of the unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), in which eavesdroppers exist near the BS. In the proposed scheme, the secure transmission process is divided into the data collection period and the data upload period according to the time sequence to minimize the age of information (AoI) for the privacy information. In the data collection period, we design two scheduling schemes by selecting the sensor with the smallest current AoI or the largest difference in the adjacent AoI. In addition, we propose two heuristic algorithms by adopting the particle swarm optimization algorithm (PSO) and genetic algorithm (GA) to solve the above two problems. In the data uploading period, the uploading time minimization problem is converted to the secrecy rate maximization problem. We design an iterative optimization algorithm with auxiliary variables that are introduced to optimize the reflection coefficient of the RIS. Simulation results show that the proposed scheme can reduce the average AoI by about 10 s compared to the current methods.

Secure Encrypted Transmission of Network Data in Cloud Computing Technology Environment

In order to solve the problem of communication data theft in conventional network communication data transmission methods and ensure the security of network communication data transmission, it is necessary to design new network communication data security transmission methods based on cloud computing technology, formulate network communication data security transmission agreements, construct a network communication data security transmission model based on cloud computing technology, and design a network communication data security transmission scheme, implement secure transmission of network communication data. The experimental results show that after using the designed network communication data secure transmission method, the amount of stolen communication data is less than that of conventional methods. This proves that the designed network communication data secure transmission method has high transmission security, good transmission effect, and reliability, and can be used as a reference for subsequent network communication data encryption transmission.

A Novel Approach for the Enhancement of Security through Defining the Spatial Secrecy Outage Probability in the Industrial Internet of Things

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems.