The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the $EC$ parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks.

The cognitive radio (CR) technique has revealed a novel way of utilizing the precious radio spectrum via allowing unlicensed users to opportunistically access unutilized licensed bands. Using such a technique enables agile and flexible access to the radio spectrum and can resolve the spectrum-scarcity problem and maximize spectrum efficiency. However, two major impediments have been limiting the widespread adoption of cognitive radio technology. The software-defined radio technology, which is the enabling technology for the CR technique, is power-hungry and this raises a major concern for battery-constrained devices such as smart phones and laptops. Secondly, the opportunistic and open nature of the CR can lead to major security concerns about the data being sent and how safe it is. In this paper, we introduce an energy-and-security-aware CR-based communication approach that alleviates the power consumption of the CR technique and enhances its security measures according to the confidentiality level of the data being sent. Furthermore, the proposed approach takes into account user-related factors, such as the user’s battery level and user’s data type, and network-related factors, such as the number of unutilized bands and vulnerability level and then models the research question as a constrained optimization problem. Considering the time complexity of the optimum solution, we also propose a heuristic solution. We examine the proposed solution against existing solutions, and our obtained results show that the proposed approach can save energy consumption up to 18%, increase user throughput up to 20%, and achieve better spectrum utilization, up to 98%. Our proposed admission approach has the potential to open a new research direction towards safer and greener cognitive radio techniques.

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of $2^{2200}$. The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application.

The Internet of Underwater Things (IoUT) has emerged as a vital technological domain, significantly advancing underwater exploration and communication capabilities. At the core of this paradigm is Underwater Acoustic Communication (UAC), which acts as the primary medium for data transmission in underwater environments, including environmental monitoring, security and surveillance, and underwater exploration. Despite its significance, UAC systems face inherent challenges such as limited bandwidth, high signal attenuation, and long propagation delays. These limitations become particularly significant in mission-critical maritime operations, such as warning systems, underwater navigation and control, and diver safety or emergency communications, where timely and reliable data transmission is crucial. Existing resource allocation solutions often fail to adequately optimize limited UAC bandwidth to address the distinct needs of mission-critical applications, making it difficult to ensure reliability and low delays for critical tasks without compromising non-critical performance. This paper introduces a novel resource management scheme to support mission-critical applications in UAC systems, optimizing the acoustic spectrum using Resource Blocks (RBs) with frequency and time division multiplexing. This approach prioritizes mission-critical applications to ensure high reliability and low latency, while also accommodating non-critical applications. We formulate a resource allocation optimization problem based on application type and present a solution. We also introduce a Meta-Heuristic approach designed to efficiently achieve near-optimal solutions while minimizing computational complexity. Simulation results obtained with the DESERT underwater network simulator show that, compared to existing resource management schemes, our approach significantly reduces end-to-end delay and improves throughput for mission critical applications while optimizes transmission power, thus validating the effectiveness of the proposed solution.

In recent years, green communications and networks have drawn increasing attention from researchers and relevant industries owing to their economic and environmental benefits. In mobile networks, base stations (BSs) account for 56% of network power consumption. Hence, using green technologies in base stations would have a considerable impact on service providers’ energy bills. Consequently, researchers have investigated various green communication techniques. In this paper, we introduce a new traffic steering technique for energy conservation in next generation communication networks. By leveraging spectrum-based cell zooming techniques, the long-term evolution licensed assisted access (LTE-LAA) concept and software defined radio technology, we introduce a green traffic-steering framework that endeavors to minimize overall network on-grid power consumption and maximize aggregated throughput. Considering the time complexity of the optimum solution, we also propose a heuristic solution. We examine the proposed solution against existing solutions, and our obtained results show that the proposed framework saves up to 15% of on-grid power and increases aggregated throughput by 23%.

This research explores the application of chaotic systems in generating pseudo-random numbers for encryption protocols, offering a novel perspective on addressing the challenges posed by limited computer precision in cryptographic applications. Chaotic systems, while promising for encryption, often suffer from degradation in their chaotic properties when implemented on computers with finite precision. Previous studies have primarily aimed to mitigate this issue, with limited consideration of harnessing finite-precision errors as a potential source of randomness. In this study, we propose an innovative encryption method that leverages finite-precision errors within chaotic systems. The algorithm generates a keystream based on lower bound error and employs standard MATLAB routines to describe its main steps, including initialization and image factor addition, Chua’s circuit simulation, error sequence generation, normalization, reshaping of the normalized sequence, and the encryption process. Comprehensive performance evaluations were conducted using benchmark images, including Cameraman, Peppers, and Catherine, each with dimensions of 256 × 256 pixels. Evaluation criteria encompassed key space analysis, pixel correlation, entropy of information, histogram analysis, and resistance to noise attacks. The results highlight the effectiveness and security of the proposed method, showcasing its practical utility in real-world encryption scenarios. This research not only contributes a novel approach to encryption but also provides valuable insights into the potential of utilizing finite-precision errors to enhance randomness generation in chaotic systems for cryptographic applications.