The electric power system is undergoing considerable changes in operation, maintenance, and planning as a result of the integration of Renewable Energy Resources (RERs). The transition to a smart grid (SG), which employs advanced automation and control techniques, brings with it new difficulties and possibilities. This paper provides an overview of next-generation smart grids by presenting the most current and cutting-edge developments in the SG sector. This paper discusses the benefits, drawbacks, and prospects of smart grids. The difficulties of integrating RERs into the grid, as well as alternative energy storage solutions, are discussed. The unpredictable nature of resources has an impact on RER output. The energy storage system is critical in dealing with RERs’ unpredictable nature and ensuring a smooth and reliable supply to load demand. Smart energy systems provide a number of problems and possibilities in terms of developing, integrating, and implementing electrical grids that incorporate network and communication technologies, as well as important privacy and security concerns for various components within the grid. This paper also shows the influence of SGs on distributed energy generation, as well as a comparative analysis on electric cars (EVs), including classification, i.e., battery, and hybrid electric vehicles, as well as current difficulties and challenges in EV technology. A discussion of SG protection concerns and their resolution is also included.

Advanced Metering Infrastructure (AMI) plays a crucial role in enabling the efficient functioning of Smart Electrical Grids, but its successful implementation hinges on robust cybersecurity measures. To uphold data confidentiality and integrity, the deployment of an effective key management scheme (KMS) for multiple Smart Meters (SMs) and devices is imperative. The AMI exhibits unique characteristics, including storage and computation constraints in SMs, hybrid message transmission techniques, and varying participation levels in Demand Response (DR) projects, necessitating a tailored approach to security compared to other systems. In this research, we propose a KMS that is designed to address the specific security concerns of the AMI. The scheme comprises three key management procedures catering to the unicast, broadcast, and multicast modes of hybrid transmission. Given the resource limitations of SMs, we adopted simple cryptographic techniques for key creation and refreshing policies, ensuring efficiency without compromising on security. Furthermore, considering the variability of participants in DR projects, we established key refreshing policies that adapted to changing involvement. The effectiveness and security of the proposed KMS were rigorously evaluated, demonstrating its practical applicability and ability to safeguard the AMI ecosystem. The results of the evaluation indicate that our approach provides a viable and robust solution to the security challenges faced by AMI systems. By employing the proposed KMS, stakeholders can confidently deploy and manage AMI, ensuring the protection of sensitive data and maintaining the integrity of the Smart Electrical Grid.

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.

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.

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.