In this article, we present an encryption technique that can encrypt any digital data. The proposed scheme basically depends on a substitution-permutation network. Two separate bijective maps are used in the proposed algorithm. Firstly, the substitution boxes are used to perform the substitution process. This technique uses elliptic curves (ECs) to create several substitution boxes with good cryptographic properties. The generated substitution boxes are utilized to replace the arrange data that produces the most uncertainty in the plain image data. Further, we used the permutation process to generate strong randomness in the proposed technique. For the evaluation of the stability of the proposed algorithm; nonlinearity, linear approximation probability, bit independence criterion, strict avalanche criterion and differential approximation probability were performed on the substitution boxes. Differential attacks (Number of Changing Pixel Rate (NPCR), Uniform Average Change Intensity (UACI)) and statistical tests (Entropy, Correlation) were performed on the encrypted images to check their resistance against different attacks. The execution and image analysis reveals that the proposed scheme attains good encryption results while requiring minimal computing ability and has efficient potential in real-time image encryption applications.

As the internet of things (IoT) continues to proliferate, the need for efficient and secure data encryption has become increasingly critical, particularly for resource-constrained devices. Existing encryption methods offer adequate security for digital data; however, they often fall short when applied to resource-constrained IoT devices. This research introduces a novel lightweight encryption algorithm optimized with metaheuristic techniques, incorporating quantum encryption, confusion and diffusion operations, discrete wavelet transform (DWT), and multiple chaotic maps. Initially, a color image is decomposed into its three color components-red (R), green (G), and blue (B)-and then transformed into its quantum representation, where quantum encryption operations are performed. Following this, the quantum image is transformed back into a classical format to apply confusion and diffusion techniques. Confusion is achieved by generating a substitution matrix and applying a modular operation to introduce pixel-level confusion. A key matrix is then created to implement the diffusion operation. In the final phase, DWT is used to extract frequency sub-bands, forming a low-frequency sub-band and further extracting sub-bands up to the 4th level, which are substituted using values from the substitution box. The performance of the proposed encryption framework is evaluated through various statistical analyses, including entropy, correlation, key sensitivity, lossless analysis, and histogram analysis. The results demonstrate notable statistical measures with an entropy of 7.9998, a correlation of 0.0001, and a key space of \(2^{947.862}\). Additionally, the encryption’s robustness is tested against several cyberattacks, such as noise, cropping, and brute force, showcasing its effectiveness in resisting these threats.

According to the standard paradigm, white box cryptographic primitives are used to block black box attacks and protect sensitive information. This is performed to safeguard the protected information and keys against black box assaults. An adversary in such a setting is aware of the method and can analyze many system inputs and outputs, but is blind to the specifics of how a critical instantiation primitive is implemented. This is the focus of white-box solutions, which are designed to withstand attacks that come from the execution environment. This is significant because an attacker may obtain unrestricted access to the program’s execution in this environment. The purpose of this article is to assess the efficiency of white-box implementations in terms of security. Our contribution is twofold: first, we explore the practical implementations of white-box approaches, and second, we analyze the theoretical foundations upon which these implementations are built. First, a research proposal is crafted that details white-box applications of DES and AES encryption algorithms. To begin, this preparation is necessary. The research effort planned for this project also includes cryptanalysis of these techniques. Once the general cryptanalysis results have been examined, the white-box design approaches will be covered. We have decided to launch an investigation into creating a theoretical model for white box, since no prior formal definitions have been offered, and suggested implementations have not been accompanied by any assurance of security. This is due to the fact that no formal definition of “white box” has ever been provided. In this way lies the explanation for why this is the situation. We define WBC to encompass the security requirements of WBC specified over a white box cryptography technology and a security concept by studying formal models of obfuscation and shown security. This definition is the product of extensive investigation. This state-of-the-art theoretical model provides a setting in which to investigate the security of white-box implementations, leading to a wide range of positive and negative conclusions. As a result, this paper includes the results of a Digital Signature Algorithm (DSA) study which may be put to use in the real world with signature verification. Possible future applications of White Box Cryptography (WBC) research findings are discussed in light of these purposes and areas of investigation.

The IoT sensor applications have grown in extreme numbers, generating a large amount of data, and it requires very effective data analysis procedures. However, the different IoT infrastructures and IoT sensor device layers possess protocol limitations in transmitting and receiving messages which generate obstacles in developing the smart IoT sensor applications. This difficulty prohibited existing IoT sensor implementations from adapting to other IoT sensor applications. In this article, we study and analyze how IoT sensor produces data for big data analytics, and it also highlights the existing challenges of intelligent solutions. IoT sensor applications required big data classification and analysis in a Fog computing (FC) environment using computation intelligence (CI). Our proposed Fog big data analysis model (FBDAM) and BPNN analysis model for IoT sensor application using fusion deep learning (FDL) pose new obstacles for potential machine-to-machine communication practices. We have applied our proposed FBDAM on the most significant Fog applications developed on smart city datasets (parking, transportation, security, and sensor IoT dataset) and got improving results. We compared different deep and machine learning algorithms (SVM, SVMG-RBF, BPNN, S3VM, and proposed FDL) on different smart city dataset IoT application environments.

Web attacks and web defacement attacks are issues in the web security world. Recently, website defacement attacks have become the main security threats for many organizations and governments that provide web-based services. Website defacement attacks can cause huge financial and data losses that badly affect the users and website owners and can lead to political and economic problems. Several detection techniques and tools are used to detect and monitor website defacement attacks. However, some of the techniques can work on static web pages, dynamic web pages, or both, but need to focus on false alarms. Many techniques can detect web defacement. Some are based on available online tools and some on comparing and classification techniques; the evaluation criteria are based on detection accuracies with 100% standards and false alarms that cannot reach 1.5% (and never 2%); this paper presents a literature review of the previous works related to website defacement, comparing the works based on the accuracy results, the techniques used, as well as the most efficient techniques.

In terms of growth, effect, and capability, the 5G-enabled Internet of Things (IoT) is incredible. The volume of data distributed and processed by IoT (Internet of Things) systems that trust connectivity and coverage raises some security problems. As IoT technology is directly used in our daily lives, the threats of present cyberspace may grow more prominent globally. Extended network life, coverage, and connectivity are all required for securing IoT-based 5G network devices. As a result of these failures, there are flaws that lead to security breaches. Because purposeful faults can quickly render the entire network dysfunctional, they are more difficult to identify than unexpected failures. Securing IoT-based 5G Network Device Connectivity and Coverage for expending Encryption and Authentication Scheme (EAS) framework is proposed in this study, which uses novel security flaws. In this research, we proposed a Boltzmann machine (BMKG)-based encryption algorithm for securing 5G-enabled IoT device network environment and compared various asymmetric algorithms for key exchange.