Hatim Alsuwat

The development and growth of internet-based technologies brings challenges in cyber-security from cyberattack organizations. It is necessary to develop an appropriate cyber-security scheme through a structured machine learning algorithm implemented in the cloud architecture. The aim of our research is to design a cloud-based architecture using an adaptive cloud authentication protocol to develop the trust model for cyber-security systems. Here, the structure learning of Bayesian networks (SL_BN) is developed with an ensemble architecture of machine learning that is integrated with the support vector machine and adaptive fuzzy-based genetic algorithm (SVM_AFGA). The fuzzy logic sets rules for using web applications at the authorized web address. The experimental results show that parametric analysis based on the cyber-security authentication model is based on median delay, transmission level overhead, and security of the network. Moreover, the parameters based on the SL-BN ensemble architecture are average runtime, accuracy, and network efficiency.

Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study’s findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%.

This paper addresses the problem that database shuffling algorithms do not preserve data dependencies. We introduce an approach for preserving functional dependencies and data-driven associations during database shuffle. We use Boyce-Codd Normal Form (BCNF) decomposition for preserving functional dependencies. Given a relation R that is not in BCNF form, we recommend to decompose R into BCNF relations R1, ..., Rn. Each Ri (i = 1, ...,n) is shuffled then rejoined to create the shuffled relation. Our approach guarantees losslessness and preserves functional dependencies. Data-driven associations may also be lost during database shuffling. For this, we generate the transitive closure of attributes that are associated. We require that the associated attributed are shuffled together. We also present our theoretical and empirical results.