In modern era, which requires the use of networks in the transmission of data across distances, the transport or storage of such data is required to be safe. The protection methods are developed to ensure data security. New schemes are proposed that merge crypto graphical principles with other systems to enhance information security. Chaos maps are one of interesting systems which are merged with cryptography for better encryption performance. Biometrics is considered an effective element in many access security systems. In this paper, two systems which are fingerprint biometrics and chaos logistic map are combined in the encryption of a text message to produce strong cipher that can withstand many types of attacks. The histogram analysis of ciphertext shows that the resulted cipher is robust. Each character in the plaintext has different representations in the ciphertext even if the characters are repeated through the message. The strength of generated cipher was measured through brute force attackers, they were unable to deduce the key from the knowledge about pairs of plaintext- ciphertext due to the fact that each occurrence of characters in the message will have different shift value, and as a result a diverse representation will be obtained for same characters of the message.
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In this paper, we deal with a dynamical system that can demonstrate a chaotic attractor of Rossleroscillator. We simulate the Rosslerequations numerically then we investigate the model experimentally. Numerically, the Rossler parameter a and b were fixed and c was changed.The evolution of the system exhibits period, period-doubling, second period doubling, and chaos when control parameters are changed. This evolution can be seen by analyze the time series, the bifurcation diagrams and phase space. Experimentally, the evolution of the system exhibited the same numerical behavior by changing the resistance (Rv) in Rossler circuit that represent as control parameter.
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The research aims at shedding light on the impact of information technology in reducing tax evasion in the General Authority for Taxation. In order to achieve this, the research relied on the analysis of its variables as a main tool for collecting data and information. The results showed that there is a positive and positive effect of information technology on tax evasion. The impact of information technology on increasing tax revenues and reducing the phenomenon of tax evasion In the performance of the research sample, the research sought to highlight the importance of tax information technology through its data and information to the tax administration for the purpose of completing the process Taxpayers for persons subject to income ta
... Show MoreAs smartphones incorporate location data, there is a growing concern about location privacy as smartphone technologies advance. Using a remote server, the mobile applications are able to capture the current location coordinates at any time and store them. The client awards authorization to an outsider. The outsider can gain admittance to area information on the worker by JSON Web Token (JWT). Protection is giving cover to clients, access control, and secure information stockpiling. Encryption guarantees the security of the location area on the remote server using the Rivest Shamir Adleman (RSA) algorithm. This paper introduced two utilizations of cell phones (tokens, and location). The principal application can give area inf
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Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedu
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The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed
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