Computer systems and networks are being used in almost every aspect of our daily life; as a result the security threats to computers and networks have also increased significantly. Traditionally, password-based user authentication is widely used to authenticate legitimate user in the current system0T but0T this method has many loop holes such as password sharing, shoulder surfing, brute force attack, dictionary attack, guessing, phishing and many more. The aim of this paper is to enhance the password authentication method by presenting a keystroke dynamics with back propagation neural network as a transparent layer of user authentication. Keystroke Dynamics is one of the famous and inexpensive behavioral biometric technologies, which identifies a user based on the analysis of his/her typing rhythm. This paper utilizes keystroke features including dwell time (DT), flight time (FT), up-up time (UUT), and a mixture of theses features as keystroke representation. The back propagation neural network is trained with the mean of keystroke timing information for each character of password. These times are used to discriminate between the authentic users and impostors. Results of the experiments demonstrate that the backpropagation network with UUT features comparable to combination of DT and FT. Also, the results of backpropagation with combination of DT, FT and UUT provide low False Alarm Rate (FAR) and False Reject Rate (FRR) and high accuracy.
The current research aims to prepare a proposed Programmebased sensory integration theory for remediating some developmental learning disabilities among children, researchers prepared a Programme based on sensory integration through reviewing studies related to the research topic that can be practicedby some active teaching strategies (cooperative learning, peer learning, Role-playing, and educational stories). The Finalformat consists of(39) training sessions.
(3)
(2)
(3)
(2)
(15)
In this work, the detection of zinc (Zn) ions that cause water pollution is studied using the CSNPs- Linker-alkaloids compound that was prepared by linking extracted alkaloids from Iraqi Catharanthus roseus plant with Chitosan nanoparticles (CSNPs) using maleic anhydride. This compound is characterized by an X-ray diffractometer (XRD) which shows that it has an orthorhombic structure with crystallite size in the nano dimension. Zeta Potential results show that the CSNPs-Linker-alkaloids carried a positive charge of 54.4 mV, which means it possesses high stability. The Fourier transform infrared spectroscopy (FTIR) shows a new distinct band at 1708.93 cm-1 due to C=O esterification. Scanning electron microscope (SEM) image
... Show More
(1)
(1)
(5)
(3)
In this work a model of a source generating truly random quadrature phase shift keying (QPSK) signal constellation required for quantum key distribution (QKD) system based on BB84 protocol using phase coding is implemented by using the software package OPTISYSTEM9. The randomness of the sequence generated is achieved by building an optical setup based on a weak laser source, beam splitters and single-photon avalanche photodiodes operating in Geiger mode. The random string obtained from the optical setup is used to generate the quadrature phase shift keying signal constellation required for phase coding in quantum key distribution system based on BB84 protocol with a bit rate of 2GHz/s.
(2)
A geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add-on, you can locate service regions around any network
... Show More
In this paper, the researcher suggested using the Genetic algorithm method to estimate the parameters of the Wiener degradation process, where it is based on the Wiener process in order to estimate the reliability of high-efficiency products, due to the difficulty of estimating the reliability of them using traditional techniques that depend only on the failure times of products. Monte Carlo simulation has been applied for the purpose of proving the efficiency of the proposed method in estimating parameters; it was compared with the method of the maximum likelihood estimation. The results were that the Genetic algorithm method is the best based on the AMSE comparison criterion, then the reliab
... Show More