Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks can also be made with smart devices that connect to the Internet, which can be infected and used as botnets. They use Deep Learning (D.L.) techniques like Convolutional Neural Network (C.N.N.) and variants of Recurrent Neural Networks (R.N.N.), such as Long Short-Term Memory (L.S.T.M.), Bidirectional L.S.T.M., Stacked L.S.T.M., and the Gat G.R.U.. These techniques have been used to detect (DDoS) attacks. The Portmap.csv file from the most recent DDoS dataset, CICDDoS2019, has been used to test D.L. approaches. Before giving the data to the D.L. approaches, the data is cleaned up. The pre-processed dataset is used to train and test the D.L. approaches. In the paper, we show how the D.L. approach works with multiple models and how they compare to each other.
This study aims to identify the amount of the effect of the ability to learn the individuals within the organization on the accumulation of intellectual capital and the role it plays in improving the performance of the organization, and to achieve that, the researcher designed a questionnaire to collect data and information from the surveyed respondents and analyzed using SPSS software, the study concluded after testing hypotheses to have a direct impact between the capacity for organizational learning and the accumulation of intellectual capital, which in turn affects the accumulation of intellectual capital as a positive and direct impact on the performance of the organization, al
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This study aims to evaluate the influence of the air abrasion of dentin on the shear bond strength of lithium disilicate using three different types of luting cements. Sixty cylindrical specimens were milled from lithium disilicate CAD/CAM blocks (IPSe.max CAD). Sixty sound human maxillary premolar teeth were decoronated to the level of peripheral dentin, then randomly divided into three groups according to the type of luting cement used for the cementation of the lithium disilicate specimens (n = 20); Group A: Glass ionomer cement (Riva Self- Cure); Group B: Adhesive resin cement (Rely X Ultimate); Group C: Self-adhesive resin cement (Rely X U200). Each group was then further subdivided into two subgroups (n=10); Subgroups AI, BI, and CI,
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This research includes theoretical and evaluation design of a polarizer filter of high transmission in the near IR region of (900-1200nm) for different incidence angles to obtain a long wave and short wave pass filter using analytical calculations. Results refer to a new configuration design in fewer layers than used in previous studies in the long wave pass at incidence angles (45o,50o,55o). Adopted Hafnium dioxide (HfO2) and Magnesium fluoride (MgF2) as coating material at design wavelength (933nm), the study also included design short wave pass polarizer by using the same coating material.
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Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca
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The research aims to identify the factors that affect the quality of the product by using the Failure Mode and Effect Analysis (FMEA) tool and to suggest measures to reduce the deviations or defects in the production process. I used the case study approach to reach its goals, and the air filter product line was chosen in the air filters factory of Al-Zawraa General Company. The research sample was due to the emergence of many defects of different impact and the continuing demand for the product. I collected data and information from the factory records for two years (2018-2019) and used a scheme Pareto Fishbone Diagram as well as an FMEA tool to analyze data and generate results.
Par
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Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference
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The aim of this research is to construct a three-dimensional maritime transport model to transport nonhomogeneous goods (k) and different transport modes (v) from their sources (i) to their destinations (j), while limiting the optimum quantities v ijk x to be transported at the lowest possible cost v ijk c and time v ijk t using the heuristic algorithm, Transport problems have been widely studied in computer science and process research and are one of the main problems of transport problems that are usually used to reduce the cost or times of transport of goods with a number of sources and a number of destinations and by means of transport to meet the conditions of supply and demand. Transport models are a key tool in logistics an
... Show MoreBootstrap is one of an important re-sampling technique which has given the attention of researches recently. The presence of outliers in the original data set may cause serious problem to the classical bootstrap when the percentage of outliers are higher than the original one. Many methods are proposed to overcome this problem such Dynamic Robust Bootstrap for LTS (DRBLTS) and Weighted Bootstrap with Probability (WBP). This paper try to show the accuracy of parameters estimation by comparison the results of both methods. The bias , MSE and RMSE are considered. The criterion of the accuracy is based on the RMSE value since the method that provide us RMSE value smaller than other is con
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