ملخص البحث :

The Internet has grown rapidly in the last ten years. Consequently, the interconnection of computers and network devices has become so complex for monitoring that even the security experts do not fully understand its deepest inner workings. Personal computers have become very fast every year. It is not rare for a very ordinary person to connect to the Internet through 20 Mbs lines or faster. With this huge network data the network security has becomes very important for monitoring the data. The big challenge of developing big data in intrusion detection system is time of building system. In this paper, the preprocessing feature section method has been applied to generated subset of relevant features for building the model. The Random forest algorithm was applied to classify the network data. To increase the accuracy of Random forest algorithm the information gain method was used. The NSL-KDD standard data was employed to examine the performance of the proposed model. Various evaluation metrics have proposed to evaluate the proposed model. The empirical results of proposed model show that it is better in terms of performance measures. A comparative analysis of the results obtained of proposed model and different various existing algorithms is presented. The results show that the performance of the proposed model outperformed the performance of existing systems.

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In this research we did a comparison between using Dapper and LINQ to access Databases, the speed of Dapper is growing, which makes us think why such a simple object mapper framework that used to retrieve data and map it to a class takes that much of attention over time, in the very next line is shown in number how Dapper is faster a saver than any other DB connections ways.

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Abstract `Face recognition is one of biometrics system used for surveillance purpose. It use to discovery criminals, suspected terrorists and missed children. The face recognition is term to identify human by using algorithms. In this paper, Local Binary Pattern (LBP) approach has applied to extract features for face recognition. LBP approach is one of powerful and robust method for extraction features from face. Main contributions of this present research work are: First LBP approach used to extract importance features from face. Second: Feature selection methods, Principal Component Analysis (PCA) has employed to remove irrelevant features for increase accuracy of classification. For face recognition the most significant features are necessary due to the face has more dimensionality. Third, Classification, Two classifiers are applied for recognition purpose. Support Vector Machine (SVM) and Linear Discriminates (LD) algorithms have used to classify the features vector which has obtained by LBP method for face recognition. For experimental analysis , the Olivetti Research Laboratory (ORL ) standard data has been used to evaluate the proposed system. The empirical analysis results of proposed system show that it is better in terms of accuracy performance measures. A Comparative analysis results between the classifiers with all features and with PCA method is presented. It is observed that the performance of classifiers with whole features is better. However, the classifier with PCA method is better in cost of time. Finally, it concluded that the proposed system is more robust and better for face recognition.

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This study offers a 5G substrate integrated waveguide (SIW) antenna with vertical S zigzag shape slot. The suggested antenna's radiating patch depends on the shape of the slot. Two types of slots have been used, straight and zigzag S shape slot with vertical and horizontal direction, slots are etched on the patch to increase the antenna's overall bandwidth and gain. The suggested straight and zigzag SIW S slot antennas both resonate at 28 GHz, and the overall structure size is 7.10×14.93 mm. The presented design could achieve high gain, efficiency, and minimal losses, which are all important concerns. The presented antenna may produce a gain of 9.48 dB, a 95% efficiency, and a wider bandwidth of no less than 3.25 GHz at 28 GHz.

ملخص البحث :

Even security specialists find it challenging to monitor the complex interconnections of computers and network devices brought about by the expansion of the internet over the past ten years. Network security has grown to be a major problem as personal computers have become faster and highspeed internet has become more widely accessible. It is extremely difficult to create intrusion detection systems that can manage massive amounts of data, especially in terms of system construction time. This work suggests a preprocessing feature selection strategy that creates subsets of pertinent characteristics to ease model construction in order to overcome this difficulty. The suggested model uses the information gain method to improve accuracy while classifying network data using the Random Forest algorithm. Using the NSL-KDD reference dataset, the suggested model's efficacy is assessed. Several measures are used to determine how well it performs. According on empirical findings, the recommended model outperforms existing algorithms in terms of performance measures. It offers a contrast. Overall, the proposed methodology has a great deal of promise to enhance large data intrusion detection systems' functionality. Keywords: Cybersecurity, Random Forest Algorithm, Big Data, Intrusion Detection System, Performance Analysis, Threat Detection, Anomaly Detection