Abstract-Gigantic development in system based services had brought about the upsurge of web users, security dangers and digital assaults. Intrusion detection systems (IDSs) have turned into a basic segment of every network architecture, in order to safe an IT foundation from the malignant activities of the intruders. A proficient ID ought to have the capacity to detect, recognize and track the malevolent attempts made by the intruders. Intrusion is broadly perceived as an unending and repeating issue of computer systems' security with the persistent changes and expanding volume of hacking systems. The Intrusion detection system identification framework manages gigantic measure of information which contains irrelevant and redundant features producing slow training and testing process, higher asset utilization as well as poor detection rate. The feature selection approach gives enhanced prediction and reduces the computation time. Because the higher numbers of features the comprehension of the data in pattern recognition becomes difficult sometimes. That is the reason analysts have utilized diverse feature selection techniques with the single classifiers in their intrusion detection system framework to develop a model which gives a better accuracy and prediction performance. Feature selection, therefore, is a critical issue in intrusion detection. In this paper we present ideas and algorithms of feature selection used by researchers, survey existing feature selection algorithms intrusion detection system.
Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural Network (Text-CNN) and Long Short-Term Memory (LSTM) architecture to produce efficient hybrid model. Text-CNN is used to identify the relevant features, whereas the LSTM is applied to deal with the long-term dependency of sequence. The results showed that when trained individually, the proposed model outperformed both the Text-CNN and the LSTM. Accuracy was used as a measure of model quality, whereby the accuracy of the Hybrid Deep Neural Network is (0.914), while the accuracy of both Text-CNN and LSTM is (0.859) and (0.878), respectively. Moreover, the results of our proposed model are better compared to previous work that used the same dataset (AraNews dataset).
The cloud paradigm has swiftly developed, and it is now well known as one of the emerging technologies that will have a significant influence on technology and society in the next few years. Cloud computing also has several benefits, including lower operating costs, server consolidation, flexible system setup, and elastic resource supply. However, there are still technological hurdles to overcome, particularly with real-time applications by providing resources. Resources allocation management most charming part of cloud computing; therefore, several authors have worked in the area of resource usage. This study introduces an innovative cloud machine learning framework-based linear regression approach called cloud linear regression (CLR), which entails both cloud technology and machine learning concept. CLR using machine learning yielded good prediction results for resource allocation management, as appeared with many researching, and still seek, research to raise optimal solutions to the resources' allocation problem as the aim of this study. This study discusses the relation between cloud resource allocation management and machine learning techniques by illustrating the role of linear regression methods, resource distribution, and task scheduling. The analytical analysis shows that the CLR promises to present an effective solution for resources (scheduling, provisioning, allocation, and availability).
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