1,426 research outputs found
Big Data Framework to Detect and Mitigate Distributed Denial of Service(DDoS) Attacks in Software-Defined Networks (SDN)
No full textThe software-defined network is in recent years come into the sight of so many network designers as a successor
to the traditional network. This type of network is vulnerable to DDoS attacks, targeting a different part of the SDN network architecture by continuously injecting fake flows. It imposes substantial processing on the controller, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to legitimate users. This paper proposes a scalable and reliable real-time DDoS attack detection and mitigation framework using machine learning incorporated with a big data pipeline infrastructure for the SDN environment. The framework supports the detection of DDoS attacks with the ability of processing and analyzing the huge amount of network traffic in near real-time. The framework employs the power of big data solutions, such as Apache Kafka,
and Apache Spark in order to realize a scalable pipeline for big data processing. The main objective of this research is to offer a vital supporter which can assist network security systems in addressing the DDoS threats in the SDN environment. For this purpose, machine learning algorithms are also utilized to provide a viable opportunity for classifying network data and detecting DDoS attacks in this regard
Latency-Sensitive Data Allocation for Cloud Storage
No full textCustomers often suffer from the variability of data access time in cloud storage service, caused by network congestion, load dynamics, etc. One solution to guarantee a reliable latency-sensitive service is to issue requests with multiple download/upload sessions, accessing the required data (replicas) stored in one or more servers. In order to minimize storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to tackle. In this paper, we study the latency-sensitive data allocation problem for cloud storage. We model the data access time as a given distribution whose Cumulative Density Function (CDF) is known, and prove that this problem is NP-hard. To solve it, we propose both exact Integer Nonlinear Program (INLP) and Tabu Search-based heuristic. The proposed algorithms are evaluated in terms of the number of used servers, storage utilization and throughput utilization
0480_023_006_Wendell_philips_Stapled_Set_04
No full text1. One-page typewritten letter dated 1 November 1949 to Wendell Phillips from Dr. Aziz S. Atiya, Professor of Medieval History, Faculty of Arts, Farouk I University. 2. One-page handwritten letter to Monseigneur Porphyrios III, Archbishop of Mt. Sinai, from Dr. Aziz S. Atiya, Professor of Medieval History, Faculty of Arts, Farouk I University. 3. One-page typewritten letter dated 28 October 1949 to Dr. Aziz S. Atiya, Professor of Medieval History, Faculty of Arts, Farouk I University, from unknown author. 4. One-page typewritten letter dated 14 October 1949 from Wandell Phillips to Dr. Aziz S. Atiya, Professor of Medieval History, Faculty of Arts, Farouk I Universit
Latency-Sensitive Data Allocation and Workload Consolidation for Cloud Storage
No full textCustomers often suffer from the variability of data access time in (edge) cloud storage service, caused by network congestion, load dynamics, and so on. One ef cient solution to guarantee a reliable latency-sensitive service (e.g., for industrial Internet of Things application) is to issue requests with multiple download/upload sessions which access the required data (replicas) stored in one or more servers, and use the earliest response from those sessions. In order to minimize the total storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to deal with. In this paper, we study the latency-sensitive data allocation problem, the latency-sensitive data reallocation problem and the latency-sensitive workload consolidation problem for cloud storage. We model the data access time as a given distribution whose cumulative density function is known, and prove that these three problems are NP-hard. To solve them, we propose an exact integer nonlinear program (INLP) and a Tabu Search-based heuristic. The simulation results reveal that the INLP can always achieve the best performance in terms of lower number of used nodes and higher storage and throughput utilization, but this comes at the expense of much higher running time. The Tabu Searchbased heuristic, on the other hand, can obtain close-to-optimal performance, but in a much lower running time
An intelligent deep convolutional network based COVID-19 detection from chest X-rays
No full textCoronavirus disease-2019 (COVID-19) seems to be a fast spreading contagious illness that affects both humans and animals. This catastrophic deadly virus has an impact on people's daily lives, their wellbeing, and a nation's economy. According to a clinical research of COVID-19 affected patients, these individuals have been most commonly infected with a lung illness after coming into touch with the virus. A chest X-ray (also known as radiography) or a chest CT scan seems to be more efficient imaging techniques for detecting lung issues. Nonetheless, when compared to a chest CT, a significant chest X-ray remains a less expensive procedure. Thus, in this research, a novel Deep convolution neural network algorithm is presented to detect the COVID-19 from X-ray image. Moreover, to enhance diagnostics sensitivity and reduce error rate, a hybrid Two-step-AS clustering approach with Ensemble Bootstrap aggregating training and Multiple NN methods used. In addition, TSEBANN model has been employed to explore the qualification procedure effects. The proposed algorithm was trained before and after classification while compared to traditional Convolutional Neural Network (CNN). After, the process of pre-processing and feature extraction, the CNN strategy was adopted as an identification approach to categorize the information depending on Chest X-ray recognition. These examples were then classified using the CNN classification technique. The testing was conducted on the COVID-19 X-ray dataset, and the cross-validation approach was used to determine the model’s validity. The result indicated that a CNN system classification has attained an accuracy of 98.062 %
A half-century of metal and metalloid-containing polymers
No full textAlaa S. Abd-El-Aziz ... [et al.]; Includes bibliographical references and indexes.; Editor, Alaa S. Abd-El-Aziz, is currently President of the University of Prince Edward Island.Source type: Electronic(1
An Application-aware SDN Controller for Hybrid Optical-electrical DC Networks
No full textThe adoption of optical switching technologies in Data Centre Networks (DCNs) offers a solution for high speed traffic and energy efficiency in Data Centre (DC) operational management, enabling an easy scaling of DC infrastructures. Flexible, slotted allocation of optical resources is fundamental to efficiently support the dynamicity of DC traffic. In this context, the NEPHELE project proposes a Time Division Multiple Access approach for optical resource allocation, orchestrated through a Software Defined Networking controller which coordinates the DCN configuration based on real-time cloud application requests
Democracy, Like Revolution, is Unattainable Without Women
No full textThe biggest challenge facing women in Arab Spring countries is transforming their leadership and attendant influence during the revolutions into high-level governance positions, both elected and appointed, after the revolutions.
To achieve this goal, reform efforts should include: 1) amending election laws to increase the proportion of women represented at the local, state, and national levels; 2) increasing high-level employment opportunities; and 3) using public education to highlight the role of women and counter perceptions of traditional gender roles.Peer reviewe
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