31 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
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 %
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
KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories
No full textWe present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-source machine learning tools, such as GROBID, S2ORC, and BioC to streamline the processes of data extraction and data mining. Central to the functionality of KnowVID-19 is its keyword-based text classification process, which plays a pivotal role in organizing and categorizing the extracted information. By employing machine learning techniques for keyword extraction—specifically RAKE, YAKE, and KeyBERT—KnowVID-19 systematically categorizes publication data into distinct topics and subtopics. This topic structuring enhances the system’s ability to match user queries with relevant research, improving both the accuracy and efficiency of the search results. In addition, KnowVID-19 leverages the NetworkX Python library to construct networks of the most relevant terms within publications. These networks are then visualized using Cytoscape software, providing a graphical representation of the relationships between key terms. This network visualization allows researchers to easily track emerging trends and developments related to COVID-19, long COVID, and associated topics, facilitating more informed and user-centered exploration of the scientific literature. KnowVID-19 also provides an interactive web application with an intuitive, user-centered interface. This platform supports seamless keyword searching and filtering, as well as a visual network of term associations to help users quickly identify emerging research trends. The responsive design and network visualization enables efficient navigation and access to targeted COVID-19 literature, enhancing both the user experience and the accuracy of data-driven insights
End-to-End Real-Time Demonstration of the Slotted, SDN-Controlled NEPHELE Optical Datacenter Network
No full textThe NEPHELE hybrid electro-optical datacenter network (DCN) architecture is proposed as a dynamic network solution to provide high capacity, scalability, and cost efficiency in comparison to the existing DCN infrastructures. The details of the NEPHELE DCN architecture and its various key parts are introduced, and the performance of its implementation is evaluated through an end-to-end NEPHELE demonstrator, which was built at the National Technical University of Athens. Several communication scenarios are demonstrated in real time, exploiting a scalable optical data-plane architecture with a software-defined network (SDN) control plane capable of slotted operation for dynamic allocation of network resources. Real-time end-to-end functionality and integration of various software and hardware components are verified in a six-host prototype datacenter cluster
SDN Control Framework with Dynamic Resource Assignment for Slotted Optical Datacenter Networks
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Schlussbericht GWDG
No full textDas AI-NET-PROTECT konzentriert sich auf die Bereitstellung automatisierter Resilienz und sicherer Netzwerke unter Verwendung vertrauenswürdiger Geräte für kritische Infrastrukturen und Unternehmen. Was AI-NET-PROTECT von anderen nationalen und europäischen Förderprojekten unterscheidet, ist seine einzigartige Struktur und starke Marktnähe. Die Einbeziehung medizinischer Use cases im erweiterten Projektumfang zeigt die praktischen Anwendungen des Projekts. Diese Anwendungsfälle, die in Zusammenarbeit mit Anwendungspartnern und offenen Projekten entwickelt wurden, präsentieren Lösungen zur Bekämpfung von Pandemien wie COVID-19 und zur Verbesserung der nationalen Vorbereitung auf zukünftige Szenarien. Insbesondere untersucht AI-NET-PROTECT medizinische Anwendungsfälle, offene Testinfrastrukturen und Edge AI, um die Konnektivität und Resilienz kritischer Infrastrukturen zu verbessern.
Die GWDG entwickelt ein skalierbares Monitoring-System für SDN-Netzwerke, um Zuverlässigkeit und Wartbarkeit zu verbessern, basierend auf Open-Source-Komponenten. Dabei werden OpenFlow-Agenten eingeführt, um Rechenzentrumsnetzwerke zu automatisieren, indem sie SDN-Controller-Anweisungen
für die Data Plane übersetzen. Zudem implementiert die GWDG ein KI-gestütztes System zur Vermeidung von Netzwerkangriffen, das die Erkennungsleistung verbessert und die Widerstandsfähigkeit gegenüber Zero-Day-Angriffen erhöht. Ein weiteres zentrales Projekt ist die Entwicklung eines KIbasierten,
selbstlernenden Tools für die medizinische Forschung, das frei verfügbare SARS-CoV-2- und COVID-19-Daten sammelt und kategorisiert, um sie in einem Knowledge Graph zu strukturieren. Dazu gehören eine Datenbank, Web-Crawling und ein intelligenter Chatbot, der Forschern aktuelle Erkenntnisse
liefert, während die Datenerfassung automatisiert aktualisiert wird.
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