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Wyszukujesz frazę "Shinde, Sumedha" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
An Efficient Controller Placement Algorithm using Clustering in Software Defined Networks
Autorzy:
Jacob, Joshua
Shinde, Sumedha
Narayan, D. G.
Powiązania:
https://bibliotekanauki.pl/articles/27312951.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
clustering
controller placement
PAM
K-means++
silhouette score
SDN
Opis:
Software defined networking (SDN) is an emerging network paradigm that separates the control plane from data plane and ensures programmable network management. In SDN, the control plane is responsible for decision-making, while packet forwarding is handled by the data plane based on flow entries defined by the control plane. The placement of controllers is an important research issue that significantly impacts the performance of SDN. In this work, we utilize clustering techniques to group networks into multiple clusters and propose an algorithm for optimal controller placement within each cluster. The evaluation involves the use of the Mininet emulator with POX as the SDN controller. By employing the silhouette score, we determine the optimal number of controllers for various topologies. Additionally, to enhance network performance, we employ the meeting point algorithm to calculate the best location for placing the controller within each cluster. The proposed approach is compared with existing works in terms of throughput, delay, and jitter using six topologies from the Internet Zoo dataset.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 4; 9--17
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of DDoS Attacks in OpenStack-based Private Cloud Using Apache Spark
Autorzy:
Gumaste, Shweta
G., Narayan D.
Shinde, Sumedha
K., Amit
Powiązania:
https://bibliotekanauki.pl/articles/1839316.pdf
Data publikacji:
2020
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
cloud
DDoS
distributed processing
OpenStack
Apache Spark
random forest
Opis:
Security is a critical concern for cloud service providers. Distributed denial of service (DDoS) attacks are the most frequent of all cloud security threats, and the consequences of damage caused by DDoS are very serious. Thus, the design of an efficient DDoS detection system plays an important role in monitoring suspicious activity in the cloud. Real-time detection mechanisms operating in cloud environments and relying on machine learning algorithms and distributed processing are an important research issue. In this work, we propose a real-time detection of DDoS attacks using machine learning classifiers on a distributed processing platform. We evaluate the DDoS detection mechanism in an OpenStack-based cloud testbed using the Apache Spark framework. We compare the classification performance using benchmark and real-time cloud datasets. Results of the experiments reveal that the random forest method offers better classifier accuracy. Furthermore, we demonstrate the effectiveness of the proposed distributed approach in terms of training and detection time.
Źródło:
Journal of Telecommunications and Information Technology; 2020, 4; 62-71
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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