Tytuł:

Detection of DDoS Attacks in OpenStack-based Private Cloud Using Apache Spark, Journal of Telecommunications and Information Technology, 2020, nr 4

Autor:

Shinde, Sumedha ; Gumaste, Shweta ; K, Amit ; G., Narayan D.

Temat i słowa kluczowe:

OpenStack ; cloud ; distributed processing ; Apache Spark ; DDoS ; 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

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Data wydania:

2020, nr 4

Typ zasobu:

artykuł

Format:

application/pdf

Identyfikator zasobu:

ISSN 1509-4553, on-line: ISSN 1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności