A Deep Reinforcement Learning-based DDoS Attack Mitigation Scheme for Securing Big Data in Fog-Assisted Cloud Environment

Cloud computing is supported by Fog computing paradigm for achieving local data investigation at edge devices by offering storage support, infrastructure, computing and network facilities to end users. Availability is considered as the important security requirement for facilitating disruptive utili...

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Bibliographic Details
Published in:Wireless personal communications 2023-06, Vol.130 (4), p.2869-2886
Main Authors: Janakiraman, Sengathir, Deva Priya, M.
Format: Article
Language:English
Subjects:
Big Data
Cloud computing
Communications Engineering
Communications traffic
Computer Communication Networks
Cybersecurity
Deep learning
Denial of service attacks
Edge computing
End users
Engineering
Network analysis
Networks
Signal,Image and Speech Processing
Software-defined networking
Traffic analysis
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Summary:Cloud computing is supported by Fog computing paradigm for achieving local data investigation at edge devices by offering storage support, infrastructure, computing and network facilities to end users. Availability is considered as the important security requirement for facilitating disruptive utilization of on-demand cloud service for diverse client applications. Seamless services of clouds are hindered by Distributed Denial of Service (DDoS) attacks in a cloud computing environment. In this paper, Deep Reinforcement Learning (DRL) and Long Short Term Memory (LSTM)-based DDoS attack mitigation scheme is propounded for handling the impacts of DDoS attacks in fog-assisted cloud computing environment. In the proposed scheme, Software Defined Network (SDN) is utilized for deploying defense module in the SDN controller for the purpose of identifying anomalous characteristics of DDoS attack in transport or network layer. It aids in filtering and forwarding legitimate packets by performing network traffic analysis while circumventing attacks caused by infected packets.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-023-10407-2