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Deep Learning based Bursty Traffic Discrimination and Management using Sandpile Model

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Title Deep Learning based Bursty Traffic Discrimination and Management using Sandpile Model
 
Creator N A, Bharathi
Parthasarathi, Ranjani
Vetriselvi, V
 
Subject Bursty traffic
Flash
Load balancing
Markov modulated poisson process
Sandpile
 
Description 1075-1085
The significant advancements in internet technologies and applications have resulted in a substantial increase in network
traffic volume, presenting considerable challenges for network management. The management of bursty traffic, in particular,
poses difficulties, as it can originate from both legitimate and malicious sources. To ensure the continuity of normal network
operations, it is critical to distinguish between genuine and attack traffic, preventing the blockage of legitimate traffic. This
study proposes a framework for detecting and managing bursty traffic within Software-Defined Networking (SDN)
environments. A deep learning-based approach is applied to differentiate between Distributed Denial of Service (DDoS) and
flash traffic, utilizing the BiLSTM algorithm for its high classification accuracy. This approach uses the Markov Modulated
Poisson Process (MMPP) to generate flash traffic, which is then integrated with the CIC-DDoS2019 dataset. For traffic
management, a drop mechanism is applied to DDoS traffic, while the Bak-Tang-Wiesenfeld (BTW) Sandpile load balancing
algorithm is utilized for managing flash traffic. The proposed Sandpile-based load balancing approach significantly reduces
round-trip time by 93% and packet loss by 98.4%, while improving bandwidth availability by 94.5%. Thus the proposed
approach combines deep learning for precise traffic classification with a dynamic, self-organizing load-balancing
mechanism, offering an efficient and novel solution for managing bursty traffic in real-time network environments.
 
Date 2024-10-21T10:32:31Z
2024-10-21T10:32:31Z
2024-10
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/64727
https://doi.org/10.56042/jsir.v83i10.4981
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.83(10) [October 2024]