Intelligent software defined networking: Long short term memory-graded rated unit enabled block-attack model to tackle distributed denial of service attacks,Transactions on Emerging Telecommunications Technologies

当前位置： X-MOL 学术 › Trans. Emerg. Telecommun. Technol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Intelligent software defined networking: Long short term memory-graded rated unit enabled block-attack model to tackle distributed denial of service attacks
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2022-07-19 , DOI: 10.1002/ett.4594
Monica Murlidhar Jagtap ₁ , Renuka Devi Saravanan ₁

Affiliation

Software defined networking (SDN) is the next-generation network. SDN enhances the programming flexibility, speed and automation to improve the network's performance. In recent times SDN has played a vital role in networking technology. It communicates with underlying hardware infrastructure and directs traffic on a network. The most complicated issue in SDN is the control plane's single point of failure (SPF). The main reason for raising the SPF problem in SDN by distributed denial of service (DDoS) attacks. The network collapses during failures in SDN, and the control plane is considered a management controller. Therefore, a novel intrusion detection and prevention system (IDPS) is proposed in the proposed approach to address SDN's problems mentioned above. In the proposed approach, a long short term memory (LSTM) and graded rated unit (GRU) deep learning model is proposed as the “Block-Attack” model. The main objective of using LSTM and GRU in the proposed approach is to enhance the rate of accuracy in detecting DDoS attacks in an SDN environment. The CICDDoS2019 dataset is used for experimental result analysis in the proposed approach. Initially, the dataset is fed into the preprocessing stage. Using the K-medoid technique, raw datasets are preprocessed to reduce the model's sensitivity to low density. In the proposed approach, the support vector machine based machine learning (SVM-ML) technique is utilized to prevent DDoS attacks in a Mininet-based emulation. Then, LSTM and GRU deep learning (DL) techniques are used to define the block-attack model to enhance the detection performance. The experimental results of the proposed approach “Block-Attack” model attain 98.5% of accuracy to detect and prevent the DDoS attacks and 95.5% of accuracy for SVM based method.

中文翻译：

智能软件定义网络：长期短期内存分级额定单元启用块攻击模型以应对分布式拒绝服务攻击

软件定义网络 (SDN) 是下一代网络。SDN 提高了编程的灵活性、速度和自动化程度，从而提高了网络的性能。近年来，SDN在网络技术中发挥了至关重要的作用。它与底层硬件基础设施通信并引导网络上的流量。SDN 中最复杂的问题是控制平面的单点故障 (SPF)。分布式拒绝服务 (DDoS) 攻击引发 SDN 中 SPF 问题的主要原因。SDN 发生故障时网络崩溃，控制平面被视为管理控制器。因此，在提出的方法中提出了一种新颖的入侵检测和预防系统（IDPS）来解决上述SDN的问题。在建议的方法中，提出了一种长短期记忆（LSTM）和分级额定单元（GRU）深度学习模型作为“块攻击”模型。在所提出的方法中使用 LSTM 和 GRU 的主要目的是提高在 SDN 环境中检测 DDoS 攻击的准确率。CICDDoS2019 数据集用于所提出方法中的实验结果分析。最初，数据集被送入预处理阶段。使用 K-medoid 技术，对原始数据集进行预处理，以降低模型对低密度的敏感性。在所提出的方法中，基于支持向量机的机器学习 (SVM-ML) 技术用于防止基于 Mininet 的仿真中的 DDoS 攻击。然后，使用 LSTM 和 GRU 深度学习 (DL) 技术定义块攻击模型以提高检测性能。

更新日期：2022-07-19

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11