Design of Ensemble Learning Methods for DDoS Detection in SDN Environment

Deepa, V.; Sudar, K. Muthamil; Deepalakshmi, P.

doi:10.1109/vitecon.2019.8899682

Cited by 45 publications

(27 citation statements)

References 9 publications

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“…Ensemble learning has been presented in the existing literature and is superior to single classifier methods in the domain of anomaly detection [13][14][15][16][17]23,35]. Ensemble learning is an approach in which a set of learning models is combined to enhance predictions performance compared to each separate model.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

Alanazi¹,

Jambi²,

Eassa³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and detection techniques that rely on manually configuring the policies. As the massive traffic from heterogeneous networks increases, conventional solutions are ineffective due to the lack of automation and human interference. This necessitates producing a detection solution, more effective than traditional ones, to ensure SDN security, resiliency, and availability. This paper addresses this problem by proposing a deep learning (DL)-based ensemble solution for efficient DDoS attack detection in SDN. Four hybrid models are presented by adopting three ensemble techniques and different DL architectures, namely convolutional neural network, long short-term memory, and gated recurrent unit, to improve the SDN traffic classification. The experimentation was conducted on the benchmark flow-based dataset CICIDS2017. High detection accuracy (99.77%) with a small number of flow-based features was achieved by our ensemble model, as our experimental results will demonstrate. The proposed solution was evaluated by several standard assessment matrices and by comparing against other state-of-the-art algorithms from the network security literature.

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Section: Ensemble Methodsmentioning

confidence: 99%

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

Alanazi¹,

Jambi²,

Eassa³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…Machine learning techniques have enabled intrusion detection systems to make useful predictions and remarks. A paper [16] developed an ensemble strategy for detecting DDoS attacks, they utilized four distinct machine learning methods. With (98.12%) accuracy, the SVM-SOM algorithm outperformed the other machine learning (ML) algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…4, it's clear that several datasets were utilized to detect attack traffic. Many of the authors employed public datasets including network traffic statistics from classical network architectures, such as NSL-KDD, UNB-ISCX, KDD Cup'99, CICIDS2017, and CAIDA2016 [16], [17], [22]. These datasets are useful for assessing the performance of machine learning techniques used in attack traffic detection.…”

Section: Performance Evaluation Based On Proposed Evolutionary Decisi...mentioning

confidence: 99%

Distributed denial of service attacks detection for software defined networks based on evolutionary decision tree model

Kamel

Abdullah

2022

Bulletin EEI

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The software defined networks (SDN) system has modern techniques in networking, it separates the forwarding plane from the control plane and works to collect control functions in a central unit (controller), and this separation process leads to many advantages, such as cost reduction and programming ability. Concurrently, because of its centralized architecture, it is prone to a variety of attacks. Distributed denial of service (DDoS) attack has a significant impact on SDN, it is characterized by its ability to consume network resources as well as its ability to turn off the entire network. The work in this study aims to improve and increase the security and robustness of SDN systems against the attack or intrusion, by using a machine learning model to detect attack traffic and classify traffic of SDN as (attack or normal), and optimization algorithm (genetic algorithm) for improving the accuracy of the classification. After preparing and preprocessing the dataset, we used the genetic algorithm (GA) to optimize the hyperparameters of the decision tree (DT) model, and the proposed evolutionary decision tree (EDT) model was used to classify traffic into normal and attack traffic. The results indicate that the suggested model achieved a high classification accuracy of 99.46.

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“…Combining three supervised and one unsupervised ML algorithm, namely KNN, NB, SVM, and SOM, Deepa et al [232] present an ensemble model for recognizing DDoS attacks in the SDN controller. By creating a Mininet virtual setup and a POX controller, they have created a virtual network setup, then applied the CAIDA 2016 dataset into the network consisting of TCP, ICMP, and UDP packets.…”

Section: Hybrid Models Based Ids In Sdnmentioning

confidence: 99%

Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques –A Comprehensive Survey

Rayhan¹,

Islam²,

Shatabda³

et al. 2022

Preprint

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<div>At present, the Internet is facing numerous attacks of different kinds that put its data at risk. The safety of information within the network is, therefore, a significant concern. In order to prevent the loss of incredibly valuable information, the Intrusion Detection System (IDS) was developed to recognize the outbreak of a stream of attacks and notify the network system administrator providing network security. IDS is an extrapolative model used to detect network traffic as routine or attack. Software-Defined Networks (SDN) is a revolutionary paradigm that isolates the control plane from the data plane, transforming the concept of a software-driven network. Through this data and control plane separation, SDN provides us the opportunity to create a manageable and programmable network, allowing applications in the top plane to access physical devices via the controller. The controller functioning inside the control plane executes network modules and establishes flow rules to forward packets in the switches residing in the data plane. Cyber attackers target the SDN controller to subdue the control plane, which is considered the brain of the SDN, providing a plethora of functionalities such as regulating flow control to switches or routers in the data plane below via southbound Application Programming Interfaces (APIs) and business and application logic in the application plane above via northbound APIs to implement sophisticated networks. However, the control plane becomes a tempting prospect for security attacks from adversaries because of its centralization feature. This paper includes an in-depth overview of the notable published articles from 2015 to 2021 that used Machine Learning (ML) and Deep Learning (DL) techniques to construct an IDS solution to provide security for SDN. We also present two detailed taxonomic studies regarding IDS, and ML-DL techniques based on their learning categories, exploring various IDS solutions to secure the SDN paradigm. We have also conducted brief research on a few benchmark datasets used to construct IDS in the SDN paradigm. To conclude the survey, we provide a discussion that sheds light on continuous challenges and IDS issues for SDN security.</div>

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Design of Ensemble Learning Methods for DDoS Detection in SDN Environment

Cited by 45 publications

References 9 publications

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

Distributed denial of service attacks detection for software defined networks based on evolutionary decision tree model

Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques –A Comprehensive Survey

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