Restricted Boltzmann Machine based detection system for DDoS attack in Software Defined Networks

MohanaPriya, Pitchai; Shalinie, S. Mercy

doi:10.1109/icscn.2017.8085731

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…Mohanapriya and Shalinie demonstrate a DDoS detection method based on RBM [209]. The suggested approach is divided into two phases: data acquisition and attack detection.…”

Section: Unsupervised DL Based Ids In Sdnmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Mohanapriya and Shalinie demonstrate a DDoS detection method based on RBM [209]. The suggested approach is divided into two phases: data acquisition and attack detection.…”

Section: Unsupervised DL Based Ids In Sdnmentioning

confidence: 99%

“…The HYBRID-CNN uses dual-channel data input to extract useful features from 1D and 2D flow data. Flow-based [209] It fuses essential features using the self-attention process and ultimately detects using an FCN. Elsayed et al proposed a new hybrid DL-approach based on CNN and SD-Reg, a new regularizer technique [228].…”

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

View full text Add to dashboard Cite

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“…K-means clustering [151][152][153][154][155], SOM [52,[156][157][158][159][160], HMM [53,161], RBMs [162] and unsupervised deep learning approaches [54][55][56][57] were the most used unsupervised learning techniques in SDN paradigm.…”

Section: Unsupervised Learning In Sdnmentioning

confidence: 99%

“…RBM in SDN: MohanaPriya and Shalinie [162] studied detection of DDoS attacks in SDN based on RBM. RBM was trained using constrastive divergence algorithm.…”

Section: Unsupervised Learning In Sdnmentioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

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Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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“…FGSM caused a significant reduction in the accuracy of LSTM (more than 50%). [154][155][156][157][158], self-organizing map [159][160][161][162][163][164], hidden Markov model [54,165], Restricted Boltzmann machines [166], and unsupervised deep learning approaches [55,[56][57][58] were the most used unsupervised learning techniques in SDN paradigm.…”

Section: Supervised Deep Learning In Sdnmentioning

confidence: 99%

Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

Latah,

Toker

2018

Preprint

View full text Add to dashboard Cite

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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Restricted Boltzmann Machine based detection system for DDoS attack in Software Defined Networks

Cited by 17 publications

References 17 publications

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

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

Artificial intelligence enabled software‐defined networking: a comprehensive overview

Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

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