An SDN-based Intrusion Detection System using SVM with Selective Logging for IP Traceback

Hadem, Pynbianglut; Saikia, Dilip Kr.; Moulik, Soumen

doi:10.1016/j.comnet.2021.108015

Cited by 57 publications

(27 citation statements)

References 17 publications

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“…RF algorithm achieved the best accuracy, whereas DT achieved the best efficiency in terms of processing time. Some other studies [153]- [155] mainly adopted SVM-based IDS solution for SDNs.…”

Section: Supervised ML 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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Section: Supervised ML 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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show abstract

“…Intrusion detection based on traditional machine learning : Hadem et al 15 proposed a software‐defined‐networking‐based IDS using the support vector machines along with selective logging for IP trace back. Its detection accuracy in the dataset NSL‐KDD reaches 95.98%.…”

Section: Related Workmentioning

confidence: 99%

Intrusion detection: A model based on the improved vision transformer

Yang

Gao

et al. 2022

Trans Emerging Tel Tech

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We propose an intrusion detection model based on an improved vision transformer (ViT). More specifically, the model uses an attention mechanism to process data, which overcomes the flaw of the short-term memory in recurrent neural network (RNN) and the difficulty of learning remote dependency in convolutional neural network. It supports parallelization and has a faster computing speed than RNN. A sliding window mechanism is presented to improve the capability of modeling local features for ViT. The hierarchical focal loss function is used to improve the classification effect, and solve the issue of the data imbalance. The public intrusion detection dataset NSL-KDD is used for experimental simulations. By experimental simulations, the accuracy is up to 99.68%, the false-positive rate is 0.22%, and the recall rate is 99.57%, which show that the improved ViT has better accuracy, false positive rate, and recall rate than existing intrusion detection models.

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“…In summary, existing IDSs cannot cope with the dynamic nature of the currently developing attack types [13][14][15][16][17][18][19].…”

Section: Challenge Of Idssmentioning

confidence: 99%

A Comprehensive Systematic Literature Review on Intrusion Detection Systems

Ozkan-Okay¹,

Samet

Aslan

et al. 2021

IEEE Access

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An SDN-based Intrusion Detection System using SVM with Selective Logging for IP Traceback

Cited by 57 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

Intrusion detection: A model based on the improved vision transformer

A Comprehensive Systematic Literature Review on Intrusion Detection Systems

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