Software-Defined Network-Based Intrusion Detection System

Hande, Yogita; Muddana, Akkalakshmi; Darade, Santosh

doi:10.1007/978-981-10-3812-9_55

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…In SDN, the IDS is currently designed using a machine learning approach [31]. A machine learning-based IDS can be trained more easily with the centralization of the SDN [32].…”

Section: Background and Motivationmentioning

confidence: 99%

IDSMatch: A Novel Deployment Method for IDS Chains in SDNs

Niknami,

2024

Network

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With the surge in cyber attacks, there is a pressing need for more robust network intrusion detection systems (IDSs). These IDSs perform at their best when they can monitor all the traffic coursing through the network, especially within a software-defined network (SDN). In an SDN configuration, the control plane and data plane operate independently, facilitating dynamic control over network flows. Typically, an IDS application resides in the control plane, or a centrally located network IDS transmits security reports to the controller. However, the controller, equipped with various control applications, may encounter challenges when analyzing substantial data, especially in the face of high traffic volumes. To enhance the processing power, detection rates, and alleviate the controller’s burden, deploying multiple instances of IDS across the data plane is recommended. While deploying IDS on individual switches within the data plane undoubtedly enhances detection rates, the associated costs of installing one at each switch raise concerns. To address this challenge, this paper proposes the deployment of IDS chains across the data plane to boost detection rates while preventing controller overload. The controller directs incoming traffic through alternative paths, incorporating IDS chains; however, potential delays from retransmitting traffic through an IDS chain could extend the journey to the destination. To address these delays and optimize flow distribution, our study proposes a method to balance flow assignments to specific IDS chains with minimal delay. Our approach is validated through comprehensive testing and evaluation using a test bed and trace-based simulation, demonstrating its effectiveness in reducing delays and hop counts across various traffic scenarios.

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“…In SDN, the IDS is currently designed using a machine learning approach [31]. A machine learning-based IDS can be trained more easily with the centralization of the SDN [32].…”

Section: Background and Motivationmentioning

confidence: 99%

IDSMatch: A Novel Deployment Method for IDS Chains in SDNs

Niknami,

2024

Network

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“…Transmitting packets according to rules given by the controller is the job of the more basic modules known as switches in a software-defined network. Programmatically handling all forwarding is the responsibility of the data plane and SDN control plane, which together form the southbound interface of the SDN [2]. The separation of the control plane and data plane makes it easy for network administrators to modify security policies.…”

Section: Sdn Frameworkmentioning

confidence: 99%

An Intrusion Detection System (Ids) Schemes for Cybersecurity in Software Defined Networks

Srinivas A Vaddadi, Abhilash Maroju, Sravanthi Dontu

2023

IJRITCC

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The process of analysing and improving network traffic is of tremendous relevance to network management and multimedia data mining techniques. Security in Software Defined Networks (SDNs), which rely on a programmable controller in the middle, has recently emerged as the most challenging aspect of SDNs. Network traffic monitoring is critical for detecting and exposing intrusion anomalies in an SDN context. Thus, this study offers a thorough assessment of the NSL-KDD dataset using five separate clustering algorithms: K-means, Farthest First, Canopy, Density-based method, and Exception-maximization (EM). The software used to conduct the comparisons is the Waikato Environment for Knowledge Analysis (WEKA). In addition, the article introduces a knowledge discovery in databases (KDD)–based deep learning (DL) model for intrusion detection that is SDN-based. Initially, the dataset that is being used is clustered into four main attack types and one normal category. We will next go over the steps necessary to build a deep learning intrusion detection system that is based on SDN. The results provide an objective assessment of the several attack types present in the KDD dataset. Just like other methods, the results demonstrate that the proposed deep learning strategy provides better intrusion detection performance. As an illustration, the tested dataset demonstrates a detection accuracy of 94.21% when using the suggested approach.

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“…The division of DL algorithms can be categorized. More information and details about the DL models and structures can be found in [37,38].…”

Section: Deep Learningmentioning

confidence: 99%

An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

Alshammri¹,

Samha²,

Hemdan³

et al. 2022

Computers, Materials &Amp; Continua

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Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process. In recent times, the most complex task in Software Defined Network (SDN) is security, which is based on a centralized, programmable controller. Therefore, monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment. Consequently, this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms: K-means, Farthest First, Canopy, Density-based algorithm, and Exception-maximization (EM), using the Waikato Environment for Knowledge Analysis (WEKA) software to compare extensively between these five algorithms. Furthermore, this paper presents an SDN-based intrusion detection system using a deep learning (DL) model with the KDD (Knowledge Discovery in Databases) dataset. First, the utilized dataset is clustered into normal and four major attack categories via the clustering process. Then, a deep learning method is projected for building an efficient SDN-based intrusion detection system. The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset. Similarly, the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques. For example, the proposed method achieves a detection accuracy of 94.21% for the examined dataset.

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Software-Defined Network-Based Intrusion Detection System

Cited by 6 publications

References 2 publications

IDSMatch: A Novel Deployment Method for IDS Chains in SDNs

IDSMatch: A Novel Deployment Method for IDS Chains in SDNs

An Intrusion Detection System (Ids) Schemes for Cybersecurity in Software Defined Networks

An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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