A Flow-based IDS for SDN-enabled Tactical Networks

Zwane, Skhumbuzo; Tarwireyi, Paul; Adigun, Matthew O.

doi:10.1109/imitec45504.2019.9015900

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…Zwane et al [245] propose an ML-based IDS for Tactical Ad hoc Mobile Network (TMANET) that resembles the concept of SDN. Their proposed model consists of two SDN controllers: local and global.…”

Section: Random Forest (Rf)mentioning

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: Random Forest (Rf)mentioning

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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“…In the past decade, the research community has proposed several solutions often adopting distributed system approaches [2]. Moreover various works focus on the security applications offered by SDN, such as [17,18,22,23,26] Zhao and Wu [24] proposed a scalable SDN architecture that can handle WAN traffic without overloading the control plane. The authors propose a joint design methods that take into consideration multiple parameters by formulating an Integer Linear Programming (ILP) problem.…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant SDN Solution for Cybersecurity Applications

Liatifis

Dalamagkas

Radoglou-Grammatikis

et al. 2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

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The rapid growth of computer networks in various sectors has led to new services previously hard or impossible to implement .Internet of Things has also assisted in this evolution offering easy access to data but at the same time imposing constraints on both security and quality of service. In this paper, an SDN fault tolerant and resilient SDN controller design approach is presented. The proposed solution is suitable for a wide range of environments. Benefits stemming from actual scenarios are presented and discussed among other solutions.

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“…It supervised and learned the sequence number of each RREP with training data updated dynamically at regular intervals for three features in a host-based detection design. In [36], they proposed a flow-based detection system that leverages ML and software-defined network in a tactical MANET. The system works in global and local controller design, gathering data from mobile nodes.…”

Section: Heuristic and Learning Systemsmentioning

confidence: 99%

Learning from Routing Information for Detecting Routing Misbehavior in Ad Hoc Networks

Basomingera

Choi

2020

Sensors

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Owing to ad hoc wireless networks’ properties, the implementation of complex security systems with higher computing resources seems troublesome in most situations. Therefore, the usage of anomaly or intrusion detection systems has attracted considerable attention. The detection systems are implemented either as host-based, run by each node; or as cluster/network-based, run by cluster head. These two implementations exhibit benefits and drawbacks, such as when cluster-based is used alone, it faces maintaining protection when nodes delay to elect or replace a cluster head. Despite different heuristic approaches that have been proposed, there is still room for improvement. This work proposes a detection system that can run either as host- or as cluster-based to detect routing misbehavior attacks. The detection runs on a dataset built using the proposed routing-information-sharing algorithms. The detection system learns from shared routing information and uses supervised learning, when previous network status or an exploratory network is available, to train the model, or it uses unsupervised learning. The testbed is extended to evaluate the effects of mobility and network size. The simulation results show promising performance even against limiting factors.

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A Flow-based IDS for SDN-enabled Tactical Networks

Cited by 10 publications

References 18 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

Fault-Tolerant SDN Solution for Cybersecurity Applications

Learning from Routing Information for Detecting Routing Misbehavior in Ad Hoc Networks

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