A Review of Anomaly Detection Techniques and Distributed Denial of Service (DDoS) on Software Defined Network (SDN)

Khairi, Mutaz H. H.; Ariffin, Sharifah Hafizah Syed; Latiff, N. M. Abdul; Abdullah, Ahmad S.; Hassan, Mohamed Khalafalla

doi:10.48084/etasr.1840

Cited by 48 publications

(24 citation statements)

References 38 publications

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“…The proposed framework utilizes the statistical information for potentially malicious nodes responsible for generating the probing traffic. In [23], the authors discussed the Distributed Denial of Services (DDoS) attacks on SDN networks. Their work also highlighted the anomaly detection techniques for SDN.…”

Section: Related Workmentioning

confidence: 99%

INMTD: Intent-based Moving Target Defense Framework using Software Defined Networks

Hyder

Ismail

2020

Eng. Technol. Appl. Sci. Res.

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Intent-Based Networking (IBN) is an emerging networking paradigm while Moving Target Defense (MTD) is an active security technique. In this paper, the Intent-based Moving Target Defense (INMTD) framework using Software Defined Networks is proposed. INMTD is the first effort in exploiting IBN for the design of an efficient Moving Target Defense (MTD) framework. INMTD uses the concept of shadow servers in order to counter the first stage of cyber-attacks, i.e. reconnaissance attacks targeted against servers running in SDN networks. INMTD comprises of an MTD application running on an SDN controller. The MTD application has reconnaissance detection, MTD movement, and MTD monitoring modules. The MTD application is integrated with the intent-based northbound API of SDN controller. INMTD not only provides protection against probing attacks, but it also provides high availability due to shadow servers. The proposed framework was implemented using Mininet and ONOS SDN controller. The proposed framework was assessed in terms of defender cost, attacker’s effort, and introduced complexity in the system. The results substantiate the efficient protection against reconnaissance attacks at lower computational cost.

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Section: Related Workmentioning

confidence: 99%

INMTD: Intent-based Moving Target Defense Framework using Software Defined Networks

Hyder

Ismail

2020

Eng. Technol. Appl. Sci. Res.

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“…But how to deal with exceptions, no solution is given [6]. Khairi et al (2018) proposed a scheme to detect anomalies and delete the abnormal beacon node. By cancelling the scheme, the beacon nodes with abnormalities are isolated from the positioning calculation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…By cancelling the scheme, the beacon nodes with abnormalities are isolated from the positioning calculation. However, the pressure of the aggregation node and the base station is increased, and the calculation amount of algorithm and energy consumption are large [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploration of Wireless Sensor Network Based on RSSI Positioning Algorithm

Shuan

2018

Int. J. Onl. Eng.

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Based on the security of the receiving signal strength indicator positioning algorithm, the RSSI positioning algorithm in the environment of witch attack, wormhole attack and replication attack has largely failed.Although existing security positioning algorithms can effectively prevent attacks from occurring, the massive consumption of network resources can’t be ignored.Therefore, a tolerable security positioning method is proposed for each of the three attacks in order to improve the security of positioning.According to the node's physical information, the attack node is detected.Through simulation experiments, compared with the traditional indoor security positioning method, the proposed algorithm can significantly reduce the intervention of witch attack, wormhole attack and replication attack on positioning error.While achieving the goal of combating attacks, it reduces the computational complexity, decreases node energy consumption, and extends the network life cycle.

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“…This is to provide direct network programmability and independence of the primary infrastructure of network services and applications by separating the network control and forwarding planes. In general, the SDN is cost-effective, manageable, dynamic, and adaptable which makes it appropriate for the dynamic nature of high-bandwidth modern applications [2]. SDN presents a virtualised execution framework which separates the network control functions from the underlying network forwarding traffic [3].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Conflict Flows in SDN Using Machine Learning Algorithms

et al. 2021

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Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows.

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A Review of Anomaly Detection Techniques and Distributed Denial of Service (DDoS) on Software Defined Network (SDN)

Cited by 48 publications

References 38 publications

INMTD: Intent-based Moving Target Defense Framework using Software Defined Networks

INMTD: Intent-based Moving Target Defense Framework using Software Defined Networks

Exploration of Wireless Sensor Network Based on RSSI Positioning Algorithm

Detection and Classification of Conflict Flows in SDN Using Machine Learning Algorithms

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