Concept of Intelligent Detection of DDoS Attacks in SDN Networks Using Machine Learning

Klymash, Mykhailo; Shpur, Olga; Peleh, Nazar; Maksysko, Oksana

doi:10.1109/picst51311.2020.9467963

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…In the case of severe DDoS attacks, where immediate action is required, Ryu can instruct switches to implement blackhole routing. This involves directing all the traffic destined for the attacked resource to a null route or a blackhole, effectively discarding the malicious traffic and preventing it from reaching the target [22].…”

Section: Real-time Ddos Detectionmentioning

confidence: 99%

Machine learning based detection of DDoS attacks in software defined network

Kannan,

Muthusamy,

Srinivasan

et al. 2023

IJEECS

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<span>Nowadays, software defined networking (SDN) offers benefits in the area so fautomation, elasticity, and resource consumption. However, evidenceis there that SDN controller may undergo certain defeat for the network structure, particularly as the yare targeted by attacks like denial of service (DoS). Due to this network traffic has increased tremendously and attacked the server severely. To handle this issue, weused the Ryu controller and Mininet tool to identify and all eviate the DoS attack by the machine learning (ML) algorithm. Since ML is deemed as themain method for detecting peculiarities, the detection of DoS attacks was done through ML based classification. In this paper, several ML techniques were used to identify the DoS attack, and the traffic which is causing the attack has been dropped immediately to avoid congestion. The proposed work hasbeen simulated in Mininet and the results show that the proposed work detects DoS attacks well and achieves good accuracy.</span>

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Section: Real-time Ddos Detectionmentioning

confidence: 99%

Machine learning based detection of DDoS attacks in software defined network

Kannan,

Muthusamy,

Srinivasan

et al. 2023

IJEECS

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“…An ML-based intelligent model is presented in [15], which uses the length of service and rules in the classification [16]. A network-oriented defensive approach for SDN is presented in [17], which detects HTTP attacks.…”

Section: Related Workmentioning

confidence: 99%

RMCARTAM For DDoS Attack Mitigation in SDN Using Machine Learning

Revathi¹,

Ramalingam²,

Amutha³

2023

Computer Systems Science and Engineering

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The impact of a Distributed Denial of Service (DDoS) attack on Software Defined Networks (SDN) is briefly analyzed. Many approaches to detecting DDoS attacks exist, varying on the feature being considered and the method used. Still, the methods have a deficiency in the performance of detecting DDoS attacks and mitigating them. To improve the performance of SDN, an efficient Real-time Multi-Constrained Adaptive Replication and Traffic Approximation Model (RMCARTAM) is sketched in this article. The RMCARTAM considers different parameters or constraints in running different controllers responsible for handling incoming packets. The model is designed with multiple controllers to handle network traffic but can turn the controllers according to requirements. The multi-constraint adaptive replication model monitors different features of network traffic like rate of packet reception, class-based packet reception and target-specific reception. According to these features, the method estimates the Replication Turning Weight (RTW) based on which triggering controllers are performed. Similarly, the method applies Traffic Approximation (TA) in the detection of DDoS attacks. The detection of a DDoS attack is performed by approximating the incoming traffic to any service and using various features like hop count, payload, service frequency, and malformed frequency to compute various support measures on bandwidth access, data support, frequency support, malformed support, route support, and so on. Using all these support measures, the method computes the value of legitimate weight to conclude the behavior of any source in identifying the malicious node. Identified node details are used in the mitigation of DDoS attacks. The method stimulates the network performance by reducing the power factor by switching the controller according to different factors, which also reduces the cost. In the same way, the proposed model improves the accuracy of detecting DDoS attacks by estimating the features of incoming traffic in different corners.

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“…Common types of attacks, such as denial of service (DoS) and network scanning, were detected in this study. Mykhailo et al [36] employed machine learning techniques to detect DDoS attacks in the SDN. By comparing the average duration of each session with the amount of time required to access the server from a specific IP address, they were able to identify anomalies.…”

Section: Related Workmentioning

confidence: 99%

Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response

Shoaib,

Chow,

Vlahu-Gjorgievska

et al. 2023

Telecom

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Software-defined networking (SDN) is an innovative technology that has the potential to enhance the scalability, flexibility, and security of telecommunications networks. The emergence and development of SDNs have introduced new opportunities and challenges in the telecommunications industry. One of the major challenges encountered by SDNs is the timing side-channel attacks. These attacks exploit timing information to expose sensitive data, including flow tables, routes, controller types, and ports, which pose a significant threat to communication networks. Existing techniques for mitigating timing side-channel attacks primarily focus on limiting them via network architectural changes. This significantly increases the overhead of SDNs and makes it difficult to identify the origin of the attack. To secure resilient integration of SDN in telecommunications networks, it is necessary to conduct comprehensive research that not only identifies the attack activity, but also formulates an adequate response. In this paper, we propose a detection and response solution for timing side-channel attacks in SDN. We used a machine learning-based approach to detect the probing activity and identify the source. To address the identified timing side-channel attack queries, we propose a response mechanism. This entails devising a feedback-oriented response to counter the identified source, such as blocking or diverting it, while minimising any adverse effects on legitimate network traffic. This methodology is characterised by an automated data-driven approach that enables prompt and effective responses. The architecture of this security solution ensures that it has a minimal impact on network traffic and resource usage as it is designed to be used in conjunction with SDN. The overall design findings show that our detection approach is 94% precise in identifying timing side-channel attacks in SDN when compared with traditional mitigation strategies. Additionally, the response mechanism employed by this approach yielded highly customised and precise responses, resulting in an impressive accuracy score of 97.6%.

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Concept of Intelligent Detection of DDoS Attacks in SDN Networks Using Machine Learning

Cited by 11 publications

References 11 publications

Machine learning based detection of DDoS attacks in software defined network

Machine learning based detection of DDoS attacks in software defined network

RMCARTAM For DDoS Attack Mitigation in SDN Using Machine Learning

Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response

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