Detection of distributed denial of service attacks using machine learning algorithms in software defined networks

Meti, Nisharani; Narayan, D. G.; Baligar, Vishwanath P.

doi:10.1109/icacci.2017.8126031

Cited by 79 publications

(38 citation statements)

References 7 publications

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“…varied in different researches. Meti et al (2017) conducted research to test the accuracy of machine learning algorithms like Naive Bayes, support vector machine and neural network. In that research, they identified that both ANN and SVM provide superior accuracy (approx.…”

Section: Discussionmentioning

confidence: 99%

AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

Al-Naeem¹,

Rahman²,

Ibrahim³

et al. 2020

Journal of Computer Science

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Section: Discussionmentioning

confidence: 99%

AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

Al-Naeem¹,

Rahman²,

Ibrahim³

et al. 2020

Journal of Computer Science

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“…Meti et al [37] proposed using SVM and Neural Networks (NN) as classifiers for intrusion detection and DDoS attacks in SDN. The approach showed promising results in detecting regular DDoS attacks for NN with 80% accuracy and 100% precision.…”

Section: Related Workmentioning

confidence: 99%

A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning

et al. 2020

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While there have been extensive studies of denial of service (DoS) attacks and DDoS attack mitigation, such attacks remain challenging to mitigate. For example, Low-Rate DDoS (LR-DDoS) attacks are known to be difficult to detect, particularly in a software-defined network (SDN). Hence, in this paper we present a flexible modular architecture that allows the identification and mitigation of LR-DDoS attacks in SDN settings. Specifically, we train the intrusion detection system (IDS) in our architecture using six machine learning (ML) models (i.e., J48, Random Tree, REP Tree, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM)) and evaluate their performance using the Canadian Institute of Cybersecurity (CIC) DoS dataset. The findings from the evaluation demonstrate that our approach achieves a detection rate of 95%, despite the difficulty in detecting LR-DoS attacks. We also remark that in our deployment, we use the open network operating system (ONOS) controller running on Mininet virtual machine in order for our simulated environment to be as close to real-world production networks as possible. In our testing topology, the intrusion prevention detection system mitigates all attacks previously detected by the IDS system. This demonstrates the utility of our architecture in identifying and mitigating LR-DDoS attacks. INDEX TERMS DDoS attack mitigation, Low-rate DDoS (LR-DDoS) attacks, Machine learning, Software-defined network (SDN).

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“…We have used our own simulated data and regularly update the training data set, before applying on the real test data. ML algorithms like SVM and ANN are used by Meti et al in to predict DDoS attack in SDN and got an accuracy of 80% for SVM. But we have used SVM, KNN, and Naive Bayes algorithm.…”

Section: Related Workmentioning

confidence: 99%

Fog‐SDN: A light mitigation scheme for DDoS attack in fog computing framework

Priyadarshini

Barik

Dubey

2020

Int J Communication

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Cloud computing is one of the most tempting technologies in today's computing scenario as it provides a cost-efficient solutions by reducing the large upfront cost for buying hardware infrastructures and computing power. Fog computing is an added support to cloud environment by leveraging with doing some of the less compute intensive task to be done at the edge devices, which reduces the response time for end user computing. But the vulnerabilities to these systems are still a big concern. Among several security needs, availability is one that makes the demanded services available to the targeted customers all the time. Availability is often challenged by external attacks like Denial of service (DoS) and distributed denial of service (DDoS). This paper demonstrates a novel source-based DDoS mitigating schemes that could be employed in both fog and cloud computing scenarios to eliminate these attacks. It deploys the DDoS defender module which works on a machine learning-based light detection method, present at the SDN controller. This scheme uses the network traffic data to analyze, predict, and filter incoming data, so that it can send the filtered legitimate packets to the server and blocking the rest. KEYWORDS fog computing, K nearest neighborhood, machine learning, Security, software-defined network, support vector machine Int J Commun Syst. 2020;33:e4389.wileyonlinelibrary.com/journal/dac

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Detection of distributed denial of service attacks using machine learning algorithms in software defined networks

Cited by 79 publications

References 7 publications

AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning

Fog‐SDN: A light mitigation scheme for DDoS attack in fog computing framework

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