An Intelligent Software defined Network Controller for preventing Distributed Denial of Service Attack

Prakash, Amit; Priyadarshini, Rojalina

doi:10.1109/icicct.2018.8473340

Cited by 31 publications

(18 citation statements)

References 10 publications

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“…In [6], the authors used KNN, SVM, and Naïve Bayes to detect DDoS packets. KNN was most suitable with 97% accuracy, while SVM had 82% and Naïve Bayes 83%.…”

Section: Related Workmentioning

confidence: 99%

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An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

et al. 2021

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Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

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“…In [6], the authors used KNN, SVM, and Naïve Bayes to detect DDoS packets. KNN was most suitable with 97% accuracy, while SVM had 82% and Naïve Bayes 83%.…”

Section: Related Workmentioning

confidence: 99%

“…Table 5, shows a comparison of the classification models from this research and other related works. SVM (DDoS, SDN) 82% [6] 86.85% 3.…”

Section: Comparison Of the Lstm Model With The Best Performing Linearmentioning

confidence: 99%

An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

et al. 2021

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“…In 2018, Prakash et al used the Mininet tool to build a topology from four virtual hosts and two virtual switches in order to generate a dataset for ML classification purposes [3]. TCPDump tool is used to collect the network traffic, while the Hping3 tool is utilized to simulate DDoS attacks.…”

Section: B Reviewer-2: Comparison Of Existing Testbeds With Proposedmentioning

confidence: 99%

“…Although some previous efforts [3]- [8] have been tried to simulate the SDN network and generate an acceptable dataset, the existing datasets only outline a few types of attacks i.e. only focus on DoS/DDoS threats without considering the different attack classes existing in the SDN network.…”

Section: Introductionmentioning

confidence: 99%

InSDN: A Novel SDN Intrusion Dataset

2020

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Software-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center's network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion Detection Systems (IDSs) to monitor malicious activities is a crucial part of the network architecture. Although the centralized view of the SDN network creates new opportunities for the implementation of IDSs, the performance of these detection techniques relies on the quality of the training datasets. Unfortunately, there are no publicly available datasets that can be used directly for anomaly detection systems applied in SDN networks. The majority of the published studies use non-compatible and outdated datasets, such as the KDD'99 dataset. This manuscript aims to generate an attack-specific SDN dataset and it is publicly available to the researchers. To the best of our knowledge, our work is one of the first solutions to produce a comprehensive SDN dataset to verify the performance of intrusion detection systems. The new dataset includes the benign and various attack categories that can occur in the different elements of the SDN platform. Further, we demonstrate the use of our proposed dataset by performing an experimental evaluation using eight popular machine-learning-based techniques for IDSs.

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“…The author used 11 different features in which only 5 were critical. In [14], the author used 6 features and labelled the traffic in the dataset for training. SVM, Naive Bayes, and k-Nearest Neighbours (KNN) have been used for the experiment while KNN had an accuracy of 97%.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Machine Learning Techniques for Security in SDN

Ahmad

Harjula

Ylianttila

et al. 2020

2020 IEEE Globecom Workshops (GC WKSHPS

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Software Defined Networking (SDN) has emerged as the most viable programmable network architecture to solve many challenges in legacy networks. SDN separates the network control plane from the data forwarding plane and logically centralizes the network control plane. The logically centralized control improves network management through global visibility of the network state. However, centralized control opens doors to security challenges. The SDN control platforms became the most attractive venues for Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Due to the success and inevitable benefits of Machine Learning (ML) in fingerprinting security vulnerabilities, this article proposes and evaluates ML techniques to counter DoS and DDoS attacks in SDN. The ML techniques are evaluated in a practical setup where the SDN controller is exposed to DDoS attacks to draw important conclusions for ML-based security of future communication networks.

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An Intelligent Software defined Network Controller for preventing Distributed Denial of Service Attack

Cited by 31 publications

References 10 publications

An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

InSDN: A Novel SDN Intrusion Dataset

Evaluation of Machine Learning Techniques for Security in SDN

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