Voting‐based intrusion detection framework for securing software‐defined networks

Abstract

Software-defined networking (SDN) is an emerging paradigm in enterprise networks because of its flexible and cost-effective nature. By decoupling control and data plane, SDN can provide various defense solutions for securing futuristic networks. However, the architectural design and characteristics of SDN attract several severe attacks. Distributed denial of service (DDoS) is considered as a major destructive cyber attack that makes the services of controller unavailable for its legitimate users. In this research article, an intrusion detection framework is proposed to detect DDoS attacks against SDN. The proposed framework relies on voting-based ensemble model for the attack detection. Ensemble model is a combination of multiple machine learning classifiers for prediction of final results. In this research article, we propose and analyze three ensemble models named as Voting-CMN, Voting-RKM, and Voting-CKM particularly to benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD, respectively. For validation of the proposed models, a cross-validation technique is used with the prediction algorithms. The effectiveness of proposed models is evaluated in terms of prominent metrics (accuracy, precision, recall, and F-measure). Experimental results indicate that the proposed models achieve better performance in terms of accuracy as compared with other existing models.

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

“…Ensemble learning has been presented in the existing literature and is superior to single classifier methods in the domain of anomaly detection [13][14][15][16][17]23,35]. Ensemble learning is an approach in which a set of learning models is combined to enhance predictions performance compared to each separate model.…”

“…The experimental analysis based on the NSL-KDD dataset shows an accuracy of 99.1% and can even perform with new emerging DDoS attacks. A voting-based ensemble technique is proposed for DDoS detection in [16]. The authors analyzed three different ensemble methods-Voting-RKM, Voting-CKM, and Voting-CMN-with standard datasets to propose a high-performance ensemble model.…”

Distributed Denial of Service Attack Prediction over Software Defined Networks Using Federated Learning

“…[14] Attacks against software-defined networks that cause a slow denial of service [15] There is a need for flexible and dynamic techniques to secure and grow fog-to-things infrastructure, and the possibility for an SDN-based architecture has been suggested. [16] By dynamically managing its infrastructure and services, SDN offers a viable solution to networking consumers.…”

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

“…A model's poor performance can be compensated for by the strong performance of other models. It is quite applicable in situations where there is some confusion as to which classification techniques are appropriate for a given problem [78]. There are two types of techniques for a voting classifier: hard and soft [79].…”