Abstract
An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security.
Highlights
We propose a Burst Header Packet (BHP) flooding attacks detection method based on support vector machine (SVM), and use particle swarm optimization (PSO) algorithm to find the optimal parameters of SVM
We perform a number of simulations to collect a relevant credible dataset, so that construct an effective classification model to block the BHP flooding attack
We propose an recursive feature elimination (RFE)-particle swarm optimization–support vector machine (PSO-SVM) model for intrusion detection in Optical Burst Switching (OBS)
Summary
A series of methods have been utilized to deal with and detect intrusion problems in OBS networks. Many artificial intelligence algorithms were applied to predict similar attack types, greatly improving the efficiency of flooding detection [12,13]. Ibrahim [14] used the layered off-line abnormal network in the distributed delay artificial neural network to detect the attack of the intrusion network, which solved the problem of attack type detection of the dynamic neural network well, and the classification accuracy reached 97.24%. A deep learning model was used to classify flooding attack types of OBS and improved the classification accuracy of the flooding attack to 99% [15]
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