Optical burst switching (OBS) networks are frequently compromised by attackers who can flood the networks with burst header\npackets (BHPs), causing a denial of service (DoS) attack, also known as a BHP flooding attack. Nowadays, a set of machine\nlearning (ML) methods have been embedded into OBS core switches to detect these BHP flooding attacks. However, due to the\nredundant features of BHP data and the limited capability of OBS core switches, the existing technology still requires major\nimprovements to work effectively and efficiently. In this paper, an efficient and effective ML-based security approach is proposed\nfor detecting BHP flooding attacks. The proposed approach consists of a feature selection phase and a classification phase. The\nfeature selection phase uses the information gain (IG) method to select the most important features, enhancing the efficiency of\ndetection. For the classification phase, a decision tree (DT) classifier is used to build the model based on the selected features of\nBHPs, reducing the overfitting problem and improving the accuracy of detection. A set of experiments are conducted on a public\ndataset of OBS networks using 10-fold cross-validation and holdout techniques. Experimental results show that the proposed\napproach achieved the highest possible classification accuracy of 100% by using only three features.
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