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

中文翻译：

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一种高效有效的光突发交换网络中洪泛攻击检测方法

攻击者经常会破坏光突发交换（OBS）网络，攻击者可以利用突发头数据包（BHP）泛洪网络，从而导致拒绝服务（DoS）攻击，也称为BHP泛洪攻击。如今，一组机器学习（ML）方法已嵌入到OBS核心交换机中，以检测这些BHP泛洪攻击。但是，由于BHP数据的冗余功能和OBS核心交换机的功能有限，现有技术仍需要进行重大改进才能有效地工作。在本文中，提出了一种有效的基于ML的安全方法来检测BHP泛洪攻击。所提出的方法包括特征选择阶段和分类阶段。特征选择阶段使用信息增益（IG）方法来选择最重要的特征，提高检测效率。对于分类阶段，决策树（DT）分类器用于根据BHP的选定特征构建模型，从而减少了过拟合问题并提高了检测准确性。使用10倍交叉验证和保留技术对OBS网络的公共数据集进行了一组实验。实验结果表明，所提出的方法仅使用三个特征就可以实现100％的最高分类精度。使用10倍交叉验证和保留技术对OBS网络的公共数据集进行了一组实验。实验结果表明，所提出的方法仅使用三个特征就可以实现100％的最高分类精度。使用10倍交叉验证和保留技术对OBS网络的公共数据集进行了一组实验。实验结果表明，所提出的方法仅使用三个功能就可以实现100％的最高分类精度。