Abstract
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.
Highlights
IntroductionIn a network with burst traffic, Optical burst switching (OBS) plays an essential role for packet switching with a higher level of necessary details than other existing networks’ switching techniques
Optical burst switching (OBS) in networks has become an important dynamic sub-wavelength switching technique and a solution for developing the new type of Internet backbone infrastructure [1]. e OBS network mainly consists of three types of nodes, namely, core nodes, ingress, and egress. e core nodes represent the intermediate nodes, which are designed to reduce the processing and buffering of the optical data burst using a control data packet with specific information, namely, burst header packets (BHPs) [2].In a network with burst traffic, OBS plays an essential role for packet switching with a higher level of necessary details than other existing networks’ switching techniques
This type of switching is still suffering from several challenges such as security and quality of service (QoS) due to BHP flooding attacks. e function of BHP in OBS is to reserve the unused channel for the arrival of a data burst (DB). is function can be exploited by attackers to send fake BHPs without DB acknowledgment
Summary
In a network with burst traffic, OBS plays an essential role for packet switching with a higher level of necessary details than other existing networks’ switching techniques. This type of switching is still suffering from several challenges such as security and quality of service (QoS) due to BHP flooding attacks. Is function can be exploited by attackers to send fake BHPs without DB acknowledgment Such fake BHPs can affect the network and reduce its performance through decreasing bandwidth utilization and increasing data loss, leading to a denial of service (DoS) attack [3], which is one of the most crucial security threats to networks. Due to the limited capability of OBS core switches, developing a lightweight method that can attain high accuracy with a small number of features is still a challenging issue for developers and researchers
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