Detection of Burst Header Packet Flooding Attacks via Optimization based Deep Learning Framework in Optical Burst Switching Network

Ramkumar Vahalingam, Bhavani Rajagopal, Sathishkumar Arumugam, Muneeswari Ganesa pandian


Optical Burst Switching (OBS) technique has the greatest potential for securing future Internet connections. In real-time applications, OBS adoption is motivated by the lack of Quality of Service (QoS) in OBS networks. The accuracy of existing methods for detecting the misbehaving nodes that cause Burst Heading Packet (BHP) flooding attacks is typically poor. To overcome these issues, a novel Elephant Herd Algorithm-based Deep Learning (EHA-DL) network has been proposed for detecting BHP flooding attacks. The proposed approach is divided into three phases: pre-processing, feature selection, and classification. The Elephant Herd Algorithm (EHA) is used to select the most crucial features after pre-processing the raw data to increase the effectiveness of the model. To decrease overfitting and increase detection accuracy, a MobileNet is used to construct the model for the classification phase using the select features of BHPs. The performance of the experimental outcomes was assessed using evaluation metrics like accuracy, specificity, recall, and f-measure. The EHA-DL approach method yielded a 99.27% accuracy rate, which was comparatively high when compared to other approaches. In optical burst switching networks, the method effectively and highly efficiently detects flooding assaults and maintains network stability.

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