Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks

Optical burst switching networks are vulnerable to various threats including Burst Header Packet Flooding attack, Circulating Burst Header attack, Address Spoofing, and Replay attack. Therefore, detecting such threats play a key role in taking appropriate security measures. One of the major challenges in identifying the risks of Burst Header Packet flooding attacks is the lack or insufficiency of reliable historical data. In this paper, firstly, Burst Header Packet flooding attacks are classified into four categories, Misbehaving-Block, Behaving-No Block, Misbehaving-No Block and Misbehaving-Wait, using Naive Bayes and K-Nearest Neighbor algorithms. Using performance metrics obtained both after testing on the same set and after applying 10-fold cross validation, the performance of Naive Bayes and K-Nearest Neighbor algorithms is compared based on commonly used performance metrics. As the results show, compared to Naive Bayes, K-Nearest Neighbor algorithm is more suitable for predicting Burst Header Packet Flooding attacks.

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