Decision Forest Classifier with Flower Search Optimization Algorithm for Efficient Detection of BHP Flooding Attacks in Optical Burst Switching Network

摘要: This research is focused on the efficient classification of BHP flooding attacks in Optical switching network environment. The burst backbone future generation optical network. header packet poses a key security challenge that may have negative impact its resource utilization performance and some cases create issues like denial service (DoS). A possible solution to this develop techniques with optimized features from data, so misbehaving edge notes be detected at an early stage remedial action taken as counter measures protect investigates feature selection by using novel flower Pollination optimization algorithm (FPA) then implementation Decision Forest Penalizing Attributes (Forest PA) classifier for detection attacks. comparison proposed approach other existing approaches terms various metrics such as: Accuracy, precision, recall, sensitivity, specificity Informedness are presented understand suitability.