Burst Header Packet Flood Detection in Optical Burst Switching Network Using Deep Learning Model
The Optical Burst Switching (OBS) network is mostly victimized to the Denial of Service (DOS) attack, referred as Burst Header Packet (BHP) flooding attack can prevent reasonable traffics from keeping the necessary resources at transitional core nodes. The attack scenario is to flood the malicious BHP without acknowledging Data Bursts (DB) which can affect low bandwidth utilization, degrade network performance, high data loss rate and ultimately DOS. Therefore, machine predicted analysis has become very promising in recent decades that can effectually identify the attack in the optical switching network. However, due to a very small number of samples of the datasets, traditional machine learning approaches such as Naïve Bayes, K-Nearest Neighbour’s (KNN) and Support Vector Machine (SVM) cannot analyse the data efficiently. In this regard, we intend a Deep Convolution Neural Network (DCNN) model to automatically detect the edge nodes at an early stage. Finally, presented that proposed deep model is working enhanced rather than any other traditional model (e.g. Naïve Bayes, SVM and KNN).
Machine learning, Classification, Burst Header Packet (BHP), Deep Learning, Data Mining, Denial of Service (DOS) attack