A Machine Learning Approach For Enhancing Security And Quality Of Service Of Optical Burst Switching Networks
Date of Award
Open Access Dissertation
Computer Science and Engineering
College of Engineering and Computing
The Optical Bust Switching (OBS) network has become one of the most promising switching technologies for building the next-generation of internet backbone infrastructure. However, OBS networks still face a number of security and Quality of Service (QoS) challenges, particularly from Burst Header Packet (BHP) flooding attacks. In OBS, a core switch handles requests, reserving one of the unoccupied channels for incoming data bursts (DB) through BHP. An attacker can exploit this fact and send malicious BHP without the corresponding DB. If unresolved, threats such as BHP flooding attacks can result in low bandwidth utilization, limited network performance, high burst loss rate, and eventually, denial of service (DoS). In this dissertation, we focus our investigations on the network security and QoS in the presence of BHP flooding attacks. First, we proposed and developed a new security model that can be embedded into OBS core switch architecture to prevent BHP flooding attacks. The countermeasure security model allows the OBS core switch to classify the ingress nodes based on their behavior and the amount of reserved resources not being utilized. A malicious node causing a BHP flooding attack will be blocked by the developed model until the risk disappears or the malicious node redeems itself. Using our security model, we can effectively and preemptively prevent a BHP flooding attack regardless of the strength of the attacker. In the second part of this dissertation, we investigated the potential use of machine learning (ML) in countering the risk of the BHP flood attack problem. In particular, we proposed and developed a new series of rules, using the decision tree method to prevent the risk of a BHP flooding attack. The proposed classification rule models were evaluated using different metrics to measure the overall performance of this approach. The experiments showed that using rules derived from the decision trees did indeed counter BHP flooding attacks, and enabled the automatic classification of edge nodes at an early stage. In the third part of this dissertation, we performed a comparative study, evaluating a number of ML techniques in classifying edge nodes, to determine the most suitable ML method to prevent this type of attack. The experimental results from a preprocessed dataset related to BHP flooding attacks showed that rule-based classifiers, in particular decision trees (C4.5), Bagging, and RIDOR, consistently derive classifiers that are more predictive, compared to alternate ML algorithms, including AdaBoost, Logistic Regression, Naïve Bayes, SVM-SMO and ANN-MultilayerPerceptron. Moreover, the harmonic mean, recall and precision results of the rule-based and tree classifiers were more competitive than those of the remaining ML algorithms. Lastly, the runtime results in ms showed that decision tree classifiers are not only more predictive, but are also more efficient than other algorithms. Thus, our findings show that decision tree identifier is the most appropriate technique for classifying ingress nodes to combat the BHP flooding attack problem.
Rajab, A. A.(2017). A Machine Learning Approach For Enhancing Security And Quality Of Service Of Optical Burst Switching Networks. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/4402