國立虎尾科技大學 |

A Machine Learning Approach for Enhancing Security and Quality of Service of Optical Burst Switching Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A Machine Learning Approach for Enhancing Security and Quality of Service of Optical Burst Switching Networks./
作者:	Rajab, Adel Dabash A.
面頁冊數:	1 online resource (132 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355666304

A Machine Learning Approach for Enhancing Security and Quality of Service of Optical Burst Switching Networks.
Rajab, Adel Dabash A.

A Machine Learning Approach for Enhancing Security and Quality of Service of Optical Burst Switching Networks. - 1 online resource (132 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--University of South Carolina, 2017.

Includes bibliographical references

The Optical Bust Switching (OBS) network has become the most promising switching technology 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) flood 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, a high burst loss rate, and eventually, 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 flood 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 and final part of this dissertation, we performed a comparative study, evaluating a number of ML techniques in classifying edge nodes, to determine the most suitable method to prevent this type of attack. The experimental results from a processed dataset related to BHP flood attacks showed that rule based classifiers, in particular decision trees (C4.5), Bagging, and RIDOR, consistently derive high predictive classifiers, compared to alternate ML algorithms, including AdaBoost, Logistic Regression, Naive 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, this is the most appropriate technique for classifying ingress nodes to combat the BHP flood attack problem.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355666304Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

A Machine Learning Approach for Enhancing Security and Quality of Service of Optical Burst Switching Networks.
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502 $a Thesis (Ph.D.)--University of South Carolina, 2017.
504 $a Includes bibliographical references
520 $a The Optical Bust Switching (OBS) network has become the most promising switching technology 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) flood 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, a high burst loss rate, and eventually, 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 flood 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 and final part of this dissertation, we performed a comparative study, evaluating a number of ML techniques in classifying edge nodes, to determine the most suitable method to prevent this type of attack. The experimental results from a processed dataset related to BHP flood attacks showed that rule based classifiers, in particular decision trees (C4.5), Bagging, and RIDOR, consistently derive high predictive classifiers, compared to alternate ML algorithms, including AdaBoost, Logistic Regression, Naive 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, this is the most appropriate technique for classifying ingress nodes to combat the BHP flood attack problem.
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538 $a Mode of access: World Wide Web
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