Enhanced probabilistic packet marking traceback mechanism
This thesis introduces two new schemes: Entropy-Minimization Clustering Technique for Probabilistic Packet Marking Scheme [102] and Legitimacy Investigation and Intention-Based Probabilistic Packet Marking Scheme [103] to improve the performance of PPM. The first scheme, Entropy-Minimization Cluster...
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sg-ntu-dr.10356-24642023-03-04T00:42:30Z Enhanced probabilistic packet marking traceback mechanism Tan, Wei Peng Lee Chee Jwai Lee Bu Sung, Francis School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks This thesis introduces two new schemes: Entropy-Minimization Clustering Technique for Probabilistic Packet Marking Scheme [102] and Legitimacy Investigation and Intention-Based Probabilistic Packet Marking Scheme [103] to improve the performance of PPM. The first scheme, Entropy-Minimization Clustering Technique for Probabilistic Packet Marking Scheme is developed to provide a more effective traceback mechanism. The new technique divides the attack traffic into clusters and processes them in parallel. This method of dividing the path reconstruction into smaller clusters significantly reduces the total number of combinations that need to be checked and will in turn minimize the probability of reconstructing a false positive. Our simulation results show that the combination overhead can be reduced by an average of N9 times, where N is the number of clusters. Our new approach has the same advantage as PPM scheme because it is entirely passive and does not generate any probe traffic into the network. In contrast to the previous work, the new technique is much more efficient and effective during path reconstruction under large-scale DDoS attacks. MASTER OF ENGINEERING (SCE) 2008-09-17T09:03:46Z 2008-09-17T09:03:46Z 2006 2006 Thesis Tan, W. P. (2006). Enhanced probabilistic packet marking traceback mechanism. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/2464 10.32657/10356/2464 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks Tan, Wei Peng Enhanced probabilistic packet marking traceback mechanism |
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This thesis introduces two new schemes: Entropy-Minimization Clustering Technique for Probabilistic Packet Marking Scheme [102] and Legitimacy Investigation and Intention-Based Probabilistic Packet Marking Scheme [103] to improve the performance of PPM. The first scheme, Entropy-Minimization Clustering Technique for Probabilistic Packet Marking Scheme is developed to provide a more effective traceback mechanism. The new technique divides the attack traffic into clusters and processes them in parallel. This method of dividing the path reconstruction into smaller clusters significantly reduces the total number of combinations that need to be checked and will in turn minimize the probability of reconstructing a false positive. Our simulation results show that the combination overhead can be reduced by an average of N9 times, where N is the number of clusters. Our new approach has the same advantage as PPM scheme because it is entirely passive and does not generate any probe traffic into the network. In contrast to the previous work, the new technique is much more efficient and effective during path reconstruction under large-scale DDoS attacks. |
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Lee Chee Jwai |
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Lee Chee Jwai Tan, Wei Peng |
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Theses and Dissertations |
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Tan, Wei Peng |
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Tan, Wei Peng |
title |
Enhanced probabilistic packet marking traceback mechanism |
title_short |
Enhanced probabilistic packet marking traceback mechanism |
title_full |
Enhanced probabilistic packet marking traceback mechanism |
title_fullStr |
Enhanced probabilistic packet marking traceback mechanism |
title_full_unstemmed |
Enhanced probabilistic packet marking traceback mechanism |
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enhanced probabilistic packet marking traceback mechanism |
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2008 |
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https://hdl.handle.net/10356/2464 |
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1759854422325723136 |