The Experts below are selected from a list of 1518 Experts worldwide ranked by ideXlab platform
Chun-ying Huang - One of the best experts on this subject based on the ideXlab platform.
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Session Level Flow Classification by Packet Size Distribution and Session Grouping
2012 26th International Conference on Advanced Information Networking and Applications Workshops, 2012Co-Authors: Chun-nan Lu, Chun-ying HuangAbstract:Classifying traffic into specific network applications is essential for application-aware network management and it becomes more challenging because modern applications obscure their network behaviors. While port number-based classifiers work only for some well-known applications and signature-based classifiers are not applicable to encrypted packet payloads, researchers tend to classify network traffic based on behaviors observed in network applications. In this paper, a session level Flow Classification (SLFC) approach is proposed to classify network Flows as a session, which comprises of Flows in the same conversation. SLFC first classifies Flows into the corresponding applications by packet size distribution (PSD) and then group Flows as sessions by port locality. With PSD, each Flow is transformed into a set of points in a two-dimension space and the distances between each Flow and the representatives of pre-selected applications are computed. The Flow is recognized as the application having a minimum distance. Meanwhile, port locality is used to group Flows as sessions because an application often uses consecutive port numbers within a session. If Flows of a session are classified into different applications, an arbitration algorithm is invoked to make the correction. The evaluation shows that SLFC achieves high accuracy rates on Flow session Classifications, say 99.9%. When SLFC is applied to online Classification, an average of 72% of packets in long-lasting Flows can be skipped without reducing the Classification accuracy rates.
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AINA Workshops - Session Level Flow Classification by Packet Size Distribution and Session Grouping
2012 26th International Conference on Advanced Information Networking and Applications Workshops, 2012Co-Authors: Chun-nan Lu, Chun-ying HuangAbstract:Classifying traffic into specific network applications is essential for application-aware network management and it becomes more challenging because modern applications obscure their network behaviors. While port number-based classifiers work only for some well-known applications and signature-based classifiers are not applicable to encrypted packet payloads, researchers tend to classify network traffic based on behaviors observed in network applications. In this paper, a session level Flow Classification (SLFC) approach is proposed to classify network Flows as a session, which comprises of Flows in the same conversation. SLFC first classifies Flows into the corresponding applications by packet size distribution (PSD) and then group Flows as sessions by port locality. With PSD, each Flow is transformed into a set of points in a two-dimension space and the distances between each Flow and the representatives of pre-selected applications are computed. The Flow is recognized as the application having a minimum distance. Meanwhile, port locality is used to group Flows as sessions because an application often uses consecutive port numbers within a session. If Flows of a session are classified into different applications, an arbitration algorithm is invoked to make the correction. The evaluation shows that SLFC achieves high accuracy rates on Flow session Classifications, say 99.9%. When SLFC is applied to online Classification, an average of 72% of packets in long-lasting Flows can be skipped without reducing the Classification accuracy rates.
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session level Flow Classification by packet size distribution and session grouping
Computer Networks, 2012Co-Authors: Chun-nan Lu, Chun-ying HuangAbstract:Classifying traffic into specific network applications is essential for application-aware network management and it becomes more challenging because modern applications complicate their network behaviors. While port number-based classifiers work only for some well-known applications and signature-based classifiers are not applicable to encrypted packet payloads, researchers tend to classify network traffic based on behaviors observed in network applications. In this paper, a session level Flow Classification (SLFC) approach is proposed to classify network Flows as a session, which comprises of Flows in the same conversation. SLFC first classifies Flows into the corresponding applications by packet size distribution (PSD) and then groups Flows as sessions by port locality. With PSD, each Flow is transformed into a set of points in a two-dimension space and the distances between each Flow and the representatives of pre-selected applications are computed. The Flow is recognized as the application having a minimum distance. Meanwhile, port locality is used to group Flows as sessions because an application often uses consecutive port numbers within a session. If Flows of a session are classified into different applications, an arbitration algorithm is invoked to make the correction. The evaluation shows that SLFC achieves high accuracy rates on both Flow and session Classifications, say 99.9% and 99.98%, respectively. When SLFC is applied to online Classification, it is able to make decisions quickly by checking at most 300 packets for long-lasting Flows. Based on our test data, an average of 72% of packets in long-lasting Flows can be skipped without reducing the Classification accuracy rates.
Akihiro Nakao - One of the best experts on this subject based on the ideXlab platform.
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Packet cache network function for peer-to-peer traffic management with Bloom-filter based Flow Classification
2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), 2016Co-Authors: Kengo Sasaki, Akihiro NakaoAbstract:Following the emergence of peer-to-peer (P2P) applications, millions of computer users have used P2P systems to search for desired content. P2P traffic is known to be highly redundant because of its inherent self-scaling characteristics, which means that file sharing is performed more efficiently when more users exchange the same content. To remove redundant P2P traffic, we have proposed a method to control the P2P traffic through a packet-level data cache that acts as a network function at the edge of the Internet service provider (ISP) networks [1]. However, our previous method involves high levels of memory consumption. Software-defined networking (SON) and network functions virtualization (NFV) are representative trends in network soft-warization that may lower the barrier to deployment of network management functions that are considered to be useful but are difficult to actually implement and deploy. In this paper, we propose a new Flow Classification for P2P that uses a queue Bloom filter (QBF) to reduce the memory consumption of the P2P cache. The QBF is a time series queue that manages Bloom filters and it can remove inserted Bloom filter elements without generating false positives. If the router can confirm that P2P Flows are carrying duplicate contents using QBF, it then begins to cache the duplicate content. Our analysis shows that the proposed method reduces memory consumption to 67% and improves the P2P cache hit ratio by 4% when compared with the previous approach, while its performance in removing redundancy from the P2P traffic is degraded by only 14% . In addition, we discuss the implementation and deployment of the proposed system at the edge routers of ISP networks by applying SON and NFV.
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Rethinking Flow Classification in SDN
2014 IEEE International Conference on Cloud Engineering, 2014Co-Authors: Hamid Farhadi, Akihiro NakaoAbstract:Software-Defined Networking (SDN) increasingly attracts more researchers as well as industry attentions. Most of current SDN packet processing approaches classify packets based on matching a set of fields on the packet against a Flow table and then applying an action on the packet. We argue we can simplify this mechanism using single-field Classification and reduce the overhead. We propose a tag-based packet Classification architecture to reduce filtering and Flow management overhead. Then, we show how to use this extra capacity to perform application layer Classification for different purposes. In this work-in-progress paper we demonstrate our preliminary evaluation results to indicate the effectiveness of the proposal.
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IC2E - Rethinking Flow Classification in SDN
2014 IEEE International Conference on Cloud Engineering, 2014Co-Authors: Hamid Farhadi, Akihiro NakaoAbstract:Software-Defined Networking (SDN) increasingly attracts more researchers as well as industry attentions. Most of current SDN packet processing approaches classify packets based on matching a set of fields on the packet against a Flow table and then applying an action on the packet. We argue we can simplify this mechanism using single-field Classification and reduce the overhead. We propose a tag-based packet Classification architecture to reduce filtering and Flow management overhead. Then, we show how to use this extra capacity to perform application layer Classification for different purposes. In this work-in-progress paper we demonstrate our preliminary evaluation results to indicate the effectiveness of the proposal.
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Application layer Flow Classification in SDN
2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS), 2013Co-Authors: Hamid Farhadi, Akihiro NakaoAbstract:Software Defined Networking (SDN) increasingly attracts more researchers as well as industry attentions. Most of current SDN packet processing approaches classify packets based on matching a set of fields on the packet. We propose a tag-based packet Classification architecture to reduce filtering and Flow management overhead.
Chun-nan Lu - One of the best experts on this subject based on the ideXlab platform.
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Session Level Flow Classification by Packet Size Distribution and Session Grouping
2012 26th International Conference on Advanced Information Networking and Applications Workshops, 2012Co-Authors: Chun-nan Lu, Chun-ying HuangAbstract:Classifying traffic into specific network applications is essential for application-aware network management and it becomes more challenging because modern applications obscure their network behaviors. While port number-based classifiers work only for some well-known applications and signature-based classifiers are not applicable to encrypted packet payloads, researchers tend to classify network traffic based on behaviors observed in network applications. In this paper, a session level Flow Classification (SLFC) approach is proposed to classify network Flows as a session, which comprises of Flows in the same conversation. SLFC first classifies Flows into the corresponding applications by packet size distribution (PSD) and then group Flows as sessions by port locality. With PSD, each Flow is transformed into a set of points in a two-dimension space and the distances between each Flow and the representatives of pre-selected applications are computed. The Flow is recognized as the application having a minimum distance. Meanwhile, port locality is used to group Flows as sessions because an application often uses consecutive port numbers within a session. If Flows of a session are classified into different applications, an arbitration algorithm is invoked to make the correction. The evaluation shows that SLFC achieves high accuracy rates on Flow session Classifications, say 99.9%. When SLFC is applied to online Classification, an average of 72% of packets in long-lasting Flows can be skipped without reducing the Classification accuracy rates.
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AINA Workshops - Session Level Flow Classification by Packet Size Distribution and Session Grouping
2012 26th International Conference on Advanced Information Networking and Applications Workshops, 2012Co-Authors: Chun-nan Lu, Chun-ying HuangAbstract:Classifying traffic into specific network applications is essential for application-aware network management and it becomes more challenging because modern applications obscure their network behaviors. While port number-based classifiers work only for some well-known applications and signature-based classifiers are not applicable to encrypted packet payloads, researchers tend to classify network traffic based on behaviors observed in network applications. In this paper, a session level Flow Classification (SLFC) approach is proposed to classify network Flows as a session, which comprises of Flows in the same conversation. SLFC first classifies Flows into the corresponding applications by packet size distribution (PSD) and then group Flows as sessions by port locality. With PSD, each Flow is transformed into a set of points in a two-dimension space and the distances between each Flow and the representatives of pre-selected applications are computed. The Flow is recognized as the application having a minimum distance. Meanwhile, port locality is used to group Flows as sessions because an application often uses consecutive port numbers within a session. If Flows of a session are classified into different applications, an arbitration algorithm is invoked to make the correction. The evaluation shows that SLFC achieves high accuracy rates on Flow session Classifications, say 99.9%. When SLFC is applied to online Classification, an average of 72% of packets in long-lasting Flows can be skipped without reducing the Classification accuracy rates.
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session level Flow Classification by packet size distribution and session grouping
Computer Networks, 2012Co-Authors: Chun-nan Lu, Chun-ying HuangAbstract:Classifying traffic into specific network applications is essential for application-aware network management and it becomes more challenging because modern applications complicate their network behaviors. While port number-based classifiers work only for some well-known applications and signature-based classifiers are not applicable to encrypted packet payloads, researchers tend to classify network traffic based on behaviors observed in network applications. In this paper, a session level Flow Classification (SLFC) approach is proposed to classify network Flows as a session, which comprises of Flows in the same conversation. SLFC first classifies Flows into the corresponding applications by packet size distribution (PSD) and then groups Flows as sessions by port locality. With PSD, each Flow is transformed into a set of points in a two-dimension space and the distances between each Flow and the representatives of pre-selected applications are computed. The Flow is recognized as the application having a minimum distance. Meanwhile, port locality is used to group Flows as sessions because an application often uses consecutive port numbers within a session. If Flows of a session are classified into different applications, an arbitration algorithm is invoked to make the correction. The evaluation shows that SLFC achieves high accuracy rates on both Flow and session Classifications, say 99.9% and 99.98%, respectively. When SLFC is applied to online Classification, it is able to make decisions quickly by checking at most 300 packets for long-lasting Flows. Based on our test data, an average of 72% of packets in long-lasting Flows can be skipped without reducing the Classification accuracy rates.
Bernd Freisleben - One of the best experts on this subject based on the ideXlab platform.
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LCN - Unsupervised Traffic Flow Classification Using a Neural Autoencoder
2017 IEEE 42nd Conference on Local Computer Networks (LCN), 2017Co-Authors: Jonas Höchst, Lars Baumgärtner, Matthias Hollick, Bernd FreislebenAbstract:To cope with the varying delay and bandwidth requirements of today’s mobile applications, mobile wireless networks can profit from classifying and predicting mobile application traffic. State-of-the-art traffic Classification approaches have various disadvantages: port-based Classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network Flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic Flow Classification using statistical properties of Flows and clustering based on a neural autoencoder. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic Flow Classification independent of known traffic classes. An experimental evaluation on real data captured over a period of four months is presented. The obtained results show that 7 different classes of mobile traffic Flows are detected with an average precision of 80% and an average recall of 75%.
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Unsupervised Traffic Flow Classification Using a Neural Autoencoder
2017 IEEE 42nd Conference on Local Computer Networks (LCN), 2017Co-Authors: Jonas Höchst, Lars Baumgärtner, Matthias Hollick, Bernd FreislebenAbstract:To cope with the varying delay and bandwidth requirements of today's mobile applications, mobile wireless networks can profit from classifying and predicting mobile application traffic. State-of-the-art traffic Classification approaches have various disadvantages: port-based Classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network Flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic Flow Classification using statistical properties of Flows and clustering based on a neural auto encoder. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic Flow Classification independent of known traffic classes. An experimental evaluation on real data captured over a period of four months is presented. The obtained results show that 7 different classes of mobile traffic Flows are detected with an average precision of 80% and an average recall of 75%.
Jonas Höchst - One of the best experts on this subject based on the ideXlab platform.
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LCN - Unsupervised Traffic Flow Classification Using a Neural Autoencoder
2017 IEEE 42nd Conference on Local Computer Networks (LCN), 2017Co-Authors: Jonas Höchst, Lars Baumgärtner, Matthias Hollick, Bernd FreislebenAbstract:To cope with the varying delay and bandwidth requirements of today’s mobile applications, mobile wireless networks can profit from classifying and predicting mobile application traffic. State-of-the-art traffic Classification approaches have various disadvantages: port-based Classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network Flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic Flow Classification using statistical properties of Flows and clustering based on a neural autoencoder. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic Flow Classification independent of known traffic classes. An experimental evaluation on real data captured over a period of four months is presented. The obtained results show that 7 different classes of mobile traffic Flows are detected with an average precision of 80% and an average recall of 75%.
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Unsupervised Traffic Flow Classification Using a Neural Autoencoder
2017 IEEE 42nd Conference on Local Computer Networks (LCN), 2017Co-Authors: Jonas Höchst, Lars Baumgärtner, Matthias Hollick, Bernd FreislebenAbstract:To cope with the varying delay and bandwidth requirements of today's mobile applications, mobile wireless networks can profit from classifying and predicting mobile application traffic. State-of-the-art traffic Classification approaches have various disadvantages: port-based Classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network Flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic Flow Classification using statistical properties of Flows and clustering based on a neural auto encoder. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic Flow Classification independent of known traffic classes. An experimental evaluation on real data captured over a period of four months is presented. The obtained results show that 7 different classes of mobile traffic Flows are detected with an average precision of 80% and an average recall of 75%.