P2P traffic classification using clustering technology

Tseng, Chuan-Mu; Guo-Tai, Huang; Liu, Tzong-Jye

doi:10.1109/sii.2016.7843994

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…However, the evaluation was performed using only three P2P applications namely BitComment, BitTorrent, and uTorrent. Tseng et al [14] proposed a methodology to classify P2P traffic on the basis of aggregation clustering. A similar traffic flow was aggregated by determining the correlation between clusters through their distance ratio.…”

Section: Related Workmentioning

confidence: 99%

Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach

et al. 2020

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Peer-to-peer (P2P) applications have been popular among users for more than a decade. They consume a lot of network bandwidth, due to the fact that network administrators face several issues such as congestion, security, managing resources, etc. Hence, its accurate classification will allow them to maintain a Quality of Service for various applications. Conventional classification techniques, i.e., port-based and payload-based techniques alone, have proved ineffective in accurately classifying P2P traffic as they possess significant limitations. As new P2P applications keep emerging and existing applications change their communication patterns, a single classification approach may not be sufficient to classify P2P traffic with high accuracy. Therefore, a multi-level P2P traffic classification technique is proposed in this paper, which utilizes the benefits of both heuristic and statistical-based techniques. By analyzing the behavior of various P2P applications, some heuristic rules have been proposed to classify P2P traffic. The traffic which remains unclassified as P2P undergoes further analysis, where statistical-features of traffic are used with the C4.5 decision tree for P2P classification. The proposed technique classifies P2P traffic with high accuracy (i.e., 98.30%), works with both TCP and UDP traffic, and is not affected even if the traffic is encrypted.

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Section: Related Workmentioning

confidence: 99%

Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach

et al. 2020

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“…Jingling Phantom 4 Pro unmanned aerial vehicle and Raspberry Pi 3Model B with thermo-hygrostat sensors and cameras for augmented reality can be used as the devices and the gateways. The process of decision making is as shown in (6)(7)(8) (9). The network data would be gathered from the control plane and the ImP to the IeP as (6) (7).…”

Section: Evaluation In 5g Scenariosmentioning

confidence: 99%

“…The network data would be gathered from the control plane and the ImP to the IeP as (6) (7). The result of decision making would be propagated directly or indirectly from the LDMs on the IeP to the ILMs on the control plane as (8), and then to a set of network elements in multi-level containers on the ImP as (9). The decisions such as prefetching would be executed as (10) (11) (12) for delivering the top-k popularity objects as the local copies for Internet service in the distributed network elements with storage spaces.…”

Section: Evaluation In 5g Scenariosmentioning

confidence: 99%

“…Currently, there are two of the most prevalent techniques for traffic offloading, peer-to-peer (P2P) [7] [8] with multiple hops and device-to-device (D2D) [9] [10] [11] with single hop, to implement large-scale content delivery [12]. To satisfy the requirements for offloading traffic and mitigating network congestion, P2P and D2D are used to exploit the idle resources such as storage space and bandwidth capability from peers and devices based on the collaboration of neighbors.…”

Section: Introductionmentioning

confidence: 99%

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Scalable Intelligence-Enabled Networking with Traffic Engineering in 5G Scenarios for Future Audio-Visual-Tactile Internet

Sheng

2018

IEEE Access

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In order to improve future network performance, this paper proposes scalable intelligence-enabled networking (SIEN) with eliminating traffic redundancy for audio-visual-tactile Internet in 5G scenarios such as enhanced mobile broadband, ultra-reliable and low latency communication, and massive machine-type communication. The SIEN consists of an intelligent management plane (ImP), an intelligence-enabled plane (IeP), a control plane and a user plane. For the ImP, the containers with decision execution are constructed by a novel graph algorithm to organize objects such as network elements and resource partitions. For the IeP, a novel learning system is designed with decision making using a congruity function for generalization and personalization in the presence of imbalanced, conflicting and partial data. For the control plane, a scheme of identifier-locator mapping is designed by referring to information-centric networking and software-defined networking. For the user plane, the registrations, requests and data are forwarded to implement the SIEN and test its performance. The evaluation shows the SIEN outperforms four state-of-the-art techniques for redundant traffic reduction by up to 46.04% based on a mix of assumption, simulation and proof-of-concept implementation for audiovisual-tactile Internet multimedia service. To confirm the validity, the best case and the worst case for traffic offloading are tested with the data rate, the latency and the density. The evaluation only focused on the scalability issue, while the SIEN would be beneficial to improve more issues such as inter-domain security, ultra-low latency, on-demand mobility, multihoming routing, and cross-layer feature incongruity.

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“…For instance, they are the precursors to intrusion detection systems, which use anomaly detection [1], [2], [4]. Furthermore, traffic classification is also applied in network security applications, network management, traffic analysis [34] and quality of service enhancement in networks [3], [29]. The traditional method of classifying traffic includes the Port based approach and the Payload method.…”

Section: Introductionmentioning

confidence: 99%

Clustering Techniques for Traffic Classification: A Comprehensive Review

Takyi

Bagga

Goopta

2018

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

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The threat of malicious content on a network requires network administrators and users to accurately detect desirable traffic flow into their respective networks. To this effect, several studies have found it imperative to classify traffic flow, and to use traffic classification in various applications such as intrusion detection, monitoring systems, as well as pattern detection in various networks. Research into machine learning techniques of clustering emerged due to the inefficiencies and drawbacks of the traditional port-based and payload-based schemes. The classic Kmeans technique of clustering, in combination with other methods and parameters, can be used to build newer unsupervised and semi-supervised approaches to meliorate the quality of service in networks. In this paper, we review twelve of the existing clustering techniques. The review covers their contribution to clustering methods, the existing challenges, as well as recommendations for further research in clustering traffic flows.

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P2P traffic classification using clustering technology

Cited by 8 publications

References 17 publications

Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach

Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach

Scalable Intelligence-Enabled Networking with Traffic Engineering in 5G Scenarios for Future Audio-Visual-Tactile Internet

Clustering Techniques for Traffic Classification: A Comprehensive Review

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