Heuristic-Based Real-Time P2P Traffic Identification

Reddy, Jagan Mohan; Hota, Chittaranjan

doi:10.1109/eites.2015.16

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…Our past work [14] proposed heuristic-based P2P traffic identification based on connection patterns. We proposed several heuristics derived from connection patterns to detect the P2P hosts in real-time.…”

Section: Related Workmentioning

confidence: 99%

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Behavior-based P2P traffic identification using fuzzy approach

Reddy

Hota

Rajarajan

2015

2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

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The use of P2P applications has been in rise over past several years due to their capabilities to share large volumes of files, efficiently transport large amounts of voice and video, allow anonymous browsing etc. bypassing the controls enforced by the service providers. Therefore the detection of these applications has become a challenge for ISPs and network administrators. In this paper, we propose P2P host identification framework based on fuzzy pattern recognition. The fuzzy patterns are examined from the behavior of P2P and NonP2P flows. We define several membership functions based on the behavioral characteristics including: (1) Bytes per second, (2) Packet Inter Arrival Time, (3) Packet count per second and (4) Number of unique hosts contacted. The performance evaluation based on the real-time traces show that the proposed system promises more than 98% of accuracy and a low false positive rate of ≈2%.

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Section: Related Workmentioning

confidence: 99%

“…To build these ML models we require large amount of time with respective to training dataset size. Apart from these approaches, research community focused on heuristicbased methods to identify P2P traffic in real-time [13], [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Behavior-based P2P traffic identification using fuzzy approach

Reddy

Hota

Rajarajan

2015

2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

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“…These characteristics were examined by collecting 100 blocks of P2P traffic (consisting of BitSpirit, Emule and other P2P applications), each ranging from 100 M bytes to 200 M bytes and evaluation of this approach achieved an accuracy ranging from 98.4 to 99.6 %. Reddy and Hota [71] proposed a new set of heuristics to identify P2P host based on its connection patterns and they do not require any payload signatures. The datasets used was realistic in nature ad consisted of applications namely Http, FTP, Dropbox, SMTP, eMule, Frostwire, Skype, uTorrent and Vuze.…”

Section: Classification Of Traffic In the Darkmentioning

confidence: 99%

Identifying P2P traffic: A survey

Bhatia

Kumar

2016

Peer-to-Peer Netw. Appl.

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Peer-to-Peer (P2P) traffic is widely used for the purpose of streaming media, file-sharing, instant messaging, games, software etc., which often involves copyrighted data. From the past decade, P2P traffic has been contributing to major portion of Internet traffic which is still rising and hence is consuming a lot of network traffic bandwidth. It also worsens congestion of network traffic significantly and degrades the performance of traditional client-server applications. Popularity of various P2P applications has led Internet Service Providers (ISPs) to face various challenges regarding efficiently and fairly utilizing network resources. The traditional methods of identifying P2P traffic such as port-based and payload-based are proving ineffective due to their significant limitations and can be bypassed. Hence, new approaches based on statistics or behaviour of network traffic needs to be developed and adopted in order to accurately identify existing and new P2P traffic which emerge over the time. This article presents a survey regarding various strategies involved in identifying P2P traffic. Furthermore, conceptual analysis of network traffic measurement and monitoring is also presented.

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“…Historically, traffic classification typically used port-based identification, which was simple to implement and yielded high accuracy for certain applications such as SMTP or DNS given their static use of specific port numbers. However, most of the present Internet applications either use dynamic port numbers randomly without any prior assumption or use encryption and tunnelling traffic through wellknown port such as 80 443 [9]. For example, Skype and P2P applications use TCP port 80 [10] which would appear to be web browsing when using portbased classification, despite the fact that it could be messaging, file transfer or voice communication traffic.…”

Section: Port-based Approachmentioning

confidence: 99%

A Novel Feature Set for Application Identification

Oudah¹,

Ghita²,

Bakhshi³

2018

IJISR

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Classifying Internet traffic into applications is vital to many areas, from quality of service (QoS) provisioning, to network management and security. The task is challenging as network applications are rather dynamic in nature, tend to use a web front-end and are typically encrypted, rendering traditional port-based and deep packet inspection (DPI) method unusable. Recent classification studies proposed two alternatives: using the statistical properties of traffic or inferring the behavioural patterns of network applications, both aiming to describe the activity within and among network flows in order to understand application usage and behaviour. The aim of this paper is to propose and investigate a novel feature to define application behaviour as seen through the generated network traffic by considering the timing and pattern of user events during application sessions, leading to an extended traffic feature set based on burstiness. The selected features were further used to train and test a supervised C5.0 machine learning classifier and led to a better characterization of network applications, with a traffic classification accuracy ranging between 90-98%.

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Heuristic-Based Real-Time P2P Traffic Identification

Cited by 10 publications

References 12 publications

Behavior-based P2P traffic identification using fuzzy approach

Behavior-based P2P traffic identification using fuzzy approach

Identifying P2P traffic: A survey

A Novel Feature Set for Application Identification

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