Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison

Date

2010-06-01

Author

Soysal, Murat
Schmidt, Şenan Ece

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

235
views

0
downloads

The task of network management and monitoring relies on an accurate characterization of network traffic generated by different applications and network protocols. We employ three supervised machine learning (ML) algorithms, Bayesian Networks, Decision Trees and Multilayer Perceptrons for the flow-based classification of six different types of Internet traffic including peer-to-peer (P2P) and content delivery (Akamai) traffic. The dependency of the traffic classification performance on the amount and composition of training data is investigated followed by experiments that show that ML algorithms such as Bayesian Networks and Decision Trees are suitable for Internet traffic flow classification at a high speed, and prove to be robust with respect to applications that dynamically change their source ports. Finally, the importance of correctly classified training instances is highlighted by an experiment that is conducted with wrongly labeled training data.

Subject Keywords

Modelling and Simulation, Computer Networks and Communications, Hardware and Architecture, Software

URI

https://hdl.handle.net/11511/35934

Journal

PERFORMANCE EVALUATION

DOI

https://doi.org/10.1016/j.peva.2010.01.001

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Path planning for mobile-anchor based wireless sensor network localization: Static and dynamic schemes Erdemir, Ecenaz; Tuncer, Temel Engin (Elsevier BV, 2018-08-01) In wireless sensor networks, node locations are required for many applications. Usually, anchors with known positions are employed for localization. Sensor positions can be estimated more efficiently by using mobile anchors (MAs). Finding the best MA trajectory is an important problem in this context. Various path planning algorithms are proposed to localize as many sensors as possible by following the shortest path with minimum number of anchors. In this paper, path planning algorithms for MA assisted loca...
SWARM-based data delivery in Social Internet of Things Hasan, Mohammed Zaki; Al-Turjman, Fadi (Elsevier BV, 2019-03-01) Social Internet of Things (SIoTs) refers to the rapidly growing network of connected objects and people that are able to collect and exchange data using embedded sensors. To guarantee the connectivity among these objects and people, fault tolerance routing has to be significantly considered. In this paper, we propose a bio-inspired particle multi-swarm optimization (PMSO) routing algorithm to construct, recover and select k-disjoint paths that tolerates the failure while satisfying quality of service (QoS) ...
Efficient active rule processing in wireless multimedia sensor networks Oztarak, Hakan; Akkaya, Kemal; Yazıcı, Adnan; Sarisaray-Boluk, Pinar (Inderscience Publishers, 2016-01-01) Due to limited energy resources in wireless multimedia sensor networks (WMSNs), there is a need to perform data reduction and elimination over raw video data at the camera sensors before transmission. Nonetheless, this data reduction and elimination may create imprecision and uncertainty in the data, reducing the quality of decision making. In this paper, we propose a reactive mechanism for not only fusing uncertain data at the sink but also for automated processing of data using active rules, extending the...
Distributed restoration in optical networks using feed-forward neural networks Karpat, Demeter Gokisik; Bilgen, Semih (Springer Science and Business Media LLC, 2006-07-01) A new method is proposed for determining protection paths in an optical network where users have different characteristics in terms of reliability needs and security restrictions. Survivability is achieved by distributed mesh protection. Over the preplanned primary and backup capacity, optimal routing and wavelength assignment is carried out. In case of a network failure, protection routes and optimum flow values on these protection routes are extracted from a previously trained feed-forward neural network ...
Energy-aware routing algorithms for wireless ad hoc networks with heterogeneous power supplies Vazifehdan, Javad; Prasad, R. Venkatesha; Onur, Ertan; Niemegeers, Ignas (Elsevier BV, 2011-10-27) Although many energy-aware routing schemes have been proposed for wireless ad hoc networks, they are not optimized for networks with heterogeneous power supplies, where nodes may run on battery or be connected to the mains (grid network). In this paper, we propose several energy-aware routing algorithms for such ad hoc networks. The proposed algorithms feature directing the traffic load dynamically towards mains-powered devices keeping the hop count of selected routes minimal. We unify these algorithms into...

Citation Formats

M. Soysal and Ş. E. Schmidt, “Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison,” PERFORMANCE EVALUATION, pp. 451–467, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35934.