A low complexity real-time Internet traffic flows neuro-fuzzy classifier

Rizzi, Antonello; Iacovazzi, Alfonso; Baiocchi, Andrea; Colabrese, Silvia

doi:10.1016/j.comnet.2015.09.011

Cited by 15 publications

(11 citation statements)

References 12 publications

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“…While there are much work on flow classification, few works in the literature consider the runtime aspects. Rizzi et al [16] present a neuro-fuzzy classifier for which they report a classification rate of below 200.000 flows per second on the evaluated FPGA hardware.…”

Section: Discussionmentioning

confidence: 99%

Towards Video Flow Classification at a Million Encrypted Flows Per Second

Garcia

Korhonen

Andersson

et al. 2018

2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)

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As end-to-end encryption on the Internet is becoming more prevalent, techniques such as deep packet inspection (DPI) can no longer be expected to be able to classify traffic. In many cellular networks a large fraction of all traffic is video traffic, and being able to divide flows in the network into video and non-video can provide considerable traffic engineering benefits. In this study we examine machine learning based flow classification using features that are available also for encrypted flows. Using a data set of several several billion packets from a live cellular network we examine the obtainable classification performance for two different ensemble-based classifiers. Further, we contrast the classification performance of a statistical-based feature set with a less computationally demanding alternate feature set. To also examine the runtime aspects of the problem, we export the trained models and use a tailor-made C implementation to evaluate the runtime performance. The results quantify the trade-off between classification and runtime performance, and show that up to 1 million classifications per second can be achieved for a single core. Considering that only the subset of flows reaching some minimum flow length will need to be classified, the results are promising with regards to deployment also in scenarios with very high flow arrival rates.

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Section: Discussionmentioning

confidence: 99%

Towards Video Flow Classification at a Million Encrypted Flows Per Second

Garcia

Korhonen

Andersson

et al. 2018

2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)

View full text Add to dashboard Cite

show abstract

“…Besides the use of supervised and unsupervised learning algorithms, some studies make use of optimization algorithms in order to enhance the quality of their methods [12,16]. Optimization algorithms are usually used to find the best set of parameters aiming to enhance the results, either by minimizing or maximizing a function (e.g., to maximize the flow accuracy metric in network classification problems, where the function is the classifier algorithm (model) itself).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Due to the growth of the Internet, both in the number of applications and traffic volume, it is important to understand the composition of such data, especially by Internet Service Providers (ISPs) to manage bandwidth resources, with focus on the Quality of Service (QoS) and security.…”

Section: Introductionmentioning

confidence: 99%

MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

Nascimento¹,

Sadok²

2018

Information

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Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users’ privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a Multi-Objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods.

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“…The advantages include reduced training time as Feed formed network is used to search fuzzy Decision Rules. Neuro Fuzzy classifiers have been used to lower down the complexity of internet flows also [16] .It used both privacy thread and usage police as well as the Quality of service and NeuroFuzzy machine learning specially in Min-Max networks trained by the Parc algorithm. This technique is applied when there is possible to describe the individual flow and also when packet payload are encrypted and the main purpose of min max algorithm is that complexity is less than the other accurate models .…”

Section: Literaturesurveymentioning

confidence: 99%

Algorithmic Design to Mitigate Risks by Neuro- Fuzzy Techniques

Malik¹

2017

ijarcs

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The heart of research aims to develop an algorithmic paradigm for risk mitigation in software projects by using Neuro-Fuzzy techniques. We have identified the requirements for developing the prototype of required tool which will help in determining the risk level of the project. Various approaches of Artificial Intelligence have been discussed in detail for risk assessment and mitigation in past. The algorithm that has been designed can be implemented using MATLAB or Java Frame works. The system utilizes Fuzzy logic to build the fuzzy inference system which focuses on the production of the membership function which is used for the Fuzzification and Defuzzification process. The training of system is done by using back propagation and Bayesian regulation approach of the neural networks. Neuro-Fuzzy technique has been applied to analyze the risk for dealing with uncertainty and incomplete specifications. The performance can have better results for neuro fuzzy model if we use Bayesian regulation approach as compare to the Back propagation because of low prediction capabilities in Back propagation.

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A low complexity real-time Internet traffic flows neuro-fuzzy classifier

Cited by 15 publications

References 12 publications

Towards Video Flow Classification at a Million Encrypted Flows Per Second

Towards Video Flow Classification at a Million Encrypted Flows Per Second

MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

Algorithmic Design to Mitigate Risks by Neuro- Fuzzy Techniques

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