Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data

Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to adaptively characterize and quantify network dynamics efficiently in real-time. We then use these models as emulators of more structured, time-varying gravity models that allow formal dissection of network dynamics. This yields interpretable inferences on traffic flow characteristics, and on dynamics in interactions among network nodes. Bayesian monitoring theory defines a strategy for sequential model assessment and adaptation in cases when network flow data deviates from model-based predictions. Exploratory and sequential monitoring analyses of evolving traffic on a network of web site-segments in e-commerce demonstrate the utility of this coupled Bayesian emulation approach to analysis of streaming network count data.Comment: 29 pages, 16 figure

Self-* Features for Semantic Networking

http://www.fitramen.eu/program.htmInternational audienceWe propose the Semantic Networking concept as a candidate for the Internet of the Future. Re-thinking of the architectural and functional paradigms is needed to face scalability and complexity issues in the current Internet developments. A fundamental of our proposal is to reconsider all the networking and service operations based on the flow granularity, thus beyond packet or circuit paradigms. This is enabled by the awareness of the transported traffic, thanks to a combined Deep Packet Inspection and Behavioral Analysis approach. Together with the flow-based and traffic-aware features, Autonomic Networking is considered as a pillar of this concept which leads in turn to specific requirements. This paper is an introduction to autonomic features which should be instantiated as per the Semantic Networking goals, within the traffic-aware data plane ("Semantic Analysis", "Elastic Fluid Switching"), the flow-based control plane ("Flow Admission Control", "Flow Policing", "Traffic Aware Routing"), and the self-management plane ("Network Mining", "Knowledge Plane"). We describe each of these functional building blocks, their interactions, the requirements for their autonomic (or self-*) features, and their localization in transport network nodes to transform them into "semantic network nodes"

Positive and unlabeled learning for user behavior analysis based on mobile internet traffic data

With the rapid development of wireless communication and mobile Internet, mobile phone becomes ubiquitous and functions as a versatile and smart system, on which people frequently interact with various mobile applications (Apps). Understanding human behaviors using mobile phone is significant for mobile system developers, for human-centered system optimization and better service provisioning. In this paper, we focus on mobile user behavior analysis and prediction based on mobile Internet traffic data. Real traffic flow data is collected from the public network of Internet Service Providers (ISPs), by high-performance network traffic monitors.We construct User-App bipartite network to represent the traffic interaction pattern between users and App servers. After mining the explicit and implicit features from User-App bipartite network, we propose two positive and unlabeled learning (PU learning) methods, including Spy-based PU learning and K-means-based PU learning, for App usage prediction and mobile video traffic identification. We firstly use the traffic flow data of QQ, a very famous messaging and social media application possessing high market share in China, as the experimental dataset for App usage prediction task. Then we use the traffic flow data from six popular Apps, including video intensive Apps (Youku, Baofeng, LeTV, Tudou) and other Apps (Meituan, Apple), as the experimental dataset for mobile video traffic identification task. Experimental results show that our proposed PU learning methods perform well in both tasks

Flow Data Collection in Large Scale Networks

In this chapter, we present flow-based network traffic monitoring of large scale networks. Continuous Internet traffic increase requires a deployment of advanced monitoring techniques to provide near real-time and long-term network visibility. Collected flow data can be further used for network behavioral analysis to indicate legitimate and malicious traffic, proving cyber threats, etc. An early warning system should integrate flow-based monitoring to ensure network situational awareness.Kapitola představuje monitorování síťového provozu v rozsáhlých počítačových sítích založené na IP tocích. Nepřetržitý růst internetového provozu vyžaduje nasazení pokročilých monitorovacích technik, které poskytují v reálném čase a dlouhodobě pohled na dění v síti. Nasbíraná data mohou dále sloužit pro analýzu chování sítě k rozlišení legitimního a škodlivého provozu, dokazování kybernetických hrozeb atd. Systém včasného varování by měl integrovat monitorování síťových toků, aby mohl poskytovat přehled o situaci na síti

Streaming media over the Internet: Flow based analysis in live access networks

Multimedia service delivery over the Internet is a success. The number of services available and the number of people accessing them is huge. In this paper, we investigate multimedia streaming services over the Internet. Our analysis is based on traffic measurement in live access fiber-to-the-home networks. We study parameters like traffic volume and flow characteristics for selected services. Especially the Swedish P2P video service Voddler and the Swedish P2P music service Spotify are studied. We show that indeed these services are widely used (20% of local hosts using Voddler, 65 % of local hosts using Spotify). We also show that they are different concerning the flow characteristics, with many short flows for Voddler and longer flows for Spotify. One thing that they have in common in our measurements is that the outbound, or uplink, traffic volume is larger than the inbound

TAMC: Traffic Analysis Measurement and Classification Using Hadoop MapReduce

Due to growth in Internet users and bandwidth-hungry applications; the amount of Internet traffic data generated is so huge. It requires scalable tools to analyze, measure, and classify this traffic data. Traditional tools fail to do this task due to their limited computational capacity and storage capacity. Hadoop is a distributed framework which performs this task in very efficient manner. Hadoop mainly runs on commodity hardware with distributed storage and process this huge amount of traffic data with a Map-Reduce programming model. We have implemented Hadoop-based TAMC tool which perform Traffic Analysis, Measurement, and Classification with respect to various parameters at packet and flow level. The results can be used by Network Administrator and ISP’s for various usages. DOI: 10.17762/ijritcc2321-8169.15013