An Overview of Internet Measurements:Fundamentals, Techniques, and Trends

The Internet presents great challenges to the characterization of its structure and behavior. Different reasons contribute to this situation, including a huge user community, a large range of applications, equipment heterogeneity, distributed administration, vast geographic coverage, and the dynamism that are typical of the current Internet. In order to deal with these challenges, several measurement-based approaches have been recently proposed to estimate and better understand the behavior, dynamics, and properties of the Internet. The set of these measurement-based techniques composes the Internet Measurements area of research. This overview paper covers the Internet Measurements area by presenting measurement-based tools and methods that directly influence other conventional areas, such as network design and planning, traffic engineering, quality of service, and network management

Computer application for measuring parameters of network

Bakalářská práce se zabývá základní teorií síťové architektury TCP/IP, která je využívána v praxi a stanovuje klíčové parametry datových a multimediálních služeb využívajících tyto sítě. Dále se práce zabývá hlavně metodikou měření jednotlivých parametrů a rozebírá různé metody měření a jejich přesnost. V praktické části bakalářské práce je naprogramována počítačová aplikace Network Meter pracující v režimu klient-server, která umožňuje měření vybraných parametrů mezi klientem a serverem s pomocí transportních protokolů TCP a UDP. Při použití protokolu TCP umožňuje aplikace měřit zpoždění, rychlost odesílání dat a rychlost stahování dat. Při použití druhého transportního protokolu, tedy UDP, umožňuje aplikace měřit kolísání zpoždění a ztrátovost paketů. S měřící aplikací Network Meter bylo provedeno testovací měření na připravené domácí síti. Na stejné síti bylo provedeno měření i pomocí aplikace jPerf, aby bylo možné porovnat vytvořenou aplikaci s jinými aplikacemi. Při porovnávacím měření byla měřena rychlost stahování dat.This bachelor’s thesis deals with the basic theory of network architecture TCP/IP which is used in practice and sets out the key parameters for data services and multimedia services using these networks. The thesis mainly deals with the methodology of measuring individual parameters and analyzes various methods of measurement and the accuracy of these methods. In the practical part of the bachelor's thesis a client-server mode application called Network Meter was programmed. This application allows the measurement of selected parameters between client and server using transport protocols TCP and UDP. When using TCP, the application can measure delay, data upload speed and data download speed. When using the second transport protocol, namely UDP, the application can measure jitter and packet loss. With the measuring application Network Meter test measurements were performed in the prepared home network. In the same network measurements were made using jPerf to compare the created application with other applications. In the comparison measurement download speed was measured.

Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation

Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and reference comparisons against Machine Learning (ML) methods. Among those works, a recent study from IMC22 [16] is worth of attention since it adopts recent DL methodologies (namely, few-shot learning, self-supervision via contrastive learning and data augmentation) appealing for networking as they enable to learn from a few samples and transfer across datasets. The main result of [16] on the UCDAVIS19, ISCX-VPN and ISCX-Tor datasets is that, with such DL methodologies, 100 input samples are enough to achieve very high accuracy using an input representation called "flowpic" (i.e., a per-flow 2d histograms of the packets size evolution over time). In this paper (i) we reproduce [16] on the same datasets and (ii) we replicate its most salient aspect (the importance of data augmentation) on three additional public datasets (MIRAGE19, MIRAGE22 and UTMOBILENET21). While we confirm most of the original results, we also found a 20% accuracy drop on some of the investigated scenarios due to a data shift in the original dataset that we uncovered. Additionally, our study validates that the data augmentation strategies studied in [16] perform well on other datasets too. In the spirit of reproducibility and replicability we make all artifacts (code and data) available to the research community at https://tcbenchstack.github.io/tcbench/Comment: to appear at ACM Internet Traffic Measurement (IMC) 2023, replication trac

Mobile application for the analysis of the basic parameters of NGA network

Bakalářská práce se zabývá problematikou měření kvality služeb v rámci mobilních sítí. V práci jsou rozebrány jednotlivá standardy mobilních sítí, kvalita služeb, jak jí dosáhnout a měřící metoda. Poté práce obsahuje popis serverového programu pro měření parametrů ovlivňující multimediální přenos dat. Dále je v práci rozebrána samotná aplikace pro mobilní platformu Android, která dané parametry vyhodnocuje. Nakonec jsou rozebrané naměřené zkušební vzorky.Bachelor thesis focuses on measurement of quality of service within mobile networks. The thesis explains standards of mobile networks, quality of service, how achieve specified quality and measurement method. Then thesis explains server program for measuring multimedia data and mobile application for operating system Android, which can evaluate and show measured data. In the end there is shown measurement of testing data.

Design and Implementation of Algorithms for Traffic Classification

Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead

Application and network traffic correlation of grid applications

Dynamic engineering of application-specific network traffic is becoming more important for applications that consume large amounts of network resources, in particular, bandwidth. Since traditional traffic engineering approaches are static they cannot address this trend; hence there is a need for real-time traffic classification to enable dynamic traffic engineering. A packet flow monitor has been developed that operates at full Gigabit Ethernet line rate, reassembling all TCP flows in real-time. The monitor can be used to classify and analyse both plain text and encrypted application traffic. This dissertation shows, under reasonable assumptions, 100% accuracy for the detection of bulk data traffic for applications when control traffic is clear text and also 100% accuracy for encrypted GridFTP file transfers when data channels are authenticated. For non-authenticated GridFTP data channels, 100% accuracy is also achieved, provided the transferred files are tens of megabytes or more in size. The monitor is able to identify bulk flows resulting from clear text control protocols before they begin. Bulk flows resulting from encrypted GridFTP control sessions are identified before the onset of bulk data (with data channel authentication) or within two seconds (without data channel authentication). Finally, the system is able to deliver an event to a local publish/subscribe server within 1 ms of identification within the monitor. Therefore, the event delivery introduces negligible delay in the ability of the network management system to react to the event

Modelos Matemáticos Basados en Consumos Computacionales para el Estudio de Rendimiento de Sondas de Análisis de Tráfico en Redes de Datos

196 p.La monitorización de tráfico es una operación crítica dentro de las tareas de gestión de red. Por ello, es necesario disponer de herramientas y equipos que analicen el tráfico de red y detecten posibles anomalías, fallos de configuración, ataques o intrusiones. Este trabajo de Tesis se centra en el estudio de equipos denominados sondas de análisis de tráfico que realizan labores de monitorización. Tras analizar la evolución de estos sistemas desde las primeras redes Gigabit Ethernet hasta las redes 5G actuales, la Tesis propone modelos analíticos dirigidos a medir el rendimiento de dichos dispositivos. Se presentan tres modelos basados en teoría de colas: en el primero, sobre un cola tándem con un único servidor activo, se formula un proceso de decisión de Markov que optimiza el throughput de una sonda de análisis; en el segundo, se analiza y se mide el rendimiento de un sistema de captura de paquetes mediante un modelo de cola con vacations; por último, el tercero plantea una red abierta de colas para tomar decisiones en el despliegue de funciones virtuales de red (VNFs) de un servicio de Misión Crítica sobre una red 5G. Cada modelo se resuelve con una técnica diferente y posteriormente se valida, bien sea comparando sus resultados con medidas experimentales de una sonda real o bien mediante simulación