Open Access
Approximations for end-to-end delay analysis in OBS networks with light load
(IEEE, 2004) Morató Osés, Daniel; Magaña Lizarrondo, Eduardo; Izal Azcárate, Mikel; Automática y Computación; Automatika eta Konputazioa
In this paper we provide an analysis of end-to-end delay in OBS networks and a large deviations approximation. The analysis is based on an exponential approximation of the OBS router blocking time and on the assumption of Poisson arrivals in routers along the path from source to destination. On the other hand, a lightload assumption is performed, namely, waiting time is mainly due to residual life of the output wavelengths and not to buffering.
Open Access
Pamplona-traceroute: topology discovery and alias resolution to build router level Internet maps
(IEEE, 2013) García-Jiménez, Santiago; Magaña Lizarrondo, Eduardo; Morató Osés, Daniel; Izal Azcárate, Mikel; Automática y Computación; Automatika eta Konputazioa
An Internet topology map at the router level not only needs to discover IP addresses in Internet paths (traceroute) but also needs to identify IP addresses belonging to the same router (IP aliases). Both processes, discovery and IP alias resolution, have traditionally been independent tasks. In this paper, a new tool called Pamplona-traceroute is proposed to improve upon current results in a state of the art for Internet topology construction at the router level. Indirect probing using TTLscoped UDP packets, usually present in the discovery phases, is reused in IP alias resolution phases, providing high identification rates, especially in access routers.
Open Access
A proposal of burst cloning for video quality improvement in optical burst switching networks
(2013) Espina Antolín, Félix; Morató Osés, Daniel; Izal Azcárate, Mikel; Magaña Lizarrondo, Eduardo; Automática y Computación; Automatika eta Konputazioa
Open Access
A survey on detection techniques for cryptographic ransomware
(IEEE, 2019) Berrueta Irigoyen, Eduardo; Morató Osés, Daniel; Magaña Lizarrondo, Eduardo; Izal Azcárate, Mikel; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación
Crypto-ransomware is a type of malware that encrypts user files, deletes the original data, and asks for a ransom to recover the hijacked documents. It is a cyber threat that targets both companies and residential users, and has spread in recent years because of its lucrative results. Several articles have presented classifications of ransomware families and their typical behaviour. These insights have stimulated the creation of detection techniques for antivirus and firewall software. However, because the ransomware scene evolves quickly and aggressively, these studies quickly become outdated. In this study, we surveyed the detection techniques that the research community has developed in recent years. We compared the different approaches and classified the algorithms based on the input data they obtain from ransomware actions, and the decision procedures they use to reach a classification decision between benign or malign applications. This is a detailed survey that focuses on detection algorithms, compared to most previous studies that offer a survey of ransomware families or isolated proposals of detection algorithms. We also compared the results of these proposals.
Open Access
Ingress traffic classification versus aggregation in video over OBS networks
(2010) Izal Azcárate, Mikel; Espina Antolín, Félix; Morató Osés, Daniel; Magaña Lizarrondo, Eduardo; Automática y Computación; Automatika eta Konputazioa
Optical Burst Switched (OBS) networks may become a backbone technology for video-on-demand providers. This work addresses the problem of dimensioning the access link of an ingress node to the optical core network in a video over OBS scenario. A video-ondemand provider using an OBS transport network will have to deliver traffic to a set of egress destinations. A large part of this traffic would be composed of video streaming traffic. However, in a real network there would be also a fraction of non video traffic related to non video services. This work studies the decision whether it is better to gather all traffic to the same destination in a joint burst assembler or separate video and general data traffic on different burs assemblers. The later may increase burst blocking probability but also allow for better tuning of OBS parameters that help improve video reception quality. Result show that this tuning of parameters is not enough to compensate the drop probability increase and thus it is better to aggregate video and general data traffic.
Open Access
Delay-throughput curves for timer-based OBS burstifiers with light load
(IEEE, 2006) Izal Azcárate, Mikel; Aracil Rico, Javier; Morató Osés, Daniel; Magaña Lizarrondo, Eduardo; Automática y Computación; Automatika eta Konputazioa
The OBS burstifier delay-throughput curves are analyzed in this paper. The burstifier incorporates a timer-based scheme with minimum burst size, i. e., bursts are subject to padding in light-load scenarios. Precisely, due to this padding effect, the burstifier normalized throughput may not be equal to unity. Conversely, in a high-load scenario, padding will seldom occur. For the interesting light-load scenario, the throughput delay curves are derived and the obtained results are assessed against those obtained by trace-driven simulation. The influence of long-range dependence and instantaneous variability is analyzed to conclude that there is a threshold timeout value that makes the throughput curves flatten out to unity. This result motivates the introduction of adaptive burstification algorithms, that provide a timeout value that minimizes delay, yet keeping the throughput very close to unity. The dependence of such optimum timeout value with traffic long-range dependence and instantaneous burstiness is discussed. Finally, three different adaptive timeout algorithms are proposed, that tradeoff complexity versus accuracy.
Open Access
Traffic generator using Perlin Noise
(IEEE, 2012) Prieto Suárez, Iria; Izal Azcárate, Mikel; Morató Osés, Daniel; Magaña Lizarrondo, Eduardo; Automática y Computación; Automatika eta Konputazioa; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Study of high speed networks such as optical next generation burst or packet switched networks require large amounts of synthetic traffic to feed simulators. Methods to generate self-similar long range dependent traffic already exist but they usually work by generating large blocks of traffic of fixed time duration. This limits simulated time or require very high amount of data to be stored before simulation. On this work it is shown how self-similar traffic can be generated using Perlin Noise, an algorithm commonly used to generate 2D/3D noise for natural looking graphics. 1-dimension Perlin Noise can be interpreted as network traffic and used to generate long range dependent traffic for network simulation. The algorithm is compared to more classical approach Random Midpoint Displacement showing at traffic generated is similar but can be generated continuously with no fixed block size.
Open Access
Online classification of user activities using machine learning on network traffic
(Elsevier, 2020) Labayen Guembe, Víctor; Magaña Lizarrondo, Eduardo; Morató Osés, Daniel; Izal Azcárate, Mikel; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
The daily deployment of new applications, along with the exponential increase in network traffic, entails a growth in the complexity of network analysis and monitoring. Conversely, the increasing availability and decreasing cost of computational capacity have increased the popularity and usability of machine learning algorithms. In this paper, a system for classifying user activities from network traffic using both supervised and unsupervised learning is proposed. The system uses the behaviour exhibited over the network and classifies the underlying user activity, taking into consideration all of the traffic generated by the user within a given time window. Those windows are characterised with features extracted from the network and transport layer headers in the traffic flows. A three-layer model is proposed to perform the classification task. The first two layers of the model are implemented using K-Means, while the last one uses a Random Forest to obtain the activity labels. An average accuracy of 97.37% is obtained, with values of precision and recall that allow online classification of network traffic for Quality of Service (QoS) and user profiling, outperforming previous proposals.
Open Access
Techniques for better alias resolution in Internet topology discovery
(IEEE, 2009) García-Jiménez, Santiago; Magaña Lizarrondo, Eduardo; Morató Osés, Daniel; Izal Azcárate, Mikel; Automática y Computación; Automatika eta Konputazioa
One of the challenging problems related with network topology discovery in Internet is the process of IP address alias identification. Topology information is usually obtained from a set of traceroutes that provide IP addresses of routers in the path from a source to a destination. If these traceroutes are repeated between several source/destination pairs we can get a sampling of all IP addresses for crossed routers. In order to generate the topology graph in which each router is a node, it is needed to identify all IP addresses that belong to the same router. In this work we propose improvements over existing methods to obtain alias identification related mainly with the types and options in probing packets.
Open Access
Protocol-agnostic method for monitoring interactivity time in remote desktop services
(Springer Nature, 2021-02-24) Arellano Usón, Jesús; Magaña Lizarrondo, Eduardo; Morató Osés, Daniel; Izal Azcárate, Mikel; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
The growing trend of desktop virtualisation has facilitated the reduction of management costs associated with traditional systems and access to services from devices with different capabilities. However, desktop virtualisation requires controlling the interactivity provided by an infrastructure and the quality of experience perceived by users. This paper proposes a methodology for the quantification of interactivity based on the measurement of the time elapsed between user interactions and the associated responses. Measurement error is controlled using a novel mechanism for the detection of screen changes, which can lead to erroneous measurements. Finally, a campus virtual desktop infrastructure and the Amazon WorkSpaces solution are analysed using this proposed methodology. The results demonstrate the importance of the location of virtualisation infrastructure and the types of protocols used by remote desktop services.