Person: Izal Azcárate, Mikel
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Izal Azcárate
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Mikel
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Ingeniería Eléctrica, Electrónica y de Comunicación
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ISC. Institute of Smart Cities
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0000-0002-2770-912X
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2083
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Publication Open Access Survey on quality of experience evaluation for cloud-based interactive applications(MDPI, 2024) 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 - ISCA cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the pandemic and the emergence of entertainment schemes similar to cloud gaming platforms. Evaluating the quality of experience (QoE) in these applications requires specific metrics, including interactivity time, responsiveness, and the assessment of video- and audio-quality degradation. Despite existing studies that evaluate QoE and compare features of general cloud applications, systematic research into QoE for CIAs is lacking. Previous surveys often narrow their focus, overlooking a comprehensive assessment. They touch on QoE in broader contexts but fall short in detailed metric analysis. Some emphasise areas like mobile cloud computing, omitting CIA-specific nuances. This paper offers a comprehensive survey of QoE measurement techniques in CIAs, providing a taxonomy of input metrics, strategies, and evaluation architectures. State-of-the-art proposals are assessed, enabling a comparative analysis of their strengths and weaknesses and identifying future research directions.Publication Open Access Network simulation in a TCP-enabled industrial internet of things environment - reproducibility issues for performance evaluation(IEEE, 2022) Morató Osés, Daniel; Pérez-Gómara, Carlos; Magaña Lizarrondo, Eduardo; Izal Azcárate, Mikel; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de ComunicaciónNetwork simulation is a tool used to analyse and predict the performance of Industrial Internet of Things deployments while dealing with the complexity of real testbeds. Large network deployments with complex protocols such as Transmission Control Protocol are subject to chaos-theory behaviour, i.e. small changes in the implementation of the protocol stack or simulator behaviour may result in large differences in the performance results. We present the results of simulating two different scenarios using three simulators. The first scenario focuses on the Incast phenomenon in a local area network where sensor data are collected. The second scenario focuses on a congested link traversed by the collected measurements. The performance metrics obtained from the simulators are compared among them and with ground-truth obtained from real network experiments. The results demonstrate how subtle implementation differences in network simulators impact performance results, and how network engineers must consider these differences.Publication Open Access Open repository for the evaluation of ransomware detection tools(IEEE, 2020) 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 ComunicaciónCrypto-ransomware is a type of malware that encrypts user files, deletes the original data, and asks for ransom to recover the hijacked documents. Several articles have presented detection techniques for this type of malware; these techniques are applied before the ransomware encrypts files or during its action in an infected host. The evaluation of these proposals has always been accomplished using sets of ransomware samples that are prepared locally for the research article, without making the data available. Different studies use different sets of samples and different evaluation metrics, resulting in insufficient comparability. In this paper, we describe a public data repository containing the file access operations of more than 70 ransomware samples during the encryption of a large network shared directory. These data have already been used successfully in the evaluation of a network-based ransomware detection algorithm. Now, we are making these data available to the community and describing their details, how they were captured, and how they can be used in the evaluation and comparison of the results of most ransomware detection techniques.Publication Open Access Crypto-ransomware detection using machine learning models in file-sharing network scenarios with encrypted traffic(Elsevier, 2022) Berrueta Irigoyen, Eduardo; Morató Osés, Daniel; Magaña Lizarrondo, Eduardo; Izal Azcárate, Mikel; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaRansomware is considered as a significant threat for home users and enterprises. In corporate scenarios, users’ computers usually store only system and program files, while all the documents are accessed from shared servers. In these scenarios, one crypto-ransomware infected host is capable of locking the access to all shared files it has access to, which can be the whole set of files from a workgroup of users. We propose a tool to detect and block crypto-ransomware activity based on file-sharing traffic analysis. The tool monitors the traffic exchanged between the clients and the file servers and using machine learning techniques it searches for patterns in the traffic that betray ransomware actions while reading and overwriting files. This is the first proposal designed to work not only for clear text protocols but also for encrypted file-sharing protocols. We extract features from network traffic that describe the activity opening, closing, and modifying files. The features allow the differentiation between ransomware activity and high activity from benign applications. We train and test the detection model using a large set of more than 70 ransomware binaries from 33 different strains and more than 2,400 h of ‘not infected’ traffic from real users. The results reveal that the proposed tool can detect all ransomware binaries described, including those not used in the training phase. This paper provides a validation of the algorithm by studying the false positive rate and the amount of information from user files that the ransomware could encrypt before being detectedPublication Open Access On the reduction of authoritative DNS cache timeouts: detection and implications for user privacy(Elsevier, 2021) Hernández Quintanilla, Tomás; Magaña Lizarrondo, Eduardo; Morató Osés, Daniel; Izal Azcárate, Mikel; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de ComunicaciónThe domain name system (DNS) is an Internet network service that is used by hosts to resolve IP addresses from symbolic names. This basic service has been attacked and abused many times, as it is one of the oldest and most vulnerable services on the Internet. Some DNS resolvers conduct DNS manipulation, in which authoritative DNS responses are modified. This DNS manipulation is sometimes used for legitimate reasons (e.g., parental control) and other times is used to support malicious activities, such as DNS poisoning or data collection. Between these DNS manipulation activities, some Internet service providers (ISPs) are changing the DNS cache timeout of the DNS responses with which their DNS resolvers responded to obtain additional data about their subscribers. These data can be a detailed web browsing profile of the user. This approach does not require a large investment and can yield huge benefits if the information is used or sold. Therefore, user privacy is disputed. We conducted a study in which we analyse how ISPs use this DNS manipulation, propose a method for identifying this DNS manipulation by the end-user and determine the amount of information an ISP can collect by using it. We also developed a public web tool, for which the source code is available, that can help Internet users determine whether their privacy is being compromised by their ISP via the exploitation of DNS cache timeouts. This service can facilitate the collection of data on how many people are victims of this abuse and which ISPs around the world are utilizing this technique.Publication Open Access Evaluation of RTT as an estimation of interactivity time for QoE evaluation in remote desktop environments(IEEE, 2023) Arellano Usón, Jesús; Magaña Lizarrondo, Eduardo; Morató Osés, Daniel; Izal Azcárate, Mikel; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio IngeniaritzaIn recent years, there has been a notable surge in the utilization of remote desktop services, largely driven by the emergence of new remote work models introduced during the pandemic. Traditional evaluation of the quality of experience (QoE) of users in remote desktop environments has relied on measures such as round-trip time (RTT). However, these measures are insufficient to capture all the factors that influence QoE. This study evaluated RTT and interactivity time in an enterprise environment over a period of 6 months and analysed the suitability of using RTT drawing previously unexplored connections between RTT, interactivity, and QoE. The results indicate that RTT is an insufficient indicator of QoE in productive environments with low RTT values. We outline some precise measures of interactivity needed to capture all the factors that contribute to QoE in remote desktop environments.Publication Open Access Validation of HTTP response time from network traffic as an alternative to web browser instrumentation(IEEE, 2021) López Romera, Carlos; 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 ComunicaciónThe measurement of response time in hypertext transfer protocol (HTTP) requests is the most basic proxy measurement method for evaluating web browsing quality. It is used in the research literature and in application performance measurement instruments. During the development of a website, response time is obtained from in-browser measurements. After the website has been deployed, network traffic is used to continuously monitor activity, and the measurement data are used for service management and planning. In this study, we evaluate the accuracy of the measurements obtained from network traffic by comparing them with the in-browser measurement of resource load time. We evaluate the response times for encrypted and clear-text requests in an emulated network environment, in a laboratory deployment equivalent to a data centre network, and accessing popular web sites on the public Internet. The accuracy for response time measurements obtained from network traffic is noticeable higher for Internet long distance paths than for lowdelay paths (below 20 ms round-trip). The overhead of traffic encryption in secure HTTP requests has a negative effect on measurement accuracy, and we find relative measurement errors higher than 70% when using network traffic to infer HTTP response times comparedPublication Open Access Interactivity anomaly detection in remote work scenarios using LTSM(IEEE, 2024) 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 - ISCIn recent years, there has been a notable surge in the utilization of remote desktop services, largely driven by the emergence of new remote work models introduced during the pandemic. These services cater to interactive cloud-based applications (CIAs), whose core functionality operates in the cloud, demanding strict end-user interactivity requirements. This boom has led to a significant increase in their deployment, accompanied by a corresponding increase in associated maintenance costs. Service administrators aim to guarantee a satisfactory Quality of Experience (QoE) by monitoring metrics like interactivity time, particularly in cloud environments where variables such as network performance and shared resources come into play. This paper analyses anomaly detection state of the art and proposes a novel system for detecting interactivity time anomalies in cloud-based remote desktop environments. We employ an automatic model based on LSTM neural networks that achieves an accuracy of up to 99.97%.Publication 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 IngeniaritzarenThe 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.