Datasets constitute the foundation for the training of a robust Intrusion Detection System (IDS). In particular, IDSs could be useful in IoT scenarios, in which resource-constrained devices can not run traditional security software and require a monitoring entity within their network. In this research, we move along two main directions: on one hand, the analysis and description of significant datasets regarding the security of IoT communications; on the other, the exploration of tools that enable their generation. Following this division, we first depict IoT datasets available in the literature and compare them with respect to their most important characteristics according to the literature. We also compare them according to the most popular features that are typically collected in such scenarios, including network and transport layer information, temporal parameters, payload characteristics, and statistical indicators. We then move our attention to the software tools and frameworks which enable the generation of benign or malicious network traffic in various experimental environments and are used in the construction of datasets. We believe this work could serve as a useful starting point to develop novel approaches and techniques in the field.

Depression and anxiety are among the most common responses to large-scale traumatic episodes (e.g. pandemics or armed conflicts), particularly when these involve prolonged restriction measures for the population. The goal of this study is to predict the extent of depressive symptoms, evaluated using the Beck Depression Inventory-II (BDI-II) scale, during the COVID-19 lockdown, starting from scores of multiple psychological scales collected prior to the lockdown. More importantly, we aim to identify the most influential features driving these predictions through eXplainable AI (XAI) techniques. To this end, we selected Gradient Boosting as our predictive model and applied SHapley Additive exPlanations (SHAP) to assess feature importance. We then generated Partial Dependence Plots (PDPs) on the topranking features identified by SHAP to further explore their impact on the model's output. Scores from Item 9, Item 3, and Item 1 of the BDI-II scales, regarding Suicidal Thoughts, Sadness and Failure emerged as key predictors in the model.

Distributed denial-of-service (DDoS) attacks may be driven not only by economic motives such as extortion, but also by social or political goals, including hacktivism and state-sponsored operations. Therefore, the monetary value of a target alone does not fully explain why some organizations are more frequently victimized. While cloud providers deploy advanced defenses — such as Anycast routing, traffic scrubbing, and filtering — they also concentrate many potential targets within a shared infrastructure, increasing their exposure to DDoS attacks. This study aims to understand what makes organizations more suitable DDoS targets by examining two key attributes: visibility and perceived value, represented by website popularity and industry sector. We also investigate how the customer portfolio of cloud and data center providers influences the DDoS threat to their infrastructure. Research Questions: • How do organizational characteristics related to value and visibility — specifically, popularity and industry sector — correlate with the threat of DDoS attacks? • How does the diversity of customer business sectors hosted by a cloud or data center provider influence the DDoS threat to its infrastructure? Methodology: We conducted a large-scale analysis of DDoS incidents inferred from network telescope data spanning five years. We estimated target visibility and value using Alexa ranks and Cisco Umbrella content categories. We also analyzed the relationship between customer sector composition and DDoS threat at the provider level. Key Findings: • Popular websites are more frequently attacked, though this pattern weakened during the COVID-19 pandemic. • Certain industry sectors face significantly higher and repeated DDoS threats. • Cloud providers serving a higher proportion of high-risk sectors are more likely to face frequent DDoS attacks.

Nowadays, the focus in 5G networks has shifted from Quality of Service (QoS) to Quality of Experience (QoE) characterisation and prediction. As a matter of fact, mobile operators are increasingly interested in measuring and/or predicting QoE Key Performance Indicators (KPIs) on their 5G networks. In this context, a recent methodology by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T) allows to characterize the level of interactivity achievable by real-time services on 5G networks, by computing a synthetic QoE KPI referred to as interactivity score (i-score). The i-score, defined as the measurable latency, continuity, and reliability of a given service, is computed by using a model that takes into account three QoS KPIs, i.e., packet trip time, jitter, and loss rate. In this paper, aiming at assessing the effectiveness of the ITU-T methodology in characterizing 5G network performance, we analyze a large-scale measurement campaign executed over two commercial 5G Non-Standalone (NSA) deployments in the city of Rome, Italy. During this campaign, traces related to radio coverage and service performance (i.e., the i-score and corresponding KPIs needed to compute it) were collected in parallel. Therefore, we use the dataset to characterize the observed i-score performance, and demonstrate that it is possible to successfully predict this KPI with machine learning techniques, using radio layer parameters and power measurements. Mobile operators could take advantage of our findings, minimizing the need for time/resource-consuming QoE tests. Ensemble methods in fact achieve an accuracy spanning from 0.79 to 0.83, with Random Forest as one of the best algorithm to predict the i-score from radio layer parameters.

Effective diabetes management is crucial to pre-venting serious complications in patients. This study employs machine learning techniques to analyze data from 11 Spanish hospital emergency departments, with the goal of improving the quality of life for individuals with type I and type II diabetes. Our model demonstrates a prediction accuracy of up to 95% for hypo- and hyperglycemia, and highlights significant insights into antidiabetic treatments. The results were validated using a decision tree and correlation plots, showing relationships between key variables. This approach shows potential for large-scale prediction of glycemic events and provides valuable support for clinical decision-making.

Software-Defined Wide Area Networks (SD-WANs) have emerged as a promising solution to address the connectivity demands of modern distributed enterprises. However, the effective application of the Software Defined Networking (SDN) paradigm in such broad and dynamic environments remains a significant challenge. In this paper, we present two novel contributions. First, we design a decentralized control plane for SD-WANs that leverages edge-based network monitoring and overlays’ configuration. Then we present a Reinforcement Learning-based orchestration plane that leverages local information for the enforcement of SD-WAN policies. Since traditional approaches suffer either a lack of scalability due to the problem’s complexity or suboptimal performance due to isolated decision-making, the proposed approach leverages a cooperative Multi-Agent Reinforcement Learning framework. Our novel cooperative approach is based on per-site agents that exchange a small amount of information to enhance performance while preserving scalability. To validate the efficacy of our proposed approach, we conducted an extensive experimental evaluation considering diverse SD-WAN scenarios. Results show that our framework is able to satisfy global network policies for a multi-site SD-WAN with different QoS requirements and cost constraints.

Phishing emails are constantly increasing their sophistication, and typical countermeasures struggle at addressing them. Attackers target our cognitive vulnerabilities with a varied set of techniques, and each of us, not trained enough or simply in the wrong moment, can be deceived and put an entire organization in trouble. To date, no study has evaluated the behavior of users when confronted with phishing emails characterized by a diverse set of features and attack strategies. We created a system called Spamley from these observations, which has the main aim of collecting and sharing data regarding such user behavior. Spamley is also meant to disseminate awareness among users. To reach these goals, we firstly analyzed the wide scientific literature on phishing. Our analysis shows that the focus of studies on phishing has more and more shifted from technical to human-oriented aspects. Building on this analysis we designed, implemented, and deployed a system comprising a web application to test user awareness about phishing, featuring a survey to identify the most interesting characteristics of users, and fueled by a large and varied set of test emails engineered to solicit the several possible cognitive vulnerabilities we all have. We describe in details the design and implementation choices, the lessons we learned, and the way we filled the gap in the available related work. Finally, we use real data from our first 500 users to show how data collected can be used for several important analyses, including which characteristics of the emails are more relevant for which cognitive vulnerability of specific groups of users. Results obtained can guide the development of novel email clients as well as tailored training programmes. Data collected is available to the scientific community for conducting further studies on the important and still unsolved issue of email phishing.

As diabetes management becomes more complicated, there is an increasing interest in understanding how to manage diabetes with physical activity. Our study aimed to investigate the role of wearable, non-invasive technologies in collecting data related to physical activity to model them via artificial intelligence methods for efficient diabetes management. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (also known as PRISMA) protocol and searched three databases, namely PubMed, Scopus, and Web of Science. Out of 960 titles, we included 32 in the full-text analysis. Results showed two main methods were used for the analysis, i.e., statistical and classification modeling. Results indicate among the employed regression methods, linear regression was used more than other methods, and the most common classification-based method for analyzing data was the Artificial Neural Network method. Assessing the quality of papers that used the classification method was done through Prediction model Risk Of Bias Assessment Tool (also known as PROBAST) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (also known as TRIPOD) tools. Based on PROBAST outcomes, although the risk of bias was low in most of the works, explaining the analyzing method specifically, the method of handling missing data needs more attention. Upon evaluating papers using the TRIPOD, it realized that there is a need to place emphasis on improving the quality of the presentation and explanation of the result. According to our review, the conjunction of non-invasive technologies and artificial intelligence is promising in managing diabetic risk factors for real-time monitoring of physical activities, enabling regular clinical intervention and optimized medical treatment.

A major health concern for men is prostate cancer. An accurate prediction of patients' conditions after surgery is essential for understanding their quality of life. For improving patient care, medical and healthcare professionals use machine learning for a variety of purposes. By using supervised machine learning algorithms, we aim to identify the most reliable predictors of patient sexual function one year after surgery. We used the EPIC-26 (Expanded Prostate Index Composite-26) questionnaire to assess the patient's quality of life and sexual function. An approximate 500 patient sample was used in our case study to test the effectiveness of this methodology. Based on demographic and clinical data collected prior to surgery, our model predicts patient self-assessment of sexual function with high accuracy one year after surgery. In order to improve and enhance the quality of the patient experience, the methodology presented can support clinical decisions.

With E-UTRA-NR Dual Connectivity, terminals can connect to 4G Long-Term Evolution and 5G New Radio networks at the same time. This technology allows using multiple bandwidths belonging to the two radio layers, enhancing the overall system performance. The system also adopts Multiple Input Multiple Output on top of the dual radio layer access. Authors predict application-layer Round-Trip Time with Machine Learning algorithms leveraging radio layer parameters such as received power and signal quality. Binary classification techniques are adopted to predict if Round-Trip Time values are above or below a threshold. The prediction is tested with real data collected in two measurement campaigns. Results show that Random Forest and Decision Tree Classifiers are the best algorithms with a precision score of respectively 0.84 and 0.92 in both measurement setups. They also evidence the radio- and physical-layer information having more importance for predicting application-layer RTT.

Software Defined Wide Area Network (SD-WAN) is rapidly becoming an attractive solution for enterprise networks as it offers several benefits such as cost efficiency, increased bandwidth, and improved application performance. However, SDWAN also brings new challenges that must be addressed for effective deployment (i.e. openness, interoperability, network automation, monitoring, QoS guarantees, scalability and security). In this paper, we highlight the criticalities of this technology and analyze the solutions proposed by the state of the art. We then present a scalable framework based on distributed Reinforcement Learning agents for guaranteeing availability and QoS to business applications. We believe that our work provides valuable insights into the opportunities and challenges of SD-WAN technology and offers new perspectives for future research in this area.

The proliferation of Internet of Things devices has resulted in an increase in security vulnerabilities and network attacks. The Mirai botnet is a well-known example of a network used for malicious activities, detected for the first time by the white-hat research group in August 2016. Since then, Mirai initiated massive DDoS attacks by scanning for and exploiting vulnerabilities in network devices. In this paper, we investigate the evolution of the Mirai botnet over a six-year period, analyzing the TCP SYN packets using Mirai signature, i.e. with TCP sequence number equal to the destination IP address. Our analysis stands out as we extensively investigate the evolution of Mirai scans over a prolonged six-year period (2016–2022). Our findings reveal that the Mirai signature is still implemented by malicious actors today, in contrast with previous works. Moreover, we observe that the number of hijacked devices and TCP SYN packets involved in the scanning phase have increased over time. We also confirm that cybercriminals generally target Telnet port 23, followed by fewer requests on Telnet port 2323. Conversely, the number of probes on the SSH ports decreases over time, followed by a subsequent increase in 2022. Lastly, we identify several ports that had not been contacted until 2018 but have since received a large number of TCP SYN packets that verify the Mirai’s signature. These ports are linked with the emergence of new variants of the Mirai botnet.

Internet of Things is a widespread technology that comprises several networking solutions for connecting things to the rest of the Internet. Understanding the characteristics of such solutions is fundamental in order to satisfy the diverse requirements of all the possible applications. The goal of this paper is to empirically evaluate and compare the performance of the most spread technologies for IoT low-power long-range communications. The parameters considered are the energy efficiency, the message losses, and the latency of a message. Obtained results show that up to 2% of messages can be lost when using LoRaWAN, but the latency is always smaller than 7 seconds. NB-IoT shows slightly larger latency values and more delivered messages than LoRaWAN. The messages sent using Sigfox are always correctly delivered, but the communication introduces delays up to 100 seconds. These results are of great importance for all the players in the IoT scenario interested in LPWAN technologies. The results of this study show how different communication technologies provide significantly different performance.

The hostilities in Ukraine have driven unprecedented forces, both from third-party countries and in Russia, to create economic barriers. In the Internet, these manifest both as internal pressures on Russian sites to (re-)patriate the infrastructure they depend on (e.g., naming and hosting) and external pressures arising from Western providers disassociating from some or all Russian customers. While quite a bit has been written about this both from a policy perspective and anecdotally, our paper places the question on an empirical footing and directly measures longitudinal changes in the makeup of naming, hosting and certificate issuance for domains in the Russian Federation.

Available bandwidth is a vital parameter for understanding network status. A huge number of tools have been proposed in literature, including general ones as well as tools specialized for specific network scenarios. In this plethora of possibilities, an expert user is currently required to select the best one according to the specific operating settings, in order to achieve accurate results without disturbing the existing traffic.In this paper we propose the use of automatic decision systems based on machine learning to substitute the expert user and choose the right tool for the current scenario. To verify if this is a viable solution, we created a custom dataset and tested different decision systems including a simple, threshold-based one and four algorithms based on machine learning: k-nearest Neighbors, Random Forest, Support Vector Machine, and Long Short-Term Memory networks. We used different features including CPU, memory, and bandwidth and verified that the decision systems based on machine learning achieve very good performance and can be considered as a promising solution for this important problem.

With the growing adoption of cloud infrastructures to deliver a variety of IT services, monitoring cloud network performance has become crucial. However, cloud providers only disclose qualitative info about network performance, at most. This hinders efficient cloud adoption, resulting in no performance guarantees, uncertainties about the behavior of hosted services, and sub-optimal deployment choices. In this work, we focus on cloud-to-user latency, i.e. the latency of network paths interconnecting datacenters to worldwide-spread cloud users accessing their services. In detail, we performed a 14-day measurement campaign from 25 vantage points deployed via Planetlab infrastructure (emulating spatially- spread users) and considering services running in distinct locations on the infrastructures of the two most popular public-cloud providers, namely Amazon Web Services and Microsoft Azure. Our experimentation allows to provide an in-depth performance characterization (based on multiple probing methods and fine-grained sampling rate) of such networks as perceived by users spread worldwide. Results show the presence of both spatial and temporal latency trends. Finally, by evaluating the advantages of multi- cloud deployments, our results also provide useful guidelines to cloud customers.

Public-cloud providers do not disclose quantitative information about the performance of their inter- datacenter networks in spite of their importance and of the growing interest they are attracting. In this paper we propose an analysis of the inter-datacenter network of the two leading providers-Amazon Web Services and Microsoft Azure- only leveraging active monitoring approaches and thus not relying on information restricted to providers. Our results show that Azure inter- datacenter infrastructure performs better than Amazon's in terms of throughput (+52%, on average). On the other hand, the performance of the two providers is comparable in terms of latency, with the exception of isolated cases. Counterintuitively, lower performance may be even related to higher costs for the customer. Network management policies that may severely impact both the performance perceived by the customers and the results of the measurement activities have been observed and characterized. Finally, a comparison with previous works shows that TCP throughput has not improved recently.

The dependence of the industry on cloud-based infrastructures has grown much faster than our understanding of the performance limits and dynamics of these environments. An aspect only marginally analyzed in the past is related to the performance of the intra-cloud network connecting the virtual machines (VMs) deployed in the same data center. The few available works either do not exhaustively describe the adopted methodology or employed different approaches causing the analyses to be hard to replicate, and the results to be hard to compare. In addition, cloud customers can today highly customize their cloud environments while previous works considered only a few of the scenarios in which a customer may operate. In this paper, we provide an intra-cloud network performance characterization of Microsoft (MS) Azure, a leading provider only preliminary investigated from this angle. We first propose and thoroughly detail a methodology to carry out similar analyses, thus encouraging its replication also in other contexts; then we apply this methodology to characterize the intra-cloud network performance in terms of maximum network throughput. More specifically, we investigate whether and how the achievable throughput between two VMs varies (i) over time; (ii) when the customer operates different decisions on VM size, network configuration, geographic region, and transport protocol; and (iii) when the customer operates the same decisions on these factors. Our analysis aims at addressing the gap existing in the literature by providing the most exhaustive and detailed results about the intra-cloud network performance for MS Azure today available.

In the context of Internet access technologies, satellite networks have traditionally been considered for specific purposes or as a backup technology for users not reached by traditional access networks, such as 3G, cable or ADSL. In recent years, however, new satellite technologies have been introduced in the market, reopening the debate on the possibilities of having high-performance satellite access networks. In this paper, we describe the testbed we set up - in collaboration with one of the main satellite operators in Europe - and the experiments we performed to evaluate and analyze the performance of both Tooway and Tooway on KA-SAT (or KASAT for short), two satellite broadband Internet access services. Also, we build a simulator to study the behavior of the traffic shaping mechanism used by the satellite operator. In terms of performance, our results show how new generation Internet satellite services are a promising way to provide broadband Internet connection to users. In terms of traffic shaping, our results shed light on the mechanisms employed by the operator for shaping user traffic and the possibilities left for the users.

Monitoring modern networks involves storing and transferring huge amounts of data. To cope with this problem, in this paper we propose a technique that allows to transform the measurement data in a representation format meeting two main objectives at the same time. Firstly, it allows to perform a number of operations directly on the transformed data with a controlled loss of accuracy, thanks to the mathematical framework it is based on. Secondly, the new representation has a small memory footprint, allowing to reduce the space needed for data storage and the time needed for data transfer. To validate our technique, we perform an analysis of its performance in terms of accuracy and memory footprint. The results show that the transformed data closely approximates the original data (within 5% relative error) while achieving a compression ratio of 20%; storage footprint can also be gradually reduced towards the one of the state-of-the-art compression tools, such as bzip2, if higher approximation is allowed. Finally, a sensibility analysis show that technique allows to trade-off the accuracy on different input fields so to accommodate for specific application needs, while a scalability analysis indicates that the technique scales with input size spanning up to three orders of magnitude.

The bursty nature of losses over the Internet is constantly asking for effective solutions. Packet interleaving or time diversity allows to cope with loss burstiness, at the cost of an additional delay. In this work, after determining the loss burstiness degree of real networks, we implement a real interleaver (we called TimeD), and we tackle the problem of how to apply such a transmission schema to UDP flows in real networks. For this aim, we propose a methodology composed of the following steps: (i) firstly, we develop a simulator to study the potential benefits of TimeD, understanding its loss decorrelation power and determining the interleaving configurations most suited to different network conditions and loss burstiness degree; (ii) then, using an emulated network, we validate TimeD and derive important operating parameters; (iii) finally, we study TimeD over a real satellite network. Thanks to this multifarious analysis we are able to move step-by-step from the theory to the practice, showing how it is possible - using TimeD - to actually decorrelate bursty losses and increase the performance of real applications.

In the field of network monitoring and measurement, the efficiency and accuracy of the adopted tools is strongly dependent on (i) structural and dynamic characteristics of the network scenario under measure and (ii) on manual fine tuning of the involved parameters. This is, for example, the case of the end-to-end available bandwidth estimation, in which the constraints of the measurement stage vary according to the use of the final results. In this work we present UANM (Unified Architecture for Network Measurement), a novel measurement infrastructure for an automatic management of measurement stages, tailored to the end-to-end available bandwidth estimation tools. We describe in details its architecture, illustrating the features we introduced to mitigate the problems affecting available bandwidth estimation in heterogeneous scenarios. Moreover, to provide evidences of UANM benefits, we present an experimental validation in three selected scenarios deployed over a real network testbed: (i) we show how UANM is able to alleviate the interferences among concurrent measures; (ii) we quantify the overhead introduced by the use of UANM; (iii) we illustrate how UANM is capable to provide more accurate results thanks to the knowledge of the network environment.

The use of multipath routing in overlay networks is a promising solution to improve performance and availability of Internet applications, without the replacement of the existing TCP/IP infrastructure. In this paper, we propose an approach to distribute data over multiple overlay paths that is able to improve Quality of Service (QoS) metrics, such as the data transfer time, loss, and throughput. By using the Imbedded Markov Chain technique, we demonstrate that the system under analysis, observed at specific instants, possesses the Markov property. We therefore cast the data distribution problem into the Markov Decision Process (MDP) framework, and design a computationally efficient algorithm named Online Policy Iteration (OPI), to solve the optimization problem on the fly. The proposed approach is applied to the problem of multipath data distribution in various wired/wireless network scenarios, with the objective of minimizing the data transfer time as well as the delay and losses. Through both intensive ns-2 simulations with data collected from real heterogeneous networks and experiments over real networks, we show the superior performance of the proposed traffic control mechanism in comparison with two classical schemes, that are Weighted Round Robin and Join the Shortest Queue.

Planetlab is widely recognized as being one of the most important Internet-scale testbeds. However, while allowing experimentations involving hundreds of hosts spread all over the world, PlanetLab still suffers of a few significant limitations. One of these limitations is the lack of heterogeneity, in particular in terms of access technologies. In this paper we describe the efforts we made, in the context of the OneLab European project, in order to mitigate this problem. In particular, we describe how we managed to integrate UMTS connectivity into a PlanetLab-based testbed. We illustrate the technical challenges we had to face, the final result we obtained, and present a case study meant to show the utility of having UMTS connectivity available in Planetlab.

Networked embedded systems are gaining more and more attention and their use in current network scenarios is of indisputable importance. Research community and industry are proposing novel embedded solutions, often based on network processors, for network connectivity, data processing and service delivery. Despite this, quantitative performance comparisons of such systems seem to be very hard to find. In this paper, we describe an experimental analysis of different boards for networked embedded systems using both general-purpose and network processors, and running both commercial and open source operating systems. The results show that network-processor based boards are able to attain very high performance when compared to boards based on x86 processors, especially when running commercial operating systems. The analysis provides a reference for the design, development, and testing of novel networked embedded systems.

Maintaining performance and reliability in wireless networks is a challenging task due to the nature of wireless channels. Multipath data transmission has been used in wired scenarios to reduce latency, improve throughput, and - when/where possible - balance the load. In this paper, we propose an approach for multipath data transmission over wireless networks. We demonstrate that the problem under study can be formulated as a Markov decision process (MDP) and we propose an algorithm called On-line Policy Iteration (OPI), to solve the formulated MDP in real time. We verified the proposed approach using simulations with ns-2 and data collected from real heterogeneous wired/wireless networks. The results indicate that we improve both delay and loss characteristics of end-to-end wireless communications outperforming the classical multi-path schemes including Round Robin and Join the Shortest Queue.

Context adaptation is certainly a promising approach in the development of fuzzy rule based systems (FRBSs). First, an initial rule base is extracted from heuristic knowledge of the application domain. Meanings of linguistic terms are defined so as to guarantee high interpretability of the FRBSs. Then, meanings are adapted to a specific context through the use of operators that, using a set of known input–output patterns, appropriately modify the corresponding fuzzy sets. The choice of the specific operators and their parameters is context based and optimized so as to obtain a good interpretability–accuracy trade-off. In this paper, we propose a set of operators that, starting from a given FRBS, adapt the FRBS to the specific context by adjusting the universes of the input and output variables, and modifying the core, the support and the shape of the fuzzy sets which compose the partitions of these universes. The operators are defined so as to preserve ordering of the linguistic terms, universality of rules, and interpretability of partitions. The choice of the parameters used in the operators is performed by a genetic optimization process aimed at maximizing the accuracy and preserving the interpretability of the FRBS. We finally describe the application of our context adaptation approach to two Mamdani fuzzy systems developed, respectively, for two different domains, namely, regression and data modeling. © 2008 Wiley Periodicals, Inc.

The long-standing vision of ubiquitous Internet access requires high-speed wireless networks that sustain 100 Mbps or more. While existing hardware already supports these speeds and they are available at single access points, measurement studies of existing mesh or multi-hop WiFi networks that cover and span larger areas report effective throughputs that are one or two orders of magnitude lower. We ask the question whether we can not already build high-speed wireless network that sustain high rates. To answer this question, we have built the MagNets high-speed WiFi backbone in the heart of Berlin. This paper presents an experimental evaluation of the single and multi-hop performance in terms of throughput, jitter, delay, packet loss, and assesses the impact of environmental factors on these parameters. Our results indicate, e.g. that some links achieve a sustained UDP throughput of up to 62 Mbps using off-the-shelf hardware supporting Super-AG modes, whereas others are limited to 4–5 Mbps due to interfering networks. In contrast, we show that the link performance is largely unaffected by environmental factors, such as day/night or social events (i.e. 2006 FIFA World Cup semi-final and final matches).

Software-based traffic generators are commonly used in experimental research on computer networks. However, there are no much studies focusing on how such instruments are accurate. Here we start a discussion reviewing the problem of using software-based traffic generators over common hardware/software, highlighting interesting issues that pose some threats to common beliefs. We started comparing the operator-requested traffic profile against the real behavior of commonly used software-based traffic generators. We aim at performing tests under different conditions and looking both at packet/bit rate and inter-packet time distribution. Preliminary results show notable differences in some cases, opening the way to interesting discussions and further investigations.

In the framework of context adaptation of fuzzy systems, a typical requirement of a contextualized system is to maintain the same interpretability as the original one. Here, we propose a novel index based on a fuzzy ordering relation to provide a measure of interpretability. Our index assesses ordering, distinguishability and coverage at the same time. We use the proposed index and the mean square error as goals of a multi-objective genetic algorithm aimed at generating contextualized Mamdani fuzzy systems with different trade-offs between the two goals. Results obtained on a synthetic data set are also discussed.

Measurement and monitoring of network topologies are essential tasks in current network scenarios. Indeed, due to their utility in planning, management, security, and reliability of network infrastructures, effective and efficient approaches and tools for discovering large topologies are gaining more and more attention from both Application Service Providers and network administrators. In this paper we propose a hybrid methodology and its implementation in a software platform we calledHynetd. We present the architecture, some novel algorithms and methods adopted in the discovery chain, and a performance evaluation over two network scenarios: a small scale test-bed and a large scale MAN in the heart of Napoli (Italy). We provide experimental results confirming and improving those previously obtained by a prototype ofHynetd. In addition a comparison with a commercial tool is presented. Achieved results, in terms of accuracy, discovery time, and traffic injected, are very encouraging in both considered scenarios.

In the study of network traffic, the collection and the processing of measurement data sets play a fundamental role. Due to the large size of typical traffic traces, their analysis is often heavy in terms of computational time and resources. In addition, even when the data sets are small, due to the intrinsic redundancy of the data, there is no need to consider the entire data sets in the processing stages. To cope with these issues, we use anentropy-based methodology to reduce network traffic data sets obtained by measurements over real networks. The off-line approach we used is based on themarginalutilityconcept, and reveals encouraging results when applied to real data captured over real networks, especially when dealing with large amounts of data. To show the applicability of our approach, we present and discuss results obtained in the analysis and characterization, at packet-level, of traffic traces from two popular network games:Counter-StrikeandAgeofMythology. Thanks to the differences between the two considered on-line games and their traffic traces we can draw pros and cons in realistic scenarios.

A long horizon end-to-end delay forecast, if possible, will be a breakthrough in traffic engineering. This paper introduces a hybrid approach to forecast end-to-end delays using wavelet transforms in combination with neural network and pattern recognition techniques. The discrete wavelet transform is implemented to decompose delay time series into a set of wavelet components, which is comprised of an approximate component and a number of detail components. Thus, it turns the problem of long horizon delay forecasting into a set of shorter horizon wavelet coefficient forecasting problems. A recurrent multi-layered perceptron neural network is applied to forecast coefficients of the wavelet approximate component, which represents the trend of the delay series. The k-nearest neighbors technique is used to forecast coefficients of the wavelet detail components, which reflect the burstiness of background traffic. The proposed approach has been verified in both simulation and over real heterogeneous networks showing promising results in terms of averaged normalized root mean square error. In addition, when compared to some existing and well known approaches it presents the superior performance.

Even if Internet traffic analysis and characterization is a fertile research area, a lot of work still must be done to study and understand the traffic characteristics of new emerging multimedia applications. Among them, an interesting category is that of multiplayer network games. This paper aims at demonstrating that, at packet level, spatial and temporal invariants exist in the traffic of such applications. For this purpose, we study Counter-Strike, a popular client/server network game, comparing results from two different networks. The effectiveness of the proposed approach is evaluated by studying statistics of both packet size and inter-packet time. Results provide a view on packet-level game traffic and they confirm that the main traffic dynamics present properties that can be generalized, independently of the observation point and time.

Context adaptation can be achieved by adjusting an initial normalized fuzzy rule-based system through the use of operators that appropriately change the representation of the linguistic variables. The choice of the specific operators and their parameters should be context-based and optimized so as to obtain a good interpretability-accuracy tradeoff. In this paper we propose a set of context adaptation operators that, starting from a given fuzzy system, adjust some of its component!, such as fuzzy set support and core, membership function shape, etc. We use a genetic tuning process for choosing the operator parameters. We finally describe the application of the proposed operators to Mamdani fuzzy systems with reference to two real examples.

Accurate identification of network elements (network bricks) composing end-to-end paths represents a novel and interesting research topic. In heterogeneous scenarios, automatic network bricks identification can improve the performance of adaptive and network-aware applications. This work proposes an approach, based on Bayesian classifiers, for the identification of network bricks belonging to a large number of real heterogeneous end-to-end paths. The identification is performed by means of measurement and off-line observation of delay, jitter, and packet loss. We introduce the term "blind identification" meaning the capability to identify network bricks, by looking at quality of service (QoS) parameters observed on the end-to-end path. We propose first insights and preliminary results regarding the identification stage, based on both concise and detailed QoS parameters statistics. Moreover, we show some results of the identification performed using a reduced set of QoS parameters