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This work presents a novel formulation for the numerical solution of optimal control problems related to nonlinear Volterra fractional integral equations systems. A spectral approach is implemented based on the new polynomials known as Chelyshkov polynomials. First, the properties of these polynomials are studied to solve the aforementioned problems. The operational matrices and the Galerkin method are used to discretize the continuous optimal control problems. Thereafter, some necessary conditions are defined according to which the optimal solutions of discrete problems converge to the optimal solution of the continuous ones. The applicability of the proposed approach has been illustrated through several examples. In addition, a comparison is made with other methods for showing the accuracy of the proposed one, resulting also in an improved efficiency.

What does crown mean for philosophers? This short contribution aims at offering a narrative of the word ‘crown’ in a few accounts of political philosophy, in order to sketch a way of construing the idea of crown through exploring its relationship to the notion of power. In particular, I shall suggest that different relationships between crown and power can fit with specific kinds of account of individual sovereignty and freedom. Suggestions shall be drawn from Thomas Hobbes, John Locke and Max Weber.

Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler parts. Assuming a modular approach to the problem, we confine our research to intent classification for an answer, given a question. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder.

Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In this work, analysing the semantic capacity of the Word2Vec algorithm, an embedding for the Italian language is produced. Parameter setting such as the number of epochs, the size of the context window and the number of negatively backpropagated samples is explored.

In this paper we discuss research issues, practical matters and legal constraints driving the design and development of lawful tools and frameworks for a real-time, automated analysis of Internet traffic, supporting detection and recovery of hidden traffic and botnets, as well as preserving the integrity and confidentiality of evidences. We propose a methodological perspective and lines of development that in our vision are the basic pillars of such a framework, to address a multi-stakeholder, multinational scenario.

Probability models have been proposed in the literature to account for "intelligent" behavior in many contexts. In this paper, probability propagation is applied to model agent's motion in potentially complex scenarios that include goals and obstacles. The backward flow provides precious background information to the agent's behavior, viz., inferences coming from the future determine the agent's actions. Probability tensors are layered in time in both directions in a manner similar to convolutional neural networks. The discussion is carried out with reference to a set of simulated grids where, despite the apparent task complexity, a solution, if feasible, is always found. The original model proposed by Attias has been extended to include non-absorbing obstacles, multiple goals and multiple agents. The emerging behaviors are very realistic and demonstrate great potentials of the application of this framework to real environments., Submitted for journal publication

Part 2: Security Testing; International audience; Penetration testing (PT) is nowadays one of the most common and used activities to evaluate a given asset’s security status. Penetration testing aims to secure networks and highlights the security issues of such networks. More precisely, PT, which is used for proactive defense and information systems protection, is a structured process, made up of various phases that typically needs to be carried out within a limited period.In this work, we first define a modular semi-automatic approach, which allows us to collect and integrate data from various exploit repositories. These data are then used to provide the penetration tester (i.e., the pentester) with information on the best available tools (i.e., exploits) to conduct the exploitation phase effectively. Also, the proposed approach has been implemented through a proof of concept based on the Nmap Scripting Engine (NSE), which integrates the features provided by the Nmap Vulscan vulnerability scanner, and allows, for each vulnerability detected, to find the most suitable exploits for this vulnerability. We remark that the proposed approach is not focused on the vulnerability mapping phase, which is carried out through Vulscan. Instead, it is focused on the automatic finding of the exploits that can be used to take advantage of the results achieved by such a phase.

The maximum network lifetime problem is a well-known and challenging optimization problem which has been addressed successfully with several approaches in the last years. It essentially consists in finding an optimal schedule for sensors activities in a wireless sensor network (WSN) aiming at maximizing the total amount of time during which the WSN is able to perform its monitoring task. In this paper, we consider a new scenario in which, in order to monitor some locations in a geographical area, the sensors need to be active for a fixed amount of time, defined as operating time slot. For this new scenario, we derive an upper bound on the maximum lifetime and propose a genetic algorithm for finding a near-optimal node activity schedule. The performance evaluation results obtained on numerous benchmark instances show the effectiveness of the proposed approach.

Nowadays, billions of people use Instant Messaging (IM) applications (called apps for short) to communicate, e.g., WhatsApp, Telegram, etc. These applications have a positive impact in social relations, allowing a real-time communication that is simple and immediate. In this way, users can be available everywhere and at any time. In the state of the art, the most popular instant messaging application in the world is definitely WhatsApp. Given the multiple operational scenarios where they are involved in, to prevent the violation of communication by malicious users, IM applications typically ensure security in terms of confidentiality, integrity and availability. Indeed, in the WhatsApp application as well as in the other IM applications, the communication between the various entities takes place in a protected manner. Therefore, it is practically impossible to break the protection of messages exchanged by such applications and find the content of such messages. On the other hand, due to their security properties, those applications are widely used also by cybercriminals. In this paper we focus on the WhatsApp application and propose an approach based on the side-channel analysis to detect some actions performed by WhatsApp users, such as the starting or rejecting of a call, the joining or leaving from/to a chat group, etc. More precisely, the approach we propose is based on the analysis of some characteristics and patterns present in the traffic generated during typical WhatsApp sessions. The proposed approach does not require particular tools or backgrounds to be completed, but only simple packet capture tools, such as WireShark. Furthermore, we point out that our approach can be very useful in the context of forensic analysis, since it complements all the other tools and methodologies typically used in the state of the art to deal with a cybercrime. Finally, the proposed approach has been tested in real usage scenarios, both as regards the communication between two (unicast) and more endpoints (multicast).

We report a number of experiments on a deep convolutional network in order to gain a better understanding of the transformations that emerge from learning at the various layers. We analyze the backward flow and the reconstructed images, using an adaptive masking approach in which pooling and nonlinearities at the various layers are represented by data-dependent binary masks. We focus on the field of view of specific neurons, also using random parameters, in order to understand the nature of the information that flows through the activation’s“holes" that emerge in the multi-layer structure when an image is presented at the input. We show how the peculiarity of the multi-layer structure is not so much in the learned parameters, but in the patterns of connectivity that are partly imposed and then learned. Furthermore, a deep network appears to focus more on statistics, such as gradient-like transformations, rather than on filters matched to image patterns. Our probes seem to explain why classical image processing algorithms, such as the famous SIFT, have provided robust, although limited, solutions to image recognition tasks.

Nowadays, modern vehicles are becoming even more connected, intelligent, and smart. A modern vehicle encloses several cyber–physical systems, such as actuators and sensors, which are controlled by electronic control units (ECUs). Such ECUs are connected through in-vehicle networks, and, in turn, such networks are connected to the Internet of Vehicles (IoV) to provide advanced and smart features. However, the increase in vehicle connectivity and computerization, although it brings clear advantages, it introduces serious safety problems that can also endanger the life of the driver and passengers of the vehicle, as well as that of pedestrians. Such problems are mainly caused by the security weaknesses affecting the controller area network (CAN) bus, used to exchange data between ECUs. In this article, we provide two algorithms that implement a data-driven anomaly detection system. The first algorithm (cluster-based learning algorithm), is used to learn the behavior of messages passing on the CAN bus, for base-lining purposes, while the second one (data-driven anomaly detection algorithm) is used to perform real-time classification of such messages (licit or illicit) for early alerting in the presence of malicious usages. The experimental results, obtained by using data coming from a real vehicle, have shown that our approach is capable of performing better than other anomaly detection-based approaches.

Resource-constrained systems are becoming more and more common as users migrate from PCs to mobile devices and as IoT systems enter the mainstream. At the same time, it is not acceptable to reduce the level of security hence it is necessary to accommodate the required security into the system-imposed resource constraints. This paper introduces BAdDroIds, a mobile application leveraging machine learning for detecting malware on resource constrained devices. BAdDroIds executes in background and transparently analyzes the applications as soon as they are installed, i.e., before infecting the device. BAdDroIds relies on static analysis techniques and features provided by the Android OS to build up sound and complete models of Android apps in terms of permissions and API invocations. It uses ad-hoc supervised classification techniques to allow resource-efficient malware detection. By exploiting the intrinsic nature of data, it has been possible to implement a state-of-the-art data-driven model which provides deep insights on the detection problem and can be efficiently executed on the device itself as it requires a very limited computational effort. Besides its limited resource footprint, BAdDroIds is extremely effective: An extensive experimental evaluation shows that it outperforms the currently available solutions in terms of accuracy, which is around 99 percent.

In the last months, the market for personal wearable devices has been booming significantly, and, in particular, smartwatches are starting to assume a fundamental role in the Bring Your Own Device (BYOD) arena as well as in the more general Internet of Things (IoT) ecosystem, by acting both as sensitive data sources and as user identity proxies. These new roles, complementing the more traditional personal assistance and telemetry/tracking ones, open new perspectives in their integration in complex IoT-based critical infrastructures such as e-payment, health care monitoring, and emergency systems, as well as in their usage as remote control facilities in smart services. Users can access their IoT devices at any time from any place through smartwatches. We argue that this new scenario calls for a strengthened and more resilient authentication of users on these devices, despite their limitations in terms of dimensions and hardware constraints that may considerably affect the usability of security mechanisms. In this article, we present an innovative authentication scheme targeted at smartwatches, namely CirclePIN, that provides both resilience to most common attacks and a high level of usability in tests with real users.

Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.

Ransomware represent one of the most dangerous threats in the modern era. Indeed, they are able to block the access to the data (e.g., images, documents, etc.) in infected systems. After the infection of a system, a ransomware asks the user of this system for the payment of a ransom, to regain the access to the blocked data. However, sometimes also the payment of such a ransom does not guarantee the recovery of all the blocked files. Despite several methods and countermeasures have been proposed in the state of the art to protect systems against ransomware, we remark that a system could be still infected by ransomware, mainly because of human errors. In the state of the art several techniques have been proposed to mitigate the issues caused by ransomware. A mitigation technique consists in the usage of data recovery tools (e.g., file carving tools, etc.) to recover the lost data of the victim infected by ransomware. The aim of this work is to introduce an extensible methodology to evaluate the performance of file carving tools for the recovery of files lost due to a ransomware attack in a simulated environment, where by the term performance we basically intend the number of files recovered. The proposed methodology can be useful to estimate the performance of such tools in real-life scenarios, in which one or more systems are the victims of ransomware attacks. The main objective of this paper is to estimate how effective these tools are against ransomware and, on the other hand, how much time you have available in trying to recover files on a system affected by ransomware. Furthermore, in this paper we provide some best practices for recovering files from a system affected by ransomware. Finally, we present and discuss the preliminary results achieved by using the proposed methodology on three popular ransomware, that is, WannaCry, JigSaw and Cerber.

There is an increasing involvement of the Internet of Things (IoT) in many of our daily activities, with the aim of improving their efficiency and effectiveness. We are witnessing the advent of smart cities, in which IoT is exploited to improve the management of a city's assets, as well as smart factories, where IoT is paving the way for the forth industrial revolution. These applications and many other ones imply several non-functional requirements to be satisfied by the adopted IoT solution, where security assumes paramount importance. Secure communications among the IoT nodes are strongly needed due to the use of wireless technologies that are easy to eavesdrop, in order to steal valuable information. Accordingly, confidentiality is a fundamental prerequisite, but the existing solutions based on transport-level encryption are ineffective, while the ones with application-level encryption may be too expensive in terms of energy consumption. In this work, we propose a series of solutions and methods to achieve confidentiality with end-to-end guarantees, by using group-based keys within the context of a clustered and distributed key management framework. We have implemented such solutions on top of TinyOS, and assessed their achievable quality by means of the TOSSIM simulator.

The right choice of features to be extracted from individual or aggregated observations is an extremely critical factor for the success of modern network traffic classification approaches based on machine learning. Such activity, usually in charge of the designers of the classification scheme is strongly related to their experience and skills, and definitely characterizes the whole approach, implementation strategy as well as its performance. The main aim of this work is supporting this process by mining new and more expressive, meaningful and discriminating features from the basic ones without human intervention. For this purpose, a novel autoencoder-based deep neural network architecture is proposed where multiple autoencoders are embedded with convolutional and recurrent neural networks to elicit relevant knowledge about the relations existing among the basic features (spatial-features) and their evolution over time (temporal-features). Such knowledge, consisting in new properties that are not immediately evident and better represent the most hidden and representative traffic dynamics can be successfully exploited by machine learning-based classifiers. Different network combinations are analyzed both from a theoretical perspective, and through specific performance evaluation experiments on a real network traffic dataset. We show that the traffic classifier obtained by stacking the autoencoder with a fully-connected neural network, achieves up to a 28% improvement in average accuracy over state-of-the-art machine learning-based approaches, up to a 10% over pure convolutional and recurrent stacked neural networks, and 18% over pure feed-forward networks. It is also able to maintain high accuracy even in the presence of unbalanced training datasets.

The 5G technology is providing totally new opportunities to mobile communications and their related applications, fostering the creation of service ecosystems and business models that will significantly revolutionize our daily life. With its massive scalability, resiliency and time-sensitive performance demands it will significantly increase the footprint of the underlying transport infrastructure by requiring new control plane facilities capable of supporting the specific needs characterizing end-to-end connections between mobile devices and their targets. Such facilities, built on top of an optical backhaul/fronthaul layer must rely on new routing mechanisms able to meet 5G Ultra-Reliable Low-Latency Communication requirements. Accordingly, this work presents a novel multi-objective distributed online routing scheme for converged fiber-radio transport infrastructures. Such scheme is able to guarantee the 5G reliability and delay optimization needs, while simultaneously taking into account the more traditional network engineering objectives, aiming at satisfying the maximum number of communication requests by taking the best from the already made infrastructural investments. Its effectiveness has been demonstrated through extensive simulation experiments that resulted in quite promising outcomes, enforcing confidence in future industrial developments.

Internet of Things (IoT) refers to the complex systems generated by the interconnections among widely available objects. Such interactions generate large networks, whose complexity needs to be addressed to provide suitable computationally efficient approaches. In this article, we propose a distributed local community detection algorithm based on specific properties of community center expansions (DLCD-CCE) for large-scale complex networks. The algorithm is evaluated via a prototype system, based on Spark, to verify its accuracy and scalability. The results demonstrate that compared to the typical local community detection algorithms, DLCD-CCE has better accuracy, stability, and scalability, and effectively overcomes the problem that existing algorithms are sensitive to the location of initial seeds.

In the last few decades, genetic algorithms (GAs) demonstrated to be an effective approach for solving real-world optimization problems. However, it is known that, in presence of a huge solution space and many local optima, GAs cannot guarantee the achievement of global optimality. In this work, in order to make GAs more effective in finding the global optimal solution, we propose a hybrid GA which combines the classical genetic mechanisms with the gradient-descent (GD) technique for local searching and constraints management. The basic idea is to exploit the GD capability in finding local optima to refine search space exploration and to place individuals in areas that are more favorable for achieving convergence. This confers to GAs the capability of escaping from the discovered local optima, by progressively moving towards the global solution. Experimental results on a set of test problems from well-known benchmarks showed that our proposal is competitive with other more complex and notable approaches, in terms of solution precision as well as reduced number of individuals and generations.

In recent years, the automotive industry has made significant investments in the Internet of Things (IoT) paradigm, aiming to develop technologies and services for connected vehicles that focus on user-oriented solutions, providing unique and tailored driving experiences. Nowadays, the vehicles are connected to the car manufacturer network, the road operator networks, and the Internet. Such interconnections are used for infotainment applications and driving assistance services, such as real-time HD-maps, and road safety applications. For these reasons, the concept of the Internet of Vehicles (IoVs) is increasingly emerging and, vehicles become Internet terminals exposed to cyber-attacks. In this context, the main weakness is given by the Controller Area Network (CAN) protocol. This protocol, which governs the in-vehicle network, was designed to reduce transmission error problems and minimize latency times. However, it does not employ any security facility to protect the communication. In particular, one of the main issues of this protocol is that it uses unencrypted communication. Improving the security of this protocol is necessary while preserving its performance in terms of communication efficiency. This paper investigates the possibility of using lightweight block ciphers to secure in-vehicle devices such as microcontrollers, which have constrained hardware and software capabilities. The results obtained have shown that this proposal while guaranteeing a high degree of safety, has a negligible impact on the vehicle’s performance.

In complex contexts, people need to adapt their behavior to interact with the surrounding environment to reach the intended destination or avert collisions. Motion dynamics should therefore include both social and kinematic rules. The proposed analysis aims at defining a linear dynamic model to predict future positions of different types of agents, namely pedestrians and cyclists, observing a limited number of frames. The dynamics are defined in terms of artificial potentials fields (APFs) obtained by static (e.g., walls, doors or benches) and dynamic (e.g, other agents) elements to produce attractive and repulsive forces that influence the motion. A linear combination of such forces affects the resulting behavior. We exploit the context using a semantic scene segmentation to derive static forces while the interactions between agents are defined in terms of their reciprocal physical distances. We conduct experiments both on synthetic and on subsets of publicly available datasets to corroborate the proposed model.

Thrombotic complications are quite frequent during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, especially during severe disease and in hospitalized patients. The incidence of thrombotic complications in mild disease is not clear and probably few cases have been identified. We reported a case of a 60-year-old man with no previous history admitted to our unit for splenic infarct and a floating clot into the descending aorta without signs of severe disease. Several mechanisms to clarify prothrombotic state have been described. However, the exact prevalence of thromboembolic phenomenon is probably underestimated. There is no consensus about the treatment and the indications for preventing these complications in non-hospitalized patients. Thrombotic events should be suspected also in healthy patients with symptoms suggestive for coronavirus disease infection but without severe COVID-19 pneumonia. Further analysis should be performed to stratify the risk in nonhospitalized patients and the indications for prophylactic treatment.

Nowadays, there is a growing demand of middleware solutions for reliable event notification over the Internet due to several key industrial projects aiming at integrating existing legacy systems and enhancing their functionalities. Reliable event notification is mainly realized by means of retransmissions, with the consequent worsening of the performance and traffic load. Traditionally, the opposite approach of spatial redundancy is not used due to the tunability, scalability and flexibility issues of its implementation schemes. In this paper, we propose a game theoretic formulation of Forward Error Correction (one of the mean schemes to introduce spatial redundancy), in order to resolve these mentioned issues. Moreover, we introduce distributed strategic learning for the optimal formulation of the payoff functions in the game, and for the effective adaptivity in response to the possible variations in the experienced loss patterns. We prove the quality of this solution by using a series of simulations run on OMNET++.

Minimizing the latency in communications is assuming a paramount importance in 5G architectures, mainly for mission-critical applications associated to the IoT environment. However, the need of interacting with cloud-based applications introduces unacceptable delays essentially due to wide area transport activities over the Internet. Many solutions are emerging for facing this problem ranging from cloudlets to fog and edge computing architectures, pushing flexible virtualization environments nearer to the end devices, ideally within the base stations. Unfortunately, most of these infrastructures are managed by cloud providers in a network- oblivious way so that in presence of multiple nodes operating at the edge level in the same area no latency optimization strategies are taken into consideration. Accordingly, we present a novel latency- aware edge node selection framework based on a multi-objective bin packing problem transposed in the mobile edge computing scenario where the selection criterion is driven by both latency optimization and load balancing, managed according to a time slotted scheme.

Internet of Things (IoT) is one of the key technologies paving the way for the next industrial revolution named as Industry 4.0, since it promises to realize smarter factories by optimizing costs and productivity. Traditionally, the adopted communication protocols among the sensors are required to manage the large scale of the infrastructure in terms on the high number of interconnected nodes and the massive volume of exchanged data. However, due to the key role of those Industrial IoT in exchanging business critical data, such protocols need to also provide high resiliency guarantees to the message exchange, with as few delivery misses as possible. The publish/subscribe interaction pattern and the protocol implementing it are a technically sound approach for achieving scalability and elasticity, thanks to their intrinsic decoupling among the interacting nodes. However, they are often unsuitable in their current form, because they provide only best-effort delivery guarantees, or they adopt naive solutions to achieve resilient communication, especially when wireless networks are used. This work presents a clustered lightweight gossiping algorithm for resilient event-based communications among the sensors, without requiring a pre-deployed brokering infrastructure supporting the adopted publish/subscribe protocol. A simulation-based assessment has been performed in order to empirically show the improvements in terms of successfully delivered notification without the excessive costs of the state-of-the-art solutions available in the literature.

Stackelberg games may reveal to be extremely useful in supporting decisions in attack-defense scenarios. We call such games Security Stackelberg games. They are characterized by two kinds of players: the defender, who defines his strategy in advance, and the attacker, who follows the defender's decisions. Security Stackelberg games may be used to model a typical cloud security scenario, where the provider has to expose its defense strategy and the attackers act by following the provider's actions. In this work we define a model based on Stackelberg games that enables the automatic selection of provider-level security decisions in Cloud Computing environments. Potential Cloud attack scenarios are modeled as nonzero-sum Security Stackelberg games between the attacker and the Cloud provider. This allows the estimation of the strategy that minimizes the reward for the attacker and maximizes the gain of the defender. The model has been experimentally verified to be effective in automatically determining defense strategies from the cloud provider perspective.

The current mobile app development practice, like other large-scale network-centric software projects, is characterized by the use of event notification facilities supporting the exchange of effective and efficient data flows between the applications front-end, usually located on customers terminal equipment and the back-end services available within the cloud. In order to avoid the need of setting up the notification infrastructure from scratch for any new application, many cloud service providers and mobile system manufacturing companies provide cloud-based messaging solutions. Such solutions, however, are often characterized by vulnerabilities that can be exploited to compromise the mobile applications security by violating the privacy and integrity in their operations. In this work, we analyze these issues and present how the blockchain technology can be helpful in dealing with such threats.

Nowadays, most real-world datasets suffer from the problem of imbalanced distribution of data samples in classes, especially when the number of data representing the larger class (majority) is much greater than that of the smaller class (minority). In order to solve this problem, various types of undersampling or oversampling techniques have been proposed to create a dataset with equal number of samples in each class by reducing or increasing the number of samples in majority or minority classes, respectively. Ensemble classifiers use multiple learning algorithms to enhance the accuracy of classification. Based on the results, combining undersampling or oversampling methods with ensemble classifiers can result in models with better performance. By using both clustering and new undersampling methods, the present study aimed to propose a novel clustering-based undersampling method to create a balanced dataset. This method uses k-means clustering algorithm for clustering the data, Mahalanobis distance to analyze samples distance in each cluster to centroid, and a selection method that preserves the pattern of data distribution in each cluster. Regarding the experimental results obtained by 44 benchmark datasets from KEEL repository, the proposed approach performed better than that of seven state-of-the-art approaches.

Mining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by Big Data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered. In this article we propose and analyse a novel method to extract and build fragments of Bayesian networks (BNs) from unstructured large data sources. The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources. The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios.

Currently, our society is undergoing a radical change due to the increasing pervasive use of ICT within all of its processes. Such an evolution has been triggered by the advent of the Internet of Things vision, where smart sensing devices can be integrated in the daily objects surrounding any human being. The increasing demand of dealing with the big data generated, managed, and stored by the applications built on top of such novel sensory networks has called for innovative architectures, such as edge and fog computing, which have not yet been fully standardized. Considering the large scale and complexity of these architectures, testing a software artifact to be deployed in the nodes composing an edge-fog ecosystem could be extremely challenging. Pure simulated environments and real testbeds could be not representative enough of realistic scenarios or unacceptably expensive. We explore such issues and present a pseudo-dynamic testing approach, where a portion of the experimental scenario is simulated, while the edge and fog nodes under test are emulated or executed in a real environment.

One regularity condition for the classical Cramér-Rao lower bound (CRLB) of an unbiased estimator to hold - that the support of the likelihood function (LF) should be independent of the parameter to be estimated - has recently been relaxed to the case of parameter-dependent support as long as the LF is continuous at the boundary of its support. For the case where the LF is not continuous on the boundary of its support, a new modified CRLB - designated the Cramér-Rao-Leibniz lower bound (CRLLB) as it relies on the Leibniz integral rule - has also been presented for the scalar parameter case. The present work derives the multidimensional CRLLB for the case of LF with parameter-dependent support by applying the general Leibniz integral rule to complete the framework of the CRLLB.

The efficiency of broadcast propagation schemes can significantly condition the performance and effectiveness of mobile ad-hoc networks. Probabilistic forwarding strategies are considered between the most promising optimization approaches due to their inherent simplicity that makes them easy to implement on any platform, very fast and computationally inexpensive. In this work, we present a novel adaptive probabilistic approach for containing broadcast propagation in reactive network discovery based on modeling the broadcast propagation behavior as harmonic spherical waves generated by a single source, and hence determining the forwarding probability by considering how the intensity of a wave attenuates with its distance from the source. We used discrete event simulation to demonstrate how the proposed strategy can introduce appreciable benefits on the overall performance of modern broadcast-based reactive routing protocols, resulting in a real breakthrough in ad-hoc environments characterized by an high degree of mobility.

Traffic matrices, abstract representations of demand, are essential for network operators endeavoring to model, measure, maintain, and improve the efficiency of their complex and heterogeneous architectures. Traffic matrix estimation consists in inferring a traffic matrix from link-level measurements. Provoked by the need to enable agile deployment of new services while, at the same time, slashing operating expenditure and energy consumption, the trend in telecommunications is to shift functionality from physical appliances to virtualized services. We analyze the effects of this landscape change on traffic matrices, their dynamics, and their estimation, indicating some new challenges and problems that will arise in all the associated modeling, analysis and evaluation activities.

Currently, cloud computing is the target business environment for many enterprises and government organizations. However, despite the huge potential gains that can be achieved, security represents a fundamental issue, which prevents the massive cloud adoption in mission-critical Information Technology sectors. The most common security issues are amplified in the cloud environment since new complex features, with their inherent weaknesses, enter into the problem space, particularly those associated to multitenancy and elasticity. Thus, new threats, such as the energy-related denial-of-service attacks against large-scale cloud infrastructures, may involve not only the quality of the delivered services but also their operational costs in terms of energy bill. The longer is the time necessary to identify such attacks, the heavier is the impact on the overall energy consumption and, consequently, on the associated expenses. This work presents a detailed analysis of such new sophisticated menaces, by focusing on those that are specifically tailored to originate the worst-case energy demands by leveraging properly crafted low-rate traffic patterns in order to ensure stealth operations. We present some strategies exploiting the cloud flexibility in order to increase in a fraudulent way the overall energy consumption and analyze their impact within large-scale cloud infrastructures. This should help cloud providers in understanding such weaknesses and highlighting their root causes, as well as in providing some hints on how they can counter these subtle security issues.

Gossiping is a widely known and successful approach to reliable communications, tolerating packet losses and link crashes. It has been extensively used in several middleware kinds, such as event notification services and application domains, like infrastructures for air traffic management, power grid control, health information exchange, just to cite some of them. Despite achieving a high loss-tolerance and scalability degrees, gossiping is affected by degraded performances and heavy traffic loads on the network. For this reason, it may be not optimal in applications where reliability must be provided jointly with timeliness and/or in congestion-prone networks. The crucial aspect for improving a gossiping scheme is deciding which nodes should receive a gossiping message, and our driving idea is to adopt a distributed strategic learning logic to determine such nodes in an efficient manner. This is able to resolve gossiping's weakness points and to achieve better performance and reduced traffic loads. This paper describes how to introduced strategic learning in a gossip scheme so as to determine the best set of nodes that can be used to send gossip messages and to optimize their utility. Such a solution has been experimentally assessed through a set of simulations demonstrating the effectiveness of the proposal.

Differently from attacks aimed at gaining control of the resources of a mobile device, energy-related attacks have the essential goal of significantly raising the energy demand on the victim side, without apparently affecting its activities. It is a fundamental point to highlight how such a goal can possibly be accomplished by mounting well-known canonical attacks and waiting for the system defenses to detect and stop them. In such an endeavor, defenses require additional amounts of energy which eventually render the mobile device completely useless. In the System on Chip (SoC) architecture, many components, each with a separate function, are integrated. As the total energy adsorption is the composition of the energy consumptions of individual components, each component may be the target of an energy-based attack. This work analyzes and discusses the effects and implication of new energy-based Denial of Service attacks based on the proper solicitation of hardware-layer encode/decode capabilities by using specifically crafted multi-media resources, in order to introduce an anomalous battery drain, and hence significantly shorten the overall battery lifetime in mobile smart devices. These attacks do not require physical access nor compromise of the target device, and they take advantage of new HTML5 functionalities that can be properly triggered during normal browsing activity. The more significant result is that the Digital Signal Processor (DSP) offers an exploitable attack surface to be kept into consideration early in the design process. Countermeasures include special filtering rules that prevent “irrelevant” content from reaching the DSP or, in a more far-reached perspective, the introduction of a power-draw controller on the SoC with the purpose of monitoring energy consumption and raising alerts.

Nowadays, several advanced ICT infrastructures demand multicast services able to jointly provide a high degree of reliability and good performances despite of failure occurrences. The current practice to tolerate failures in such services mainly consists in the adoption of methods based on temporal redundancy, where messages are successfully delivered to all the interested destinations by means of retransmissions. However, in such methods, a high degree of reliability is typically obtained at the expenses of severe performance fluctuations and instability. On the contrary, approaches that make use of forward error correction imply a more performance-friendly behavior; however, the effective tuning of such approaches is a critical and challenging task, which has limited their applicability in large-scale multicast services. This paper is aimed at presenting a solution to resolve this issue by effectively tuning the redundancy degree to be applied, so as to be able to tolerate the losses affecting the multicast communications without an excessive, and un-needed, workload imposed over the network. To this aim, we drawn from the experience of layered multicast, commonly applied to multimedia content delivery, for addressing such tunability problem. Specifically, we detail how to apply layered multicast within the context of a well known multicast service and how to determine the correct redundancy degree to be applied. We also make use of extensive simulations to evaluate the quality of the proposed approach in terms of reliability, performance and overhead, and to compare it with the best retransmission-based approach available in the literature.

Anastomotic leakage is considered the commonest major complication after surgery for rectal cancer.Patients who underwent laparoscopic LAR or ULAR for rectal cancer were recruited. The primary outcome was the incidence of the AL during 30 days postoperative.Fifty-nine consecutive patients were included in the study. Fifty-three patients underwent LAR with stapled colorectal anastomoses, while the remaining 6 patients underwent ULAR with hand-sewn coloanal anastomoses. The median duration of operation was 195 minutes (range; 120-315). The defunctioning ileostomy was created in 24 (7%) patients. Overall, there was no recorded mortality. Only 10 (17%) patients developed complications. There were only 4 patients who developed AL. Three patients had a subclinical AL as they had defunctioning ileostomy at the time of the initial procedure, the diagnosis was made by CT with rectal contrast. They were treated conservatively with transanal anastomotic drainage under endoscopic guidance. One patient had a clinically significant AL, demonstrated as a peritonitis. This patient required reoperation during which pelvic abscess was drained, resection of the previous anastomosis, and hartmann's colostomy was performed.Standardization of a definition, as well as, criteria for the diagnosis of AL, will help in comparison of the results and the surgical techniques in order to optimize the required care offered to rectal cancer patients. On expert hands, it is feasible to perform a laparoscopic sphincter-saving total mesorectal excision, additionally, it provides the advantages of a clear view of the deep pelvis and facilitates a precise sharp dissection.Anastomosis, Anastomotic Leakage, Rectal cancer, Total mesorectal excision.

A novel pairwise comparison algorithm that determines whether a pair of ellipsoids is overlapping, touching, or separated by exploiting two new Mahalanobis distance conditions is presented. These conditions are filtering steps prior to a previously proposed method that transforms the intersection query problem into a least squares minimization over a sphere. The algorithm introduced here is shown to be more computationally efficient than previous methods, which either solve the least squares minimization over a sphere or check the signs of the roots of a fourth order polynomial. The proposed method may be applied in cases when an ellipsoid is an appropriate safety region. The resulting algorithm is useful in applications including, but not limited to, collision detection and avoidance.

The complexity of current cyber infrastructures requires specialized security-oriented skills to be acquired by a variety of different actors, in order to defend critical systems and sensitive data against emerging security threats and attacks. In order to facilitate this process, advanced cybersecurity education and training programs should be considered, as well as platforms and tools for simulating realistic training scenarios should be exploited. This paper presents an open-source solution for simulating cyber infrastructures, and reproducing realistic Internet of Things (IoT) scenarios, with specific focus on Edge applications. It can be used for the implementation of new cooperative and competitive cybersecurity training exercises and skills for different application domains, as well as validate solutions that can be used to prevent, detect, mitigate, recovery, and evaluate the attack impact.

The management of iron overload in thalassemia has changed dramatically since the implementation of magnetic resonance imaging, which allows detection of preclinical iron overload and prevention of clinical complications. This study evaluated the effect of deferasirox (DFX), the newest once-daily oral chelator, on cardiac function, iron overload and cardiovascular events over a longer follow up in a "real world" setting. Longitudinal changes in cardiac magnetic resonance T2*, cardiac function parameters and cardiovascular clinical events were assessed in a cohort of 98 TM patients exposed to DFX for a mean of 6.9 years (range 1.8-11.6 years). No cardiac death or incident heart failure occurred. Cardiac T2* significantly increased (+2.6 ± 11.9 msec; P = 0.035) in the whole population, with a significantly greater increase (+11.6 ± 15.5 msec, P = 0.019) in patients with cardiac iron overload (T2*20 ms). A significant improvement in left-ventricular ejection fraction (LVEF) (from 50.6 ± 6 to 60.2 ± 5; P = 0.001) was observed in 11 (84.6%) out of 13 patients who normalized cardiac function (LVEF56%). Arrhythmias were the most frequent cardiac adverse event noted but none led to DFX discontinuation. Our data indicate that DFX is effective in maintaining cardiac iron level in the normal range and in improving cardiac iron overload. No heart failure or cardiac death was reported over this longer observation up to 12 years. For the first time, a DFX-induced improvement in LVEF was observed in a subgroup of patients with abnormal cardiac function at baseline, a preliminary observation which deserves further evaluation.

The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1.

In pervasive/ubiquitous computing environments, interacting users may evaluate their respective trustworthiness by using historical data coming from their past interactions. Nevertheless, when two users are at the first interaction, they have no historical data involving their own activities to be analyzed, and then use information (recommender-data) provided by other users (recommenders) who, in the past, have had interactions with one of the involved parties. Although this approach has proven to be effective, it might fail if dishonest recommenders provide unfair recommender-data. Indeed, such unfair data may lead to skewed evaluations, and therefore either increase the trustworthiness of a malicious user or reduce the one of a honest user in a fraudulent way. In this work, we propose an algorithm for identifying false recommender-data. Our attention is explicitly focused on recommender-data rather than recommenders. This because some recommenders could provide recommender-data containing only a limited (but specific) set of altered information. This is used by dishonest recommenders as a tactic to avoid being discovered. The proposed algorithm uses association rules to express a confidence-based measure (reputation rank), which is used as a reliability ranking of the recommender-data. The resulting approach has been compared with other existing ones in this field, resulting more accurate in finding out unfair recommender-data sent by dishonest recommenders.

Nowadays, the growing need to connect modern vehicles through computer networks leads to increased risks of cyberattacks. The internal network, which governs the several electronic components of a vehicle, is becoming increasingly overexposed to external attacks. The Controller Area Network (CAN) protocol, used to interconnect those devices is the key point of the internal network of modern vehicles. Therefore, securing such protocol is crucial to ensure a safe driving experience. However, the CAN is a standard that has undergone little changes since it was introduced in 1983. More precisely, in an attempt to reduce latency, the transfer of information remains unencrypted, which today represents a weak point in the protocol. Hence, the need to protect communications, without introducing low-level alterations, while preserving the performance characteristics of the protocol. In this work, we investigate the possibility of using symmetric encryption algorithms for securing messages exchanged by CAN protocol. In particular, we evaluate the using of lightweight ciphers to secure CAN-level communication. Such ciphers represent a reliable solution on hardware-constrained devices, such as microcontrollers.