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A cholecystoparietal fistula is an uncommon complication of gallstone disease as a result of neglected gallbladder disease).The subcutaneous abdominal wall abscess, derived from this condition, might be wide and hard to treat, especially in elderly and debilitated patients. The best management of cholecystoparietal fistula depends on its etiology and may require medical, surgical, or endoscopic treatment. Negative Pressure Wound Therapy (NPWT) is a valuable support therapy that can improve the prognosis of the disease and the patient’s outcome. We report the case of an 89-year-old female patient affected by a spontaneous cholecystoparietal fistula with a wide abdominal wall abscess treated by a one-stage surgical approach combined with NPWT over the resulting skin loss.

Due to their open nature and popularity, Android-based devices have attracted several end-users around the World and are one of the main targets for attackers. Because of the reasons given above, i...

Invoice financing has been a steadily growing component of the financing market as a whole for the last few years, and, in 2016, it became the third largest financing market. Nonetheless, the risk of frauds is still very high, and most solutions proposed so far are based on private, proprietary platforms that cannot match the global nature of such a market. Even the most recent proposals based on blockchain are mainly adopting a private, permissioned blockchain due to the lack of confidentiality in public blockchain. In this article, we propose an Invoice financing platform based on a public blockchain supporting both fully open and group-restricted auctioning of invoices. We addressed the confidentiality issue by storing the confidential data encrypted in IPFS and the corresponding hash in the smart contract hosted on Ethereum blockchain. Our blockchain-based solution ensures data confidentiality and benefits from the main properties of the public blockchain required in Invoice financing systems, such as transparency, immutability, trustworthiness, and security. Furthermore, our platform introduces a reputation system based on the past behavior of entities, computed using the blockchain global ledger. Such a reputation system allows insurance companies to modulate the cost of the insurance contracts they offer. This combination guarantees the complete transparency and tamperproofness of a public blockchain, while it allows reducing insurance costs and fraud possibilities.

Due to their open nature and popularity, Android-based devices represent one of the main targets for malware attacks that may adversely affect the privacy of their users. Considering the huge Android market share, it is necessary to build effective tools able to reliably detect zero-day malware on these platforms. Therefore, several static and dynamic analysis methods based on Neural Networks and Deep Learning have been proposed in the literature. Despite machine learning can be considered the most promising approach for classifying applications into malware or legitimate ones, its success strongly depends on the choice of the right features used for building the detection model. This is definitely not an easy task that requires a systematic solution. Accordingly, this work represents the sequences of API calls invoked by apps during their execution as sparse matrices looking like images (API-images), which can be used as fingerprints of the apps’ behavior over time. We also used autoencoders to autonomously extract the most representative and discriminating features from these matrices, that, once provided to an artificial neural network-based classifier have shown to be effective in detecting malware, also when the network is trained on a reduced number of samples. Experimental results show that the resulting framework is able to outperform more complex and sophisticated machine learning approaches in malware classification.

Smart personal devices are assuming a fundamental role in the ubiquitous communication and computing arena. They provide new sophisticated cameras and new visual search interfaces and facilities that can drastically improve their presence and role in complex IoT-based critical infrastructures, such as healthcare monitoring and emergency systems, or remote access control facilities and smart authentication services. This new scenario calls for strong secure and resilient visual query mechanisms for these devices. In this work we propose an innovative secure visual search system, which is well-suited for ubiquitous computing scenarios empowered by modern smart personal devices. More precisely, we show how to insert, at the visual data acquisition time, a watermark inside the already compressed descriptor characterizing an MPEG-CDVS data stream used in visual queries, to make it possible to decode the watermark on the server side in order to improve the robustness against image-based identity spoofing. Such a security enforcement solution may be practical in several real-life applications involving visual queries performed from personal trusted devices, and it is particularly suitable in all those application domains that require performing visual queries with a high degree of security. It has been extensively tested and achieved satisfactory results: the presence of such a watermark does not affect the image matching performance and functionality.

In non-destructive testing of aerospace structures’ defects, the tests reliability is a crucial issue for guaranteeing security of both aircrafts and passengers. Most of the widely recognized approaches rely on precision and reliability of testing equipment, but also the methods and techniques used for processing measurement results, in order to detect defects, may heavily influence the overall quality of the testing process. The effectiveness of such methods strongly depends on specific field knowledge that is definitely not easy to be formalized and codified within the results processing practices. Although many studies have been conducted in this direction, such issue is yet an open-problem. This work describes the use of Genetic Programming for the diagnosis and modeling of aerospace structural defects. The resulting approach aims at extracting such knowledge by providing a mathematical model of the considered defects, which can be used for recognizing other similar ones. Eddy-Current Testing has been selected as a case study in order to assess both the performance and functionality of the whole framework, and a publicly available dataset of specific measures for aircraft structures has been considered. The experimental results put into evidence the effectiveness of the proposed approach in building reliable models of the aforementioned defects, so that it can be considered a successful option for building the knowledge needed by tools for controlling the quality of critical aerospace systems.

In the last years, the number of frauds in credit card-based online payments has grown dramatically, pushing banks and e-commerce organizations to implement automatic fraud detection systems, performing data mining on huge transaction logs. Machine learning seems to be one of the most promising solutions for spotting illicit transactions, by distinguishing fraudulent and non-fraudulent instances through the use of supervised binary classification systems properly trained from pre-screened sample datasets. However, in such a specific application domain, datasets available for training are strongly imbalanced, with the class of interest considerably less represented than the other. This significantly reduces the effectiveness of binary classifiers, undesirably biasing the results toward the prevailing class, while we are interested in the minority class. Oversampling the minority class has been adopted to alleviate this problem, but this method still has some drawbacks. Generative Adversarial Networks are general, flexible, and powerful generative deep learning models that have achieved success in producing convincingly real-looking images. We trained a GAN to output mimicked minority class examples, which were then merged with training data into an augmented training set so that the effectiveness of a classifier can be improved. Experiments show that a classifier trained on the augmented set outperforms the same classifier trained on the original data, especially as far the sensitivity is concerned, resulting in an effective fraud detection mechanism.

With the proliferation of mobile devices characterizing modern cyber-physical systems, service switching and handoff over large coverage areas become key aspects of the Internet of Things (IoT), mainly when remotely controlling and interacting with mission-critical autonomous vehicles that potentially may cover quite large distances such as driverless cars and Unmanned Aerial Vehicles (UAVs). These requirements can now be fully satisfied by the widespread Fast handover for Proxy Mobile IPv6 (F-PMIPv6) technology, that can be yet considered as a cornerstone in emerging 5G communications, but, unfortunately, such an approach only supports homogeneous handover, that may result in a nontrivial problem due to the heterogeneity in mobile communications technologies characterizing the available cyber-physical solutions and IoT network access devices. Recently, many researchers developed efficient solutions for the integration of F-PMIPv6 and Media Independent Handover (MIH) to allow fast handover in a highly heterogeneous mobile network. However, these models lack the security features which are necessary to protect IoT devices during handoffs. In this paper, a new security protocol, MIH-based secure cross-layer handover protocol for Fast Proxy Mobile IPv6 networks (MIH-SPFP), is proposed, incorporating the features of Secure Protocol for Fast-PMIPv6 (SPFP) into F-PMIPv6-MIH and reducing the security risks during the handover. The proposed solution also provides low latency by reducing the re-authentication path during the inter-Mobile Access Gateway (MAG) handovers. The security of the proposed protocol has been analyzed by using Burrows–Abadi–Needham (BAN) logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and its performance has been evaluated through numerical simulation by selecting “Marathon Broadcasting” as a case study. Results show that the proposed protocol not only effectively secures the handover process but is also more efficient compared with the standard MIH handover solution.

Government agencies rely more and more heavily on the availability of flexible and intelligent solutions for the interception and analysis of Internet-based telecommunications. Unfortunately, the global lawful interception market has been recently put into a corner by the emerging sophisticated encryption, obfuscation and anonymization technologies provided by modern overlay communication infrastructures. To face this challenge, this work proposes a novel strategy for defeating the anonymity of traffic flows, collected within and at the exit of these anonymizing networks, relying on distributed flow-capture, characterization and correlation attacks driven by wavelet-based multi-resolution analysis. Such a strategy, starting from a properly formalized attack model, results in an effective and promising framework that can be easily deployed on real-life network equipment and can potentially scale by working according to different distribution/parallelization scenarios.

With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

Background Surgical strategies are being adapted to face the COVID‐19 pandemic. Recommendations on the management of acute appendicitis have been based on expert opinion, but very little evidence is available. This study addressed that dearth with a snapshot of worldwide approaches to appendicitis. Methods The Association of Italian Surgeons in Europe designed an online survey to assess the current attitude of surgeons globally regarding the management of patients with acute appendicitis during the pandemic. Questions were divided into baseline information, hospital organization and screening, personal protective equipment, management and surgical approach, and patient presentation before versus during the pandemic. Results Of 744 answers, 709 (from 66 countries) were complete and were included in the analysis. Most hospitals were treating both patients with and those without COVID. There was variation in screening indications and modality used, with chest X‐ray plus molecular testing (PCR) being the commonest (19·8 per cent). Conservative management of complicated and uncomplicated appendicitis was used by 6·6 and 2·4 per cent respectively before, but 23·7 and 5·3 per cent, during the pandemic (both P, The COVID‐19 pandemic required reorganization of surgical services, affecting patients with common surgical diseases including acute appendicitis. No evidence is available on the topic. This study found global variation in screening policies, use of personal protective equipment and intraoperative directives. There has been increased adoption of non‐operative management and open appendicectomy. Hands off

Despite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try to shed some light on various issues focusing on a typical dataset. It is shown that the learning rate prevents the exact mapping of the co-occurrence matrix, that Word2Vec is unable to learn syntactic relationships, and that it does not suffer from the problem of overfitting. Furthermore, through the creation of an ad-hoc network, it is also shown how it is possible to improve Word2Vec directly on the analogies, obtaining very high accuracy without damaging the pre-existing embedding. This analogy-enhanced Word2Vec may be convenient in various NLP scenarios, but it is used here as an optimal starting point to evaluate the limits of Word2Vec.

Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors.

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.

This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges.

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.

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.

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.

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.

Mobile cellular communications are experiencing an exponential growth in traffic load on Long Term Evolution (LTE) eNode B (eNB) components. Such load can be significantly contained by directly sharing content among nearby users through device-to-device (D2D) communications, so that repeated downloads of the same data can be avoided as much as possible. Accordingly, for the purpose of improving the efficiency of content sharing and decreasing the load on the eNB, it is important to maximize the number of simultaneous D2D transmissions. Specially, maximizing the number of D2D links can not only improve spectrum and energy efficiency but can also reduce transmission delay. However, enabling maximum D2D links in a cellular network poses two major challenges. First, the interference between the D2D and cellular communications could critically affect their performance. Second, the minimum quality of service (QoS) requirement of cellular and D2D communication must be guaranteed. Therefore, a selection of active links is critical to gain the maximum number of D2D links. This can be formulated as a classical integer linear programming problem (link scheduling) that is known to be NP-hard. This paper proposes to obtain a set of network features via deep learning for solving this challenging problem. The idea is to optimize the D2D link schedule problem with a deep neural network (DNN). This makes a significant time reduction for delay-sensitive operations, since the computational overhead is mainly spent in the training process of the model. The simulation performed on a randomly generated link schedule problem showed that our algorithm is capable of finding satisfactory D2D link scheduling solutions by reducing computation time up to 90% without significantly affecting their accuracy.

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.