The increased adoption of the Internet Protocol (IP) in ICSs has made these systems vulnerable to the same security risks that are present in traditional IT environments. The legacy nature of ICSs and their unique operational requirements make them vulnerable to security threats that are different from those in IT environments. In this paper, we describe a protocol, named ArpON, which is able to wipe out in quasi real time any ARP cache poisoning attempt, thus making it ineffective. Contrarily to solutions presented in the literature for contrasting ARP cache poisoning, ArpON incurs in low operational costs, is backward compatible, transparent to the ARP protocol and does not use any HW feature nor cryptography functionality. We also model and validate ArpON in the OMNET[Formula: see text] network simulator. The simulation results show that ArpON is effective in avoiding ARP poisoning, and its communication overhead is negligible with respect to classical ARP protocol.

Smartphones and wearable devices, along with Artificial Intelligence, can represent a game-changer in the pandemic control, by implementing low-cost and pervasive solutions to recognize the development of new diseases at their early stages and by potentially avoiding the rise of new outbreaks. Some recent works show promise in detecting diagnostic signals of COVID-19 from voice and coughs by using machine learning and hand-crafted acoustic features. In this paper, we decided to investigate the capabilities of the recently proposed deep embedding model L3-Net to automatically extract meaningful features from raw respiratory audio recordings in order to improve the performances of standard machine learning classifiers in discriminating between COVID-19 positive and negative subjects from smartphone data. We evaluated the proposed model on 3 datasets, comparing the obtained results with those of two reference works. Results show that the combination of L3-Net with hand-crafted features overcomes the performance of the other works of 28.57% in terms of AUC in a set of subject-independent experiments. This result paves the way to further investigation on different deep audio embeddings, also for the automatic detection of different diseases.

Since the nineties, the Man-in-The-Middle (MITM) attack has been one of the most effective strategies adopted for compromising information security in network environments. In this article, we focus our attention on ARP cache poisoning, which is one of the most well-known and more adopted techniques for performing MITM attacks in Ethernet local area networks. More precisely, we will prove that, in network environments with at least one malicious host in the absence of cryptography, an ARP cache poisoning attack cannot be avoided. Subsequently, we advance ArpON, an efficient and effective solution to counteract ARP cache poisoning, and we use a model-checker for verifying its safety property. Our main finding, in accordance with the above impossibility result, is that the only event that compromises the safety of ArpON is a cache poisoning that nevertheless is removed by ArpON itself after a very short period, thus making it practically infeasible to perpetrate an ARP cache poisoning attack on network hosts where ArpON is installed.

Abstract We develop quantifier elimination procedures for fragments of higher order logic arising from the formalization of distributed systems (especially of fault-tolerant ones). Such procedures can be used in symbolic manipulations like the computation of pre/post images and of projections. We show in particular that our procedures are quite effective in producing counter abstractions that can be model-checked using standard SMT technology. In fact, very often in the current literature verification tasks for distributed systems are accomplished via counter abstractions. Such abstractions can sometimes be justified via simulations and bisimulations. In this work, we supply logical foundations to this practice, by our technique for second order quantifier elimination. We implemented our procedure for a simplified (but still expressive) subfragment and we showed that our method is able to successfully handle verification benchmarks from various sources with interesting performances.

The vast amounts of data generated in wireless industrial networked deployments introduce significant challenges on the data distribution process to consumer nodes within the timeframes imposed by the requirements of the Industry 4.0 paradigm. Using technological and methodological enablers, we can compose centralized or decentralized data distribution methods, which are able to help meeting the data requirements of the industrial applications. In this paper, using the technological enablers of WirelessHART, RPL and the methodological enabler of proxy selection as building blocks, we compose the protocol stacks of four different methods (both centralized and decentralized) for data distribution in wireless industrial networks over the IEEE 802.15.4 physical layer. Although there have been several comparisons of relevant methods in the recent literature, we identify that most of those comparisons are either theoretical, or based on abstract simulation tools, unable to uncover the specific, detailed impacts of the methods to the underlying networking infrastructure. We implement the presented methods in OMNeT++ and we evaluate their performance via a detailed simulation analysis. Interestingly enough, we demonstrate that the careful selection of a limited set of proxies for data caching in the network can lead to increased data delivery success rate and low data access latency.

The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. On the one hand, users tend to be always connected for sharing experiences and conducting their social interactions with friends and acquaintances, through so-called Mobile Social Networks, further improving their social inclusion. On the other hand, the pervasiveness of communication infrastructures spreading data (cellular networks, direct device-to-device contacts, interactions with ambient devices as in the Internet-of-Things) makes compulsory the deployment of solutions able to filter off undesired information and to select what content should be addressed to which users, for both (i) better user experience, and (ii) resource saving of both devices and network. In this work, we propose a novel framework for pervasive social networks, called Pervasive PLIERS (p-PLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. p-PLIERS exploits the recently proposed PLIERS tag-based recommender system (Arnaboldi et al., 2016) as a context reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. p-PLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes’ knowledge during proximity contacts and through device-to-device communications. We evaluated p-PLIERS by simulating its behavior in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD’15), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterize the generation and sharing of user contents.

In the era of pervasive mobile computing, human encounters can be leveraged to enable new forms of social interactions mediated by the personal devices of individuals. In this framework, emerging needs, such as content dissemination, social discovery and question&answering, advocate the raising of novel communication paradigms where the binding content-recipients is not provided by the sender (in the classical IP addressing style), but directly executed by specific recipients with interest on it. This allows tagged contents to be freely advertised on the network according to a content-driven approach; human encounters drive the information towards potential recipients that extract it from the stream when content type and personal interest match. This very active research area has recently produced a few preliminary solutions to this networking problem; they inherently confine message delivery inside a specific location and/or community. This covers only a part of users needs, as emerging from everyday life experience and recent studies in human sciences. This paper proposes a novel communication protocol, named InterestCast, or ICast, solving the problem for a wide range of social scenarios and applying to a delay tolerant ad hoc network whose nodes are the personal device of moving individuals, possibly interacting with fixed road-side devices. The protocol is able to chase users interests decoupling content tags from locations and social communities. The main advantages the proposal achieves are: it ensures remarkable performance results; it is simple and, thus, it is feasible and keeps computational and networking costs low; it preserves users privacy.

Opportunistic Networks (OppNets) offer a very volatile and dynamic networking environment. Several applications proposed for OppNets - such as social networking, emergency management, pervasive and urban sensing - involve the problem of sharing content amongst interested users. Despite the fact that nodes have limited resources, existing solutions for content sharing require that the nodes maintain and exchange large amount of status information, but this limits the system scalability. In order to cope with this problem, in this paper we present and evaluate a solution based on cognitive heuristics. Cognitive heuristics are functional models of the mental processes, studied in the cognitive psychology field. They describe the behavior of the brain when decisions have to be taken quickly, in spite of incomplete information. In our solution, nodes maintain an aggregated information built up from observations of the encountered nodes. The aggregate status and a probabilistic decision process is the basis on which nodes apply cognitive heuristics to decide how to disseminate content items upon meeting with each other. These two features allow the proposed solution to drastically limit the state kept by each node, and to dynamically adapt to both the dynamics of item diffusion and the dynamically changing node interests. The performance of our solution is evaluated through simulation and compared with other solutions in the literature.

Rank-based policies represent a promising approach for designing message forwarding algorithms that meet the needs of opportunistic networks. In fact, they combine low computation and communication costs with good performance in terms of both latency and delivery rates. Nonetheless, they highly depend on the mobility scenario relevant to the user, and a forwarding policy with good performances in heterogeneous settings has yet to be designed. In this paper, we propose to provide each mobile device with novel autonomic observation and reasoning components according to the following objectives: enable the device (i) to achieve awareness about the behavior of the mobility scenario it is moving in, and (ii) to identify the role played by the device within the set of other moving devices. These components are combined into a self-configuring forwarding algorithm that uses them to locally install both the utility function and the relevant settings suitable for the sensed configuration. Through of extensive simulations, this paper shows that by properly discriminating between roles it is possible to derive a self-configuring forwarding mechanism that constantly performs well in different mobility settings.

The workshop is sponsored by IEEE and IEEE Computer society, and jointly organized by the EU FET-PerAda SOCIALNETS and EU FP7-FIRE SCAMPI projects.

Mobile computing services are becoming highly personalized and influenced by user location, social attitudes and interests. This trend is often synthesized with the term behavior-awareness. Behavioral services characterize most of the applications of Opportunistic Networks and advocate a single communication paradigm: multicast. Despite that, multicast is less studied than unicast and broadcast. We believe that there is not correspondence between problem relevance and research attention. This paper focuses on the multicast best effort service in opportunistic networks with the aim of better understanding the problem and defining the guidelines for the design of a novel multicast protocol.

We introduce a wait-and-chase scheme that models the contact times between moving agents within a connectionist construct. The idea that elementary processors move within a network to get a proper position is borne out both by biological neurons in the brain morphogenesis and by agents within social networks. From the former, we take inspiration to devise a medium-term project for new artificial neural network training procedures where mobile neurons exchange data only when they are close to one another in a proper space (are in contact). From the latter, we accumulate mobility tracks experience. We focus on the preliminary step of characterizing the elapsed time between neuron contacts, which results from a spatial process fitting in the family of random processes with memory, where chasing neurons are stochastically driven by the goal of hitting target neurons. Thus, we add an unprecedented mobility model to the literature in the field, introducing a distribution law of the intercontact times that merges features of both negative exponential and Pareto distribution laws. We give a constructive description and implementation of our model, as well as a short analytical form whose parameters are suitably estimated in terms of confidence intervals from experimental data. Numerical experiments show the model and related inference tools to be sufficiently robust to cope with two main requisites for its exploitation in a neural network: the nonindependence of the observed intercontact times and the feasibility of the model inversion problem to infer suitable mobility parameters.

The differentiated services model is the emerging standard to provide Quality-of-Service (QoS) support for multimedia applications in the future Internet. This model involves bandwidth broker agents performing admission control and network configuration functionalities. A great deal of effort has been recently devoted to investigate viable approaches to the implementation of mechanisms that automatically perform the bandwidth broker functions, yet no standard policy has been proposed so far. In this paper we propose a distributed measurement-based protocol that performs admission control functionalities for multicast traffic in diff-serv networks. The protocol supports dynamic changes of the multicast group membership, operates on-demand, and supports the premium service. We prove that the proposed protocol performs an effective and efficient admission control function.

We describe a reliable broadcast protocol for mobile multihop packet radio networks.The reliable broadcast service ensures that all the hosts in a group deliver the same set of messages to the upper layer.It represents the building block to construct fault tolerant distributed applications,, whose demand is growing in a mobile environment too.The protocol is intended for use in networks where the host mobility arbitrarily changes the network connectivity and where the rate of change is not so fast as to impose flooding as the only option.In this context, our protocol is a iirst attempt to show that a mechanism more flexible than spanning tree and more efficient than flooding is possible and a service such as the reliable broadcast can be obtained.The protocol is constructed on top of a multi-hop, multicluster architecture.It provides an exactly once message delivery semantics and tolerates communication failures and host mobility.Temporaneous disconnections and network partitions are also tolerated under the assumption that they are eventually repaired.

A multicast protocol that is independent of the subnetwork and can operate on the top of a basic datagram network is described. The protocol provides reliable communication among the members of a group nd guarantees that each message sent to a group G is delivered to all active, i.e. both correct and faulty, processes in G or to none of them, and that all the members of G consistently decide on the same progressive order to process messages. The focus is mainly on the design of the protocol. The protocol architecture to which it belongs is described, and some simulation results are given. An outline of the protocol algorithm, which uses a centralized approach based on the rotating coordinator paradigm, allows processes to asynchronously decide on a given value, and recovers processes from failures through history buffers, is also given.>

In this paper we present a solution to the causal reliable multicast problem. User processes generate separate sequences of messages and specify the causal relation among them according to some application need; the algorithm ensures that the messages within the same sequence are delivered to all active, i.e. both correct and faulty, processes in the group, or to none of them, and are processed according to their causal order. Messages belonging to different sequences can be concurrently processed.This problem has few solutions presented in literature; in common with a part of them, the algorithm we describe has the centralized approach and the use of history buffers to recover from omission failures. The differences mainly concern the mechanism we devised to recover from crash failures, that avoids resorting to specialized protocols. As a consequence, under failure conditions, the algorithm performs better than other proposals in terms of both network load and throughput without affecting the performances under reliable conditions. Further, it allows to implement the most general interpretation of causality and it does not require any particular service to the underlying transport protocol.