1,721,122 research outputs found
A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices
Full text linkThe diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog computing paradigm to move heavy computation from the sensing layer to intermediate devices and then to the cloud. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Experimental analysis aims to evaluate the performance of the entire platform in terms of accuracy of the recognition process while also highlighting the benefits it might bring in smart environments
SMCP: a Secure Mobile Crowdsensing Protocol for fog-based applications
Full text linkThe possibility of performing complex data analysis through sets of cooperating personal smart devices has recently encouraged the definition of new distributed computing paradigms. The general idea behind these approaches is to move early analysis towards the edge of the network, while relying on other intermediate (fog) or remote (cloud) devices for computations of increasing complexity. Unfortunately, because both of their distributed nature and high degree of modularity, edge-fog-cloud computing systems are particularly prone to cyber security attacks that can be performed against every element of the infrastructure. In order to address this issue, in this paper we present SMCP, a Secure Mobile Crowdsensing Protocol for fog-based applications that exploit lightweight encryption techniques that are particularly suited for low-power mobile edge devices. In order to assess the performance of the proposed security mechanisms, we consider as case study a distributed human activity recognition scenario in which machine learning algorithms are performed by users’ personal smart devices at the edge and fog layers. The functionalities provided by SMCP have been directly compared with two state-of-the-art security protocols. Results show that our approach allows to achieve a higher degree of security while maintaining a low computational cost
HDDroid: Federated Hyperdimensional Computing for Mobile Malware Detection
Full text linkMobile malware poses significant security and privacy risks, hence effective detection methods are crucial. Graph-based representations of mobile applications have been shown to be well-suited for this task. However, traditional
graph-based machine learning techniques are computationally expensive and unsuitable for on-device analysis.
Nevertheless, off-device analysis raises privacy concerns, making on-device analysis combined with decentralized
learning approaches like Federated Learning (FL) an attractive alternative. Hyperdimensional Computing (HDC)
offers efficient graph classification on resource-constrained mobile devices. This work introduces HDDroid, an
FL framework leveraging HDC to detect malicious software via function call graph analysis. HDDroid’s novel
online encoding strategy reduces memory usage, enabling large graph analysis on mobile devices. Additionally,
HDDroid’s improved model aggregation strategy enhances model robustness and classification accuracy, achieving
state-of-the-art performance in distributed learning scenarios
Twitter spam account detection by effective labeling
Full text linkIn the last years, the widespread diffusion of Online Social Networks (OSNs) has enabled new forms of communications that make it easier for people to interact remotely. Unfortunately, one of the first consequences of such a popularity is the increasing number of malicious users who sign-up and use OSNs for non-legit activities. In this paper we focus on spam detection, and present some preliminary results of a system that aims at speeding up the creation of a large-scale annotated dataset for spam account detection on Twitter. To this aim, two different algorithms capable of capturing the spammer behaviors, i.e., to share malicious urls and recurrent contents, are exploited. Experimental results on a dataset of about 40.000 users show the effectiveness of the proposed approach
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Efficient tree construction for the multicast problem
No full textA new heuristic for the Steiner Minimal Tree problem is presented here. The method described is based on the detection of particular sets of nodes in networks, the “Hot Spot” sets, which are used to obtain better approximations of the optimal solutions. An algorithm is also proposed which is capable of improving the solutions obtained by
classical heuristics, by means of a stirring process of
the nodes in solution trees. Classical heuristics and an enumerative method are used CIS comparison terms in the experimental analysis which demonstrates the goodness of the heuristic discussed in this paper
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A fuzzy approach for the network congestion problem
No full textIn the recent years, the unpredictable growth of the Internet has moreover pointed out the congestion problem, one of the problems that historicallyha ve affected the network. This paper deals with the design and the evaluation of a congestion control algorithm which adopts
a FuzzyCon troller. The analogyb etween Proportional Integral (PI) regulators and Fuzzycon trollers is discussed and a method to determine the scaling factors of the Fuzzycon troller is presented. It is shown that
the Fuzzycon troller outperforms the PI under traffic conditions which are different from those related to the operating point considered in the design
Secure e-Voting in Smart Communities
No full textNowadays, digital voting systems are growing in importance. This is an especially sensitive area, because elections can directly affect democratic life of many smart communities. The goal of digital voting systems is to exploit ICT technologies to improve the security and usability of traditional electoral systems. In this work we present a secure electronic voting system that guarantees the secrecy, anonymity, integrity, uniqueness and authenticity of votes, while offering a user-friendly experience to voters, putting them at ease through the use of technologies familiar to them. To ensure these fundamental security requirements, the system fully separates the registration and voting phases and does not collect information on users, making it impossible to determine the identity of whoever cast each vote. Only the electoral supervisor, during the tallying phase, can decipher the electronic ballot papers, which are also totally anonymous. We consider universities to be one of the most representative smart communities, and for this reason we used the case study of university elections held on our campus to test the system. The experiments carried out tested the system in increasingly challenging scenarios, and were carried out by volunteer students and university staff members
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