3,650 research outputs found
A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices
No full textThe 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
No full textThe 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
Twitter Analysis for Real-Time Malware Discovery
No full textIn recent years, the increasing number of cyber-attacks has gained the development of innovative tools to quickly detect new threats. A recent approach to this problem is to analyze the content of Social Networks to discover the rising of new malicious software. Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. The subscribers can insert messages, called tweet, that are usually related to international news. In this work, we present a system for real-time malware alerting using a set of tweets captured through the Twitter API’s, and analyzed by means of a Bayes naïve classifier. Then, groups of tweets discussing the same topic, e.g, a new malware infection, are summarized in order to produce an alert. Tests have been performed to evaluate the performance of the system and results show the effectiveness of our implementation
Twitter spam account detection by effective labeling
No full textIn 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
Milano consolato nell' elettione a questo arciuescouado, e promotione alla sagra porpora dell' eminentissimo Federico Visconti : colla sua solennissima entrata seguita a' 11. genaro 1682 e fontioni antecedenti /
No full textFrontispiece coat of arms of Milan, engraved by Federico Agnelli.Signatures: pi⁴ A-G⁴ H⁴(-H4).Mode of access: Internet.Binding: limp vellum. Author & title written on spine
SmartWave: A Smart Platform for Marine Environmental Monitoring
No full textIn recent years, the interest in the study of seas and oceans has dramatically increased as they are considered of primary importance for forecasting catastrophic events or for supporting blue economy, as well as the marine tourism, improving the tourist reception or enhancing any marine-related activity. This led to the development of IT platforms that allow to monitor the marine environment and provide a number of services to different kinds of final users, whether they are private individuals interested in the status of the seas, or companies whose business depends on the marine environmental monitoring. The main limitations of current platforms are due to such a difference between free trials, which often focus only on specific aspects of deep waters, and subscriptions, which provide analyzes whose reliability is generally not proportional to the costs. This paper presents SmartWave, a project funded by Regione Sicilia (European Regional Development Fund), that aims to develop a novel IT platform to observe and predict phenomena that characterize the marine environment, while also providing the consumer with a unified portal to collect, access and analyze marine-related information. To achieve this goal, one of the main challenges of this project is to aggregate and standardize heterogeneous data from multiple sources in order to offer very accurate information to private or business consumers
A Hybrid Recommender System for Cultural Heritage Promotion
No full textAssisting users during their cultural trips is paramount in promoting the heritage of a territory. Recommender Systems offer the automatic tools to guide users in their decision process, by maximizing the adherence of the proposed contents with the particular preferences of every single user. However, traditional recommendation paradigms suffer from several drawbacks which are exacerbated in Cultural Heritage scenarios, due to the extremely wide range of users behaviors, which may also depend on their different educational backgrounds. In this paper, we propose a Hybrid recommender system which combines the four most common recommendation paradigms, namely collaborative filtering, popularity-, knowledge-, and content-based, according to different hybridization strategies. Experimental evaluation shows the versatility of the hybrid recommender with respect to the other paradigms adopted individually
Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: a DES instance
No full textEncryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential attack to the Data Encryption Standard (DES) which, despite being one of the methods that has been most thoroughly analyzed, is still of great interest to the scientific community since its vulnerabilities may have implications on other ciphers
Smartphone data analysis for human activity recognition
No full textIn recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the userâ s context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a participatory sensing paradigm where userâ s feedbacks on recognised activities are exploited to update the inner models of the system. Experimental results show the effectiveness of our solution as compared to other state-of-the-art techniques
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