EFFICIENT AND SECURE ALGORITHMS FOR MOBILE CROWDSENSING THROUGH PERSONAL SMART DEVICES.

The success of the modern pervasive sensing strategies, such as the Social Sensing, strongly depends on the diffusion of smart mobile devices. Smartwatches, smart- phones, and tablets are devices capable of capturing and analyzing data about the user’s context, and can be exploited to infer high-level knowledge about the user himself, and/or the surrounding environment. In this sense, one of the most relevant applications of the Social Sensing paradigm concerns distributed Human Activity Recognition (HAR) in scenarios ranging from health care to urban mobility management, ambient intelligence, and assisted living. Even though some simple HAR techniques can be directly implemented on mo- bile devices, in some cases, such as when complex activities need to be analyzed timely, users’ smart devices should be able to operate as part of a more complex architecture, paving the way to the definition of new distributed computing paradigms. The general idea behind these approaches is to move early analysis to- wards the edge of the network, while relying on other intermediate (fog) or remote (cloud) devices for computations of increasing complexity. This logic represents the main core of the fog computing paradigm, and this thesis investigates its adoption in distributed sensing frameworks. Specifically, the conducted analysis focused on the design of a novel distributed HAR framework in which the heavy computation from the sensing layer is moved to intermediate devices and then to the cloud. Smart personal devices are used as processing units in order to guarantee real-time recognition, whereas the cloud is responsible for maintaining an overall, consistent view of the whole activity set. 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. Then, the fog-based architecture allowed the design and definition of a novel HAR technique that combines three machine learning algorithms, namely k-means clustering, Support Vector Machines (SVMs), and Hidden Markov Models (HMMs), to recognize complex activities modeled as sequences of simple micro- activities. The capability to distribute the computation over the different entities in the network, allowing the use of complex HAR algorithms, is definitely one of the most significant advantages provided by the fog architecture. However, because both of its intrinsic nature and high degree of modularity, the fog-based system is particularly prone to cyber security attacks that can be performed against every element of the infrastructure. This aspect plays a main role with respect to social sensing since the users’ private data must be preserved from malicious purposes. Security issues are generally addressed by introducing cryptographic mechanisms that improve the system defenses against cyber attackers while, at the same time, causing an increase of the computational overhead for devices with limited resources. With the goal to find a trade-off between security and computation cost, the de- sign and definition of a secure lightweight protocol for social-based applications are discussed and then integrated into the distributed framework. The protocol covers all tasks commonly required by a general fog-based crowdsensing application, making it applicable not only in a distributed HAR scenario, discussed as a case study, but also in other application contexts. Experimental analysis aims to assess the performance of the solutions described so far. After highlighting the benefits the distributed HAR framework might bring in smart environments, an evaluation in terms of both recognition accuracy and complexity of data exchanged between network devices is conducted. Then, the effectiveness of the secure protocol is demonstrated by showing the low impact it causes on the total computational overhead. Moreover, a comparison with other state-of-art protocols is made to prove its effectiveness in terms of the provided security mechanisms

SMCP: a Secure Mobile Crowdsensing Protocol for fog-based applications

The 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

SpADe: Multi-Stage Spam Account Detection for Online Social Networks

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this paper we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time

Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: a DES instance

Encryption 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

A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices

The 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

Smartphone data analysis for human activity recognition

In 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 userâs 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 userâs 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

Twitter spam account detection by effective labeling

In 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

Twitter Analysis for Real-Time Malware Discovery

In 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

Modeling Efficient and Effective Communications in VANET through Population Protocols

Vehicular Ad-hoc NETworks (VANETs) enable a countless set of next-generation applications thanks to the technological progress of the last decades. These applications rely on the assumption that a simple network of vehicles can be extended with more complex and powerful network infrastructure, in which several Road Side Units (RSUs) are employed to achieve application-specific goals. However, this assumption is not always satisfied as in many real-world scenarios it is unfeasible to have a conspicuous deployment of RSUs, due to both economic and environmental constraints. With the aim to overcome this limitation, in this paper we investigate how the only Vehicle-to-Vehicle (V2V) communications can be effectively exploited to share data among the vehicles about an event of interest, such as vehicular traffic. In this sense, we propose a novel communication schema based on the Population Protocol model that allows vehicles to be efficiently updated about a given event. Experimental analysis aims to evaluate the performance of the proposed schema, while also highlighting the benefits it might bring in VANETs applications

A Fog-Assisted System to Defend Against Sybils in Vehicular Crowdsourcing

Technological advancements in vehicular transportation systems have led to the growth of novel paradigms, in which vehicles and infrastructures collaborate to infer high-level knowledge about phenomena of interest. Vehicular Social Network (VSN) is one such paradigm in which vehicular network entities are considered as part of an Online Social Network (OSN), paving the way for new services derived from social context. Although vehicular crowdsourcing has tremendous benefits, its deployment in real systems requires to solve important challenges including defense against Sybil attacks. This paper proposes a novel fog-assisted system that uses SybilDriver to minimize the presence of Sybil entities in VSN-based crowdsourcing applications. The proposed system exploits the characteristics of Vehicular Ad-hoc NETworks (VANETs) and OSNs to effectively recognize Sybils, and the adoption of fog computing helps reduce the overall network overhead by processing data closer to the vehicles. We perform detailed experiments on real-world publicly available datasets primarily to assess the effectiveness of SybilDriver against different Sybil attack strategies. Our experimental results show that SybilDriver detects Sybils with higher performance than state-of-the-art techniques under different settings. Furthermore, an evaluation of the fog architecture in terms of message complexity demonstrates low impact on the network overhead