Cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these assets, and nowadays, cryptocurrency exchanges process transactions for over 100 billion US dollars per month. Despite this, many cryptocurrencies have low liquidity and are highly prone to market manipulation. This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it has new vitality in the loosely regulated market of cryptocurrencies. Groups of highly coordinated people systematically arrange this scam, usually on Telegram and Discord. We monitored these groups for more than 3 years, detecting around 900 individual events. We report on three case studies related to pump and dump groups. We leverage our unique dataset of the verified pump and dumps to build a machine learning model able to detect a pump and dump in 25 seconds from the moment it starts, achieving the results of 94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that hit the news in the first months of 2021, when a Reddit community inflated the price of the GameStop stocks (GME) by over 1,900% on Wall Street, the world’s largest stock exchange. Later, other Reddit communities replicated the operation on the cryptocurrency markets. The targets were DogeCoin (DOGE) and Ripple (XRP). We reconstruct how these operations developed and discuss differences and analogies with the standard pump and dump. We believe this study helps understand a widespread phenomenon affecting cryptocurrency markets. The detection algorithms we develop effectively detect these events in real-time and helps investors stay out of the market when these frauds are in action.

In this paper we are concerned with geolocating the anonymous crowds of Dark Web forums. We do not focus on single users, but on the crowd as a whole. We work in two directions: The first idea is to exploit the time of all posts in the Dark Web forums to build profiles of the visiting crowds and to match the crowd profiles to that of users from known regions. Then, we develop a new dataset to detect the native language of the crowds to support and integrate this match. We assess the effectiveness of our methodology on the standard web and two Dark Web forums with users of known origin, and apply it to three controversial anonymous Dark Web forums. We believe that this work helps the community better understand the Dark Web from a sociological point of view and supports the investigation of authorities when the security of citizens is at stake.

In the last years, cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these securities and nowadays cryptocurrency exchanges process transactions for over 100 billion US dollars per month. However, many cryptocurrencies have low liquidity and therefore they are highly prone to market manipulation schemes.In this paper, we perform an in-depth analysis of pump and dump schemes organized by communities over the Internet. We observe how these communities are organized and how they carry out the fraud. Then, we report on two case studies related to pump and dump groups. Lastly, we introduce an approach to detect the fraud in real time that outperforms the current state of the art, so to help investors stay out of the market when a pump and dump scheme is in action.

Most users have several Internet names. On Face-book or LinkedIn, for example, people usually appear with the real one. On other standard websites, like forums, people often use aliases to protect their real identities with respect to the other users, with no real privacy against the web site and the authorities. Aliases in the Dark Web are different: users expect strong identity protection.In this paper, we show that using both "open" aliases (aliases used in the standard Web) and Dark Web aliases can be dangerous per se. Indeed, we develop tools to link Dark Web to open aliases. For the first time, we perform a massive scale experiment on real scenarios. First between two Dark Web forums, then between the Dark Web forums and the standard forums. Due to a large number of possible pairs, we first reduce the search space cutting down the number of potential matches to a small set of candidates, and then on the selection of the correct alias among these candidates. We show that our methodology has excellent precision, from 87% to 94%, and recall around 80%.

One year following the entry into force of the GDPR, all websites and data controllers have updated their procedures to store users' data. The GDPR does not only cover how and what data should be saved by the service providers, but it also guarantees an easy way to know what data are collected and the freedom to export them. In this paper, we carry out a comprehensive study on the right to access data provided by Article 15 of the GDPR. We examined more than 300 data controllers, requesting access to personal data to each of them. We found that almost each data controller has a slightly different procedure to fulfill the request and several ways to provide data back to the user, from a structured file like CSV to a screenshot of the monitor. We measure the time needed to complete the access data request and the completeness of the information provided. After this phase of data gathering, we analyze the authentication process followed by the data controllers to establish the identity of the requester. We find that 50.4% of the data controllers that handled the request have flaws in their procedures of identifying users or in their phase of sending the data, exposing users to new threats, even if these data controllers store data in compliance with the GDPR. Our surprising and undesired results show that, in its present deployment, the GDRP has actually decreased the privacy of users of web services.

Dark Web platforms like the infamous Silk Road market, or other cyber-criminal or terrorism related forums, are only accessible by using anonymity mechanisms like Tor. In this paper we are concerned with geolocating the crowds accessing Dark Web forums. We do not focus on single users. We aim at uncovering the geographical distribution of groups of visitors into time-zones as a whole. Our approach, to the best of our knowledge, is the first of its kind applied to the Dark Web. The idea is to exploit the time of all posts in the Dark Web forums to build profiles of the visiting crowds. Then, to uncover the geographical origin of the Dark Web crowd by matching the crowd profile to that of users from known regions on regular web platforms. We assess the effectiveness of our methodology on standard web and two Dark Web platforms with users of known origin, and apply it to three controversial anonymous Dark Web forums. We believe that this work helps the community better understand the Dark Web from a sociological point of view and support the investigation of authorities when the security of citizens is at stake.

Distributed financial systems are radically changing the way we do business and spend our money. Ripple, in particular, is unique in its kind. It is built on consensus and trust among its users and it allows to exchange both fiat currencies and goods over its network. It does so by storing the accounts of its users, their balances, and all the transactions in a distributed ledger, publicly accessible. In this paper we perform an in-depth study of the Ripple exchange system and its public distributed ledger. We analyze payments, the structure of payment paths, and the role of the entities in the system such as Gateways (the equivalent of banks) and Market Makers. We also analyze the internal stream of events and show that Ripple relies on a surprisingly small number of active validators, raising concerns on the actual robustness and fairness of the system. Moreover, we consider the degree of anonymity that Ripple is able to guarantee. By examining the first three years of Ripple history (more than 500 GB worth of data), we show that even approximate information on a single payment can uncover, with incredible accuracy, the entire financial life of the user. For example, anyone who overhears our order of a Latte at our favourite bar can easily get complete and unlimited access to our balance, our previous and future payments, our monthly income, as well as critical information about the places where we shop and the people we trust.

We show that the new hover (floating touch) technology, available in a number of today's smartphone models, can be abused by malicious Android applications to record all touchscreen input into applications system-wide. Leveraging this attack, a malicious application running on the system is able to capture sensitive input such as passwords and PINs, record all user's social interactions, as well as profile user's behavior. To evaluate our attack we implemented Hoover, a proof-of-concept malicious application that runs in the background and records all input to all foreground applications. We evaluated Hoover with 20 users, across two different Android devices and two input methods, stylus and finger. In the case of touchscreen input by finger, Hoover estimated the positions of users' clicks within an error of 100 pixels and keyboard input with an accuracy of 79%. Hoover captured users' input by stylus even more accurately, estimating users' clicks within 2 pixels and keyboard input with an accuracy of 98%. Differently from existing well-known side channel attacks, this is the first work that proves the security implications of the hover technology and its potential to steal all user inputs with high granularity. We discuss ways of mitigating this attack and show that this cannot be done by simply restricting access to permissions or imposing additional cognitive load on the users since this would significantly constrain the intended use of the hover technology.

Whenever our smartphones have their WiFi radio interface on, they periodically try to connect to known wireless APs (networks the user has connected to in the past). This is done through WiFi Probe requests - special wireless frames that contain the MAC address of the sending device and, in most of the cases, the human-readable name-string (SSID) of the known AP. This semantic information, inherent to the network protocol, is sent in the clear and, if sniffed, can help discover important information and phenomena of people and human nature that have nothing to do with technology. In this paper we present the idea of exploiting WiFi probe requests to de-anonymize the origin of participants in large events. We make use of several, publicly available datasets containing more than 11M of probe requests collected in scenarios that are of citywide, national (two political meetings), and international religion-related relevance. We show how, by exploiting the semantic information brought by the relative WiFi probes, we are able to discover with high accuracy the provenance of the crowds in each event. In particular, the de-anonymization outcome of the two political meetings held few days before the election days in Italy match surprisingly well the official voting results reported for the two respective parties.

Existing social-aware routing protocols for packet switched networks make use of the information about the social structure of the network deduced by state information of nodes (e.g., history of past encounters) to optimize routing. Although these approaches are shown to have superior performance to social-oblivious, stateless routing protocols (BinarySW, Epidemic), the improvement comes at the cost of considerable storage overhead required on the nodes. In this paper we present SANE, the first routing mechanism that combines the advantages of both social-aware and stateless approaches. SANE is based on the observation - that we validate on a real-world trace - that individuals with similar interests tend to meet more often. In SANE, individuals (network members) are characterized by their interest profile, a compact representation of their interests. By implementing a simple routing rule based on interest profile similarity, SANE is free of network state information, thus overcoming the storage capacity problem with existing social-aware approaches. Through thorough experiments, we show the superiority of SANE over existing approaches, both stateful, social-aware and stateless, social-oblivious. We discuss the statelessness of our approach in the supplementary file, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPDS.2014.2307857, of this manuscript. Our interest-based approach easily enables innovative networking services, such as interest-casting. An interest-casting protocol is also introduced in this paper, and evaluated through experiments based on both real-world and synthetic mobility traces.

Mobile-cloud offloading mechanisms delegate heavy mobile computation to the cloud. In real life use, the energy tradeoff of computing the task locally or sending the input data and the code of the task to the cloud is often negative, especially with popular communication intensive jobs like social-networking, gaming, and emailing. We design and build a working implementation of CDroid, a system that tightly couples the device OS to its cloud counterpart. The cloud-side handles data traffic through the device efficiently and, at the same time, caches code and data optimally for possible future offloading. In our system, when offloading decision takes place, input and code are likely to be already on the cloud. CDroid makes mobile cloud offloading more practical enabling offloading of lightweight jobs and communication intensive apps. Our experiments with real users in everyday life show excellent results in terms of energy savings and user experience.

This paper presents small world in motion (SWIM), a new mobility model for ad hoc networking. SWIM is relatively simple, is easily tuned by setting just a few parameters, and generates traces that look real-synthetic traces have the same statistical properties of real traces in terms of intercontact times, contact duration, and frequency among node couples. Furthermore, it generates social behavior among nodes and models networks with complex social communities as the ones observed in the real traces. SWIM shows experimentally and theoretically the presence of the power-law and exponential decay dichotomy of intercontact times, and, most importantly, our experiments show that predicts very accurately the performance of forwarding protocols for PSNs like Epidemic, Delegation, Spray&Wait, and more complex, social-based ones like BUBBLE. Moreover, we propose a methodology to assess protocols on model with a large number of nodes. To the best of our knowledge, this is the first such study. Scaling of mobility models is a fundamental issue, yet never considered in the literature. Thanks to SWIM, here we present the first analysis of the scaling capabilities of Epidemic Forwarding, Delegation Forwarding, Spray&Wait, and BUBBLE.

The increasing spread of mobile cloud computing paradigm is changing the traditional mobile communication infrastructure. Today, smartphones can rely on virtual (software) “clones” in the cloud, offering backup/recovery solutions as well as the possibility to offload computations. As a result, clones increase the communication and computation capabilities of smartphones, making their limited batteries last longer. Unfortunately, mobile cloud introduces new privacy risks, since personal information of the communicating users is distributed among several parties (e.g., cellular network operator, cloud provider). In this paper, we propose a solution implementing an end-to-end anonymous communication protocol between two users in the network, which leverages properties of social networks and ad hoc wireless networks. We consider an adversary model where each party observing a portion of the communication possibly colludes with others to uncover the identity of communicating users. We then extensively analyze the security of our protocol and the anonymity preserved against the above adversaries. Most importantly, we assess the performance of our solution by comparing it to Tor on a real testbed of 36 smartphones and relative clones running on Amazon EC2 platform.

The mobile cloud computing paradigm involves communications between smartphones and their virtual (software) clones in the cloud. It offers both backup/recovery solutions as well as offload of mobile computations, increasing the communication and computation capabilities of smartphones and making their limited batteries last longer. Unfortunately, in this scenario, the privacy of the users is at stake. The cellular network operator knows how often users contact the cloud, and the cloud provider knows how often users' clones contact each other. We address this privacy problem by providing an anonymous communication protocol, leveraging properties of social networks and ad-hoc wireless networks. Our solution provides anonymous end-to-end communication between two users in the network, and in turn between a user and her clone in the cloud. The proposal copes with an adversary model, where each party observing a portion of the communication (including the cloud provider and the cellular network operator) possibly colludes with others to uncover the identity of communicating users.

The ever increasing ubiquitousness of WiFi access points, coupled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a reality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user experience. Most of the times, this is achieved by recurrently broadcasting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., "Home WiFi", "Campus WiFi", and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural question: "Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes?". In this work we give a positive answer to this question. We organized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. During the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the underlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociological aspects of its participants---language, vendor adoption, and so on.

The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). In this work we study the fmobile software/data backupseasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user's data and apps is needed. We give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration.

Thanks to their power, the many sensors they embed, and their inherent connectivity to Internet, smartphones are certainly becoming the primary source of multimedia content and the main tool for content sharing. In this demo, we analyze the complexity of real-time video streaming among smartphone users. Firstly, we show that the traditional solution-a unique server receiving and dispatching all devices' content-suffers from scalability issues. Then, we present StreamSmart, a distributed system for real-time video streaming of smartphones, that leverages a virtual P2P network of smartphone software clones on the cloud. In StreamSmart, the captured content is forwarded from the sharing device to its own cloud clone, that in turn forwards it to the clones of other users. These latter transmit the content to the respective devices and, at the same time, contribute to further spread it to other possible clones in the network. We show that the StreamSmart system is highly scalable, responsive, and fault tolerant.

The Internet of the future will be of things: Large scale IoT systems integrating various technologies (tracking, wired and wireless sensor and actuator networks, enhanced communication protocols, distributed intelligence for smart objects) will change the way we live and interact with the environment. Unfortunately, a standardization for IoT systems that allows for integration of sensors, data, services and applications in a smooth way and for interoperability of different technologies was still missing.

The battery limits of today smartphones require a solution. In the scientific community it is believed that a promising way of prolonging battery life is to offload mobile computation to the cloud. State of the art offloading architectures consists of virtual copies of real smartphones (the clones) that run on the cloud, are synchronized with the corresponding devices, and help alleviate the computational burden on the real smartphones. Recently, it has been proposed to organize the clones in a peer-to-peer network in order to facilitate content sharing among the mobile smartphones. We believe that P2P network of clones, aside from content sharing, can be a useful tool to solve critical security problems on the mobile network of smartphones. In particular, we consider the problem of computing an efficient patching strategy to stop worm spreading between smartphones. The peer-to-peer network of clones is used to compute the best strategy to patch the smartphones in such a way that the number of devices to patch is low (to reduce the load on the cellular infrastructure) and that the worm is stopped quickly. We consider two well defined worms, one spreading between the devices and one attacking the cloud before moving to the real smartphones; we describe CloudShield, a suite of protocols running on the peer-to-peer network of clones; and we show by experiments that CloudShield outperforms state-of-theart worm-containment mechanisms for mobile wireless networks.

In this paper, we present two forwarding protocols for mobile wireless networks of selfish individuals. We assume that all the nodes are selfish and show formally that both protocols are strategy proof, that is, no individual has an interest to deviate. Extensive simulations with real traces show that our protocols introduce an extremely small overhead in terms of delay, while the techniques we introduce to force faithful behavior have the positive and quite surprising side effect to improve performance by reducing the number of replicas and the storage requirements. We test our protocols also in the presence of a natural variation of the notion of selfishness-nodes that are selfish with outsiders and faithful with people from the same community. Even in this case, our protocols are shown to be very efficient in detecting possible misbehavior.

In this paper we describe SANE, the first forwarding mechanism that combines the advantages of both social-aware and stateless approaches in pocket switched network routing. SANE is based on the observation“that we validate on real-world traces”that individuals with similar interests tend to meet more often. In our approach, individuals (network members) are characterized by their interest profile, a compact representation of their interests. Through extensive experiments, we show the superiority of social-aware, stateless forwarding over existing stateful, social-aware and stateless, social-oblivious forwarding. An important byproduct of our interest-based approach is that it easily enables innovative routing primitives, such as interest-casting. An interest-casting protocol is also described, and extensively evaluated through experiments based on both real-world and synthetic mobility traces.

In this paper we present two forwarding protocols for mobile wireless networks of selfish individuals. We assume that all the nodes are selfish and show formally that both protocols are Nash equilibria, that is, no individual has an interest to deviate. Extensive simulations with real traces show that our protocols introduce an extremely small overhead in terms of delay, while the techniques we introduce to force faithful behavior have the positive side-effect to improve performance by reducing the number of message considerably (more than 20%). We test our protocols also in the presence of a natural variation of the notion of selfishness-nodes that are selfish with outsiders and faithful with people from the same community. Even in this case, our protocols are shown to be very efficient in detecting possible misbehavior.

The complexity of social mobile networks, networks of devices carried by humans (e.g. sensors or PDAs) and communicating with short-range wireless technology, makes it hard protocol evaluation. A simple and efficient mobility model such as SWIM reflects correctly kernel properties of human movement and, at the same time, allows to evaluate accurately protocols in this context. In this paper we investigate the properties of SWIM, in particular how SWIM is able to generate social behavior among the nodes and how SWIM is able to model networks with a power-law exponential decay dichotomy of inter contact time and with complex sub-structures (communities) as the ones observed in the real data traces. We simulate three real scenarios and compare the synthetic data with real world data in terms of inter-contact, contact duration, number of contacts, and presence and structure of communities among nodes and find out a very good matching. By comparing the performance of BUBBLE, a community-based forwarding protocol for social mobile networks, on both real and synthetic data traces, we show that SWIM not only is able to extrapolate key properties of human mobility but also is very accurate in predicting performance of protocols based on social human sub-structures.

This paper presents small world in motion (SWIM), a new mobility model for ad-hoc networking. SWIM is relatively simple, is easily tuned by setting just a few parameters, and generates traces that look real-synthetic traces have the same statistical properties of real traces. SWIM shows experimentally and theoretically the presence of the power law and exponential decay dichotomy of inter-contact time, and, most importantly, our experiments show that it can predict very accurately the performance of forwarding protocols.

In this paper, we consider security-related and energy efficiency issues in multihop wireless networks. We start our work from the observation, known in the literature, that shortest path routing creates congested areas in multihop wireless networks. These areas are critical-they generate both security and energy efficiency issues. We attack these problems and set out routing in outer space, a new routing mechanism that transforms any shortest path routing protocol (or approximated versions of it) into a new protocol that does not create congested areas, does not have the associated security-related issues, and does not encourage selfish positioning. Moreover, the network is more energy efficient than the same network using the original routing protocol (in spite of using more energy globally) and dies more gracefully. We also describe applications of our idea to mobility and to a security protocol for the detection of node replication attacks.

In this paper we consider security-related and energy-efficiency issues in multi-hop wireless networks. We start our work from the observation, known in the literature, that shortest path routing creates congested areas in multi- hop wireless networks. These areas are critical-they generate both security and energy efficiency issues. We attack these problems and set out routing in outer space, a new routing mechanism that transforms any shortest path routing protocol (or an approximated version of it) into a new protocol that, in case of uniform traffic, guarantees that every node of the network is responsible for relaying the same number of messages, on expectation. We can show that a network that uses routing in outer space does not have congested areas, does not have the associated security-related issues and does not encourage selfish positioning.

In this paper we consider security-related and energy-efficiency issues in multi-hop wireless networks. We start our work from the observation, known in the literature, that shortest path routing creates congested areas in multi-hop wireless networks. These areas are critical - they generate both security and energy efficiency issues. We attack these problems and set out routing in outer space, a new routing mechanism that transforms any shortest path routing protocol (or approximated versions of it) into a new protocol that does not create congested areas, does not have the associated security-related issues, and does not encourage selfish positioning. Moreover, the network lives longer of the same network using the original routing protocol (in spite of using more energy globally), and dies more gracefully.