Telegram is a widely adopted instant messaging platform. It has become worldwide popular because of its emphasis on privacy and its social network features such as channels-virtual rooms in which only the admins can post and broadcast messages to all the subscribers. Channels are used to deliver live updates (e.g., weather alerts) and content to a large audience (e.g., COVID-19 announcements) but unfortunately also to disseminate radical ideologies and coordinate attacks such as the Capitol Hill riot.

In the world of cryptocurrencies, the public listing of a new token often generates significant hype. In many cases, the price of the token skyrockets in a few seconds, and timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify operations performed using sniper bots. Our findings reveal 352,413 sniping operations on Ethereum and 1,716,917 on BSC for a total turnaround of $155,630,184 and $137,548,859, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze possible countermeasures and mechanisms used in token smart contracts that can reduce the negative impact of sniper bots.

Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection. Telegram has standout social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams. In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze two types of channels: Clones and fakes. Clones are channels that publish identical content from another channel in order to gain subscribers and promote services. Fakes, on the other hand, are channels that impersonate celebrities or well-known services by posting their own messages. To automatically detect fake channels, we propose a machine learning model that achieves an F1-score of 85.45%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.

As the adoption of microservices architectures in cloud deployments grows, so does the challenge of ensuring rapid recovery and minimal energy consumption in the face of disasters. This paper introduces a Generalized Stochastic Petri Net (GSPN) model specifically designed to quantify recovery times and electrical consumption in such environments. The model supports the development of resilient and eco-conscious systems by allowing precise manipulation and planning based on various configuration scenarios. We identify critical architectural elements and define intervals that yield significant improvements. Our findings not only enhance the understanding of energy and recovery dynamics in microservices but also serve as a crucial tool for system designers aiming to optimize both performance and sustainability. The implications of this research facilitate a strategic approach to disaster recovery planning, contributing to the broader field of cloud computing resilience.

With the rise of smart working and recent global events, the risk of cyberattacks is increasing steadily. Sometimes adversaries focus on stealing valuable data, such as intellectual property (IP): they exfiltrate a large volume of IP documents from a target company. They then identify those of their interest by leveraging automated methods. This work proposes the DARD (Deceptive Approaches for Robust Defense against IP theft) system, a framework designed to deceive adversaries who rely on automatic approaches to classify exfiltrated documents. Starting from an original repository of documents, DARD automatically generates a new deceptive repository that misleads popular automatic approaches, resulting in clusters of documents that are significantly different from the actual ones. By utilizing this approach, DARD aims to hinder the accurate clustering and the identification of the topic of documents by adversaries relying on automated techniques. The paper presents four deceptive operations (Basic Shuffle, Shuffle increment, Shuffle reduction, and Change topic) that DARD leverages to create a deceptive repository. We evaluate the efficacy of our approach by considering three different types of adversaries, each possessing varying levels of knowledge and expertise. Through extensive experiments, we show that the DARD system can deceive both automatic topic modeling and document clustering techniques, including widely-used commercial tools such as Amazon Comprehend. Hence, our solution provides a robust defense mechanism against Intellectual Property (IP) theft.

The Non-Fungible Token (NFT) market in the Ethereum blockchain experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022. However, concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially. Our research examines the effects of wash trading on the NFT market in Ethereum from the beginning until January 2022, using multiple approaches. We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774. We look at two ways to profit from wash trading: Artificially increasing the price of the NFT and taking advantage of the token reward systems provided by some marketplaces. Our findings show that exploiting the token reward systems of NFTMs is much more profitable (mean gain of successful operations is $1.055M on LooksRare), more likely to succeed (more than 80% of operations), and less risky than reselling an NFT at a higher price using wash trading (50% of activities result in a loss). Our research highlights that wash trading is frequent in Ethereum and that NFTMs should implement protective mechanisms to stop such illicit behavior.

Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection and social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams.In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze fake channels on Telegram. To automatically detect fake channels, we propose a machine learning model that achieves an accuracy of 85.49%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.

Payment Channel Networks (PCNs) have been introduced as a viable solution to the scalability problem of the popular blockchain. In PCNs, a payment channel allows its end nodes to pay each other without publishing every transaction to the blockchain. A transaction can be routed in the network if there is a path of channels with sufficient funds, and the intermediate routing nodes can ask the transaction sender for a compensatory fee. However, a channel may eventually become depleted and cannot support further payments in a certain direction, as transaction flows from that direction is heavier than flows from the other direction. In this paper, we discuss a PCN node’s possible roles and objectives, and analyze the strategies nodes should take under different roles by considering nodes’ benefits and the network’s performance. Then, we examine two basic network structures (ring and chord) and determine the constraints under which they constitute a Nash equilibrium. Based on the theoretical results, we propose a balance-aware fee-incentivized routing algorithm to guarantee cost-efficient routing, fair fee charging, and the network’s long lasting good performance in general PCNs. Testbed-based evaluation is conducted to validate our theoretical results and to show the feasibility of our proposed approach.

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.

Autonomous Systems (ASes) exist in two dimensions on the Internet: the administrative and the operational one. Regional Internet Registries (RIRs) rule the former, while BGP the latter. In this work, we reconstruct the lives of the ASes on both dimensions, performing a joint analysis that covers 17 years of data. For the administrative dimension, we leverage delegation files published by RIRs to report the daily status of Internet resources they allocate. For the operational dimension, we characterize the temporal activity of ASNs in the Internet control plane using BGP data collected by the RouteViews and RIPE RIS projects. We present a methodology to extract insights about AS life cycles, including dealing with pitfalls affecting authoritative public datasets. We then perform a joint analysis to establish the relationship (or lack of) between these two dimensions for all allocated ASNs and all ASNs visible in BGP. We characterize the usual behaviors, specific differences between RIRs and historical resources, as well as measure the discrepancies between the two "parallel" lives. We find discrepancies and misalignment that reveal useful insights, and we highlight through examples the potential of this new lens to help pinpoint malicious BGP activity and various types of misconfigurations. This study illuminates a largely unexplored aspect of the Internet global routing system and provides methods and data to support broader studies that relate to security, policy, and network management.

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.

Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading heavy tasks to cloud computing infrastructures. The first step in this procedure is partitioning the application into small tasks and identifying those that are better suited for offloading. The method call partitioning strategy splits the code into a set of method calls that are offloaded to remote servers. Quite often, many applications need to make use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications is not an easy task. Deciding the number of remote servers determines the performance of the applications and the costs of the cloud usage. On one hand, users are interested in improving the performance of their applications, so they would like to use as many servers as possible, but on the other hand, they would also like to reduce their costs by using fewer cloud resources. In this paper, we propose a Stochastic Petri Net (SPN) modeling strategy to represent method call executions of mobile cloud systems. This approach enables a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications.

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.

Mobile Cloud Computing (MCC) combines mobile computing and cloud computing aiming to aid performance of mobile devices. The idea is simple: thin devices offload heavy methods to resource-rich servers in the clouds. We believe that in the near future MCC will adopt more advanced offloading techniques. In particular, in this paper we envision a scenario where offloading frameworks will have to deal with GPU code offloading. Amazon already offers instances with Graphics Processing Units (GPU), which can be used for this purpose. We propose and implement MCC-Adviser, a simulation tool that can predict the performance of GPUs with different number of cores using Stochastic Petri Nets. We tested MCC-Adviser in a case study with one of the expensive Amazon GPU instances. The simulations showed that it is possible to minimize costs, while satisfying user's quality of service requirements, by utilizing less powerful instances.

Abstract Commercial Virtual Private Network (VPN) services have become a popular and convenient technology for users seeking privacy and anonymity. They have been applied to a wide range of use cases, with commercial providers often making bold claims regarding their ability to fulfil each of these needs, e.g., censorship circumvention, anonymity and protection from monitoring and tracking. However, as of yet, the claims made by these providers have not received a sufficiently detailed scrutiny. This paper thus investigates the claims of privacy and anonymity in commercial VPN services. We analyse 14 of the most popular ones, inspecting their internals and their infrastructures. Despite being a known issue, our experimental study reveals that the majority of VPN services suffer from IPv6 traffic leakage. The work is extended by developing more sophisticated DNS hijacking attacks that allow all traffic to be transparently captured.We conclude discussing a range of best practices and countermeasures that can address these vulnerabilities

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 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.

We consider the problem of detecting clones in wireless mobile ad-hoc networks. We assume that one of the devices of the network has been cloned. Everything, including saved passwords, certificates and secret keys. We propose a solution in networks of mobile devices carried by individuals - composed by nodes that can communicate by short-range technology like bluetooth or Wi-Fi, and links appear and disappear according to social relationships between users. Our idea is to use social physical contacts, securely collected by wireless personal smart phones, as a biometric way to authenticate the owner of the device and detect the clone attack. We introduce two mechanisms: Personal Marks and Community Certificates. Personal Marks is a simple cryptographic protocol that works well when the adversary is an insider, a malicious node in the network that tries to use the stolen credentials in the social community of the original device that has been cloned. Community Certificates works well when the adversary is an outsider, a node that has the goal of using the stolen credentials when interacting with other nodes that are far in the social network from the original device. When combined, these mechanisms provide an excellent protection against this very strong attack. We prove our ideas and solutions with extensive simulations in both simulated and real world scenarios - with mobility traces collected in a real life experiment.

We derive a consensus protocol for a hybrid failure model. In this model, clients are Byzantine faulty and servers are crash faulty. We argue that this model is well suited to environments where the servers run within one administrative domain, and the clients run outside of this domain. Our consensus protocol, which is derived from crash Paxos, provides low latency for client requests, tolerates any number of (Byzantine) faulty clients, up to 1/3 (crash) faulty servers, and does not rely on computing costly signatures in the common case. It can be used to build state machine replication that provides a highly available service.

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.

Wireless Sensor Networks (WSNs) are often deployed in hostile environments where an adversary can physically capture some of the nodes, first can reprogram, and then, can replicate them in a large number of clones, easily taking control over the network. A few distributed solutions to address this fundamental problem have been recently proposed. However, these solutions are not satisfactory. First, they are energy and memory demanding: A serious drawback for any protocol to be used in the WSN-resource-constrained environment. Further, they are vulnerable to the specific adversary models introduced in this paper. The contributions of this work are threefold. First, we analyze the desirable properties of a distributed mechanism for the detection of node replication attacks. Second, we show that the known solutions for this problem do not completely meet our requirements. Third, we propose a new self-healing, Randomized, Efficient, and Distributed (RED) protocol for the detection of node replication attacks, and we show that it satisfies the introduced requirements. Finally, extensive simulations show that our protocol is highly efficient in communication, memory, and computation; is much more effective than competing solutions in the literature; and is resistant to the new kind of attacks introduced in this paper, while other solutions are not.

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 work considers key management for secure multicast in the Logical Key Hierarchy (LKH) model and proposes a methodology to establish the minimal key bit length that guarantees a specified degree of confidentiality for the multicast communications managed within this model. We also introduce the concepts of information lifetime and information dependence to formalize the intuition that keys should be longer, and thus stronger, when used to encrypt “important” information, that is information (including other keys) that need to be kept confidential for a longer period. Then, these concepts are used to build a formal theory that is applied to set the correct bit length of every key in the system in such a way to guarantee the prescribed degree of confidentiality of the multicast messages. Quite surprisingly, we formally show that not all the keys in the LKH hierarchy should have the same length; this observation, besides being of theoretical interest, also leads to substantial savings in terms of memory, computation, and bandwidth. The theory we develop to obtain these results can be useful in other contexts as well.

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.

We show that massive attacks against sensor networks that use random key pre-distribution schemes cannot be cheap, provided that the parameters are set in the right way. By choosing them appropriately, any adversary whose aim is to compromise a large fraction of the communication links is forced, with overwhelming probability, to capture a large fraction of the nodes. This holds regardless of the information available to the adversary to select the nodes. We consider two important security properties: We say that the network is unassailable if the adversary cannot compromise a linear fraction of the communication links by compromising a sub-linear fraction of the nodes, and that the network is unsplittable if the adversary cannot partition the network into two (or more) linear size fragments. We show how to set the relevant parameters of random key pre-distribution---pool and key ring size---in such a way that the network is not only connected, but also provably unassailable and unsplittable with high probability. Moreover, we also show how to set the parameters in such a way to form a giant component in the network, a connected subgraph including, say, 99% of the sensors. Giant components emerge by using much smaller key rings, are sparse, and, quite remarkably, are provably unassailable and unsplittable as well. All these results are supported by experiments.

One of the most vexing problems in wireless sensor network security is the node capture attack. An adversary can capture a node from the network as the first step for further different types of attacks. For example, the adversary can collect all the cryptographic material stored in the node. Also, the node can be reprogrammed and re-deployed in the network in order to perform malicious activities. To the best of our knowledge no distributed solution has been proposed to detect a node capture in a mobile wireless sensor network. In this paper we propose an efficient and distributed solution to this problem leveraging emergent properties of mobile wireless sensor networks. In particular, we introduce two solutions: SDD, that does not require explicit information exchange between the nodes during the local detection, and CCD, a more sophisticated protocol that uses local node cooperation in addition to mobility to greatly improve performance. We also introduce a benchmark to compare these solutions with. Experimental results demonstrate the feasibility of our proposal. For instance, while the benchmark requires about 9,000 seconds to detect node captures, CDD requires less than 2,000 seconds. These results support our intuition that node mobility, in conjunction with a limited amount of local cooperation, can be used to detect emergent global properties.

We give, for the first time, a precise mathematical analysis of the connectivity and security properties of sensor networks that make use of the random predistribution of keys. We also show how to set the parameters---pool and key ring size---in such a way that the network is not only connected with high probability via secure links but also provably resilient, in the following sense: We formally show that any adversary that captures sensors at random with the aim of compromising a constant fraction of the secure links must capture at least a constant fraction of the nodes of the network. In the context of wireless sensor networks where random predistribution of keys is employed, we are the first to provide a mathematically precise proof, with a clear indication of parameter choice, that two crucial properties---connectivity via secure links and resilience against malicious attacks---can be obtained simultaneously. We also show in a mathematically rigorous way that the network enjoys another strong security property. The adversary cannot partition the network into two linear size components, compromising all the links between them, unless it captures linearly many nodes. This implies that the network is also fault tolerant with respect to node failures. Our theoretical results are complemented by extensive simulations that reinforce our main conclusions.

Access control models need to be interoperable when administrative domains with heterogeneous access control models need to collaborate. Even, collaboration among homogeneous access control models is not straight-forward due to the different security orderings they might employ. In this paper, we briefly put forward an overlay formation mechanism based on chameleon hash functions. The mechanism allows collaborators to map their collaborating entities into a new collaboration specific security ordering that is agreeable to the peer collaborator. Collaborators use overlays as interoperation interfaces. By digitally signing each others' overlays, organizations enter into collaboration. Since overlays are virtual mappings, defining an overlay does not interfere with the access control model of the host organization. The use of overlays hides the internal security ordering of an organization from its collaborators. The trapdoor collision property of chameleon hash function ensures the privacy of collaboration agreements.

In this paper, we consider new issues in building secure p2p file sharing systems. In particular, we define a powerful adversary model and consequently present the requirements to address when implementing a threat-adaptive secure file sharing system. We describe the main components of such a system: an early warning mechanism to perform pre-emptive actions against new vulnerabilities; a mechanism to sanitize corrupted nodes; a protocol to securely "migrate" data from non-safe nodes; and an efficient dynamic secret sharing mechanism.

Wireless sensor networks are often deployed in hostile environments, where anadversary can physically capture some of the nodes. Once a node is captured, the attackercan re-program it and replicate the node in a large number of clones, thus easily taking over the network. The detection of node replication attacks in a wireless sensor network is therefore a fundamental problem. A few distributed solutions have recently been proposed. However, these solutions are not satisfactory. First, they are energy and memory demanding: A serious drawback for any protocol that is to be used in resource constrained environment such as a sensor network. Further, they are vulnerable to specific adversary models introduced in this paper.

In this paper we consider a large distributed system in which data is shared among several users. Specifically, we present a secure adaptive algorithm for data fragment allocation on multiple nodes of the system. The algorithm handles (replicated) data fragments, stored by nodes without the need of encryption, in such a way to ease information sharing. Data confidentiality is guaranteed in the presence of passive and active attacks, and fragments are dynamically reallocated/replicated in the system to converge, under assumptions of regularity of the read-write activity, to an allocation that provably guarantees highest performance in terms of network load.

Wireless sensor networks (WSN) are often deployed in hostile environments, where an attacker can also capture some nodes. Once a node is captured, the attacker can re-program it and start replicating the node. These replicas can then be deployed in all (or a part of) the network area. The replicas can thus perform the attack they are programmed for: DoS (Denial of Service), or influencing any voting mechanism are just examples. Detection of node replication attack is therefore a fundamental property of all the WSN applications in which an attacker presence is possible. The contribution of this paper is twofold: First, we analyze the desirable properties of a distributed mechanism for the detection of replicated IDs; second, we show that the first proposal recently appeared in literature to realize a distributed solution for the detection of replicas does not completely fulfil the requirements. Hence, the design of efficient and distributed protocols to detect node identity replicas is still an open and demanding issue.

This paper establishes the state of the art in both deterministic and randomized online permutation routing in the POPS network. Indeed, we show that any permutation can be routed online on a {\rm POPS}(d, g) network either with O({\frac{d}{g}}\log g) deterministic slots, or, with high probability, with 5c\lceil d/g \rceil + o(d/g) + O(\log\log g) randomized slots, where constant c = \exp (1 + e^{-1}) \approx 3.927. When d = \Theta(g), which we claim to be the "interesting” case, the randomized algorithm is exponentially faster than any other algorithm in the literature, both deterministic and randomized ones. This is true in practice as well. Indeed, experiments show that it outperforms its rivals even starting from as small a network as a POPS(2, 2) and the gap grows exponentially with the size of the network. We can also show that, under proper hypothesis, no deterministic algorithm can asymptotically match its performance.

This paper shows that an n=2kprocessor partitioned optical passive stars (POPS) network with g groups and d processors per group can simulate every bidirectional move of an n processor hypercube using one slot when dg. Moreover, the same POPS network can simulate every monodirectional move of a processor hypercube using one slot when d=g. All these results are shown to be optimal. Our simulations improve on the literature whenever dneg and directly yield several important consequences. For example, as a direct consequence of our simulations, a POPS network, n=dg and d2n slots. This is faster than the best previously known ad hoc algorithm and is actually optimal. Similarly, we improve on the best POPS network algorithms for both the prefix sums problem on general POPS networks and the fundamental online permutation routing problem, among others

Summary form only given. A distributed wireless sensor network (WSN) is a collection of n sensors with limited hardware resources and multihop message exchange capabilities. Due to the scarceness of resources, the distributed paradigm required, and the threats to the security, a challenging problem is how to implement secure pair-wise communications among any pair of sensors in a WSN. In particular, storage memory and energy saving as well as resilience to physical compromising of a sensor are the more stringent requirements. The contributions are twofold: (1) we describe a new threat model to communications confidentiality in WSNs (the smart attacker model); under this new, more realistic threat model, the security features of the previous schemes proposed in the literature drastically decrease; (2) we provide a new pseudo-random key predeployment strategy that assures: (a) a key discovery phase that requires no communications; (b) high resilience against the smart attacker model. We provide both analytical evaluations and extensive simulations of the proposed scheme. The results indicate that our pseudo-random key predeployment proposal achieves a provably efficient assignment of keys to sensors, an energy preserving key discovery phase, and is resilient against the smart attacker model.

This paper brings up a new concern regarding efficient re-keying of large groups with dynamic membership: minimizing the overall time it takes for the key server and the group members to process the re-keying message. Specifically, we concentrate on re-keying algorithms based on the Logical Key Hierarchy (LKH), and minimize the longest sequence of encryptions and decryptions that need to be done in a re-keying operation. We first prove a lower bound on the time required to perform a re-keying operation in this model, then we provide an optimal schedule of re-keying messages matching the above lower bound. In particular, we show that the optimal schedule can be found only when the ariety of the LKH key graph is chosen according to the available communication bandwidth and the users processing power. Our results show that key trees of ariety 3, commonly assumed to be optimal, are not optimal when used in high bandwidth networks, or networks of devices with low computational power like sensor networks.

We present a distributed algorithm for file allocation that guarantees high assurance, availability, and scalability in a large distributed file system. The algorithm can use replication and fragmentation schemes to allocate the files over multiple servers. The file confidentiality and integrity are preserved, even in the presence of a successful attack that compromises a subset of the file servers. The algorithm is adaptive in the sense that it changes the file allocation as the read-write patterns and the location of the clients in the network change. We formally prove that, assuming read-write patterns are stable, the algorithm converges toward an optimal file allocation, where optimality is defined as maximizing the file assurance.

Several dynamic programming algorithms are considered which can be efficiently implemented using parallel networks with reconfigurable buses. The bit model of general reconfigurable meshes with directed links, common write, and unit-time delay for broadcasting is assumed. Given two sequences of length m and n, respectively, their longest common subsequence can be found in constant time by an O(mh)/spl times/O(nh) directed reconfigurable mesh, where h=min{m, n}+1. Moreover, given an n-node directed graph G=(V, E) with (possibly negative) integer weights on its arcs, the shortest distances from a source node /spl nu/ /spl epsiv/ V to all other nodes can be found in constant time by an O(n/sup 2/w) x O(n/sup 2/w) directed reconfigurable mesh, where w is the maximum are weight.

Several new number representations based on a residue number system are presented which use the smallest prime numbers as moduli and are suited for parallel computations on a reconfigurable mesh architecture. The bit model of linear reconfigurable mesh with exclusive write and unit-time delay for broadcasting on a subbus is assumed. It is shown how to convert in O(1) time any integer, ranging between 0 and n-1, from any commonly used representation to any new representation proposed in this paper (and vice versa) using an nxO(log2n/log log n) reconfigurable mesh. In particular, some of the previously known conversion techniques are improved. Moreover, as a byproduct, it is shown how to compute in O(1) time the Prefix Sums of n bits by a reconfigurable mesh having the above mentioned size, thus improving previously known results. Applications to the Prefix Sums of n h-bit integers and to Approximate String Matching with α mismatches are also considered. The Summation and the Prefix Sums can be computed in O(1) time using O(h log N+log2N/log log N)xNh and O(h2+log2N/log(h+log N))xO(N(h+log N)) reconfigurable meshes, respectively. Moreover, it is shown for the first time how to find in O(1) time all the occurrences of a pattern of length m in a text of length n, allowing less than α mismatches, using a reconfigurable mesh of size O(m log|Σ|)xO (n(log|Σ|+log2α/log log α)), where the pattern and the text are strings over a finite alphabet Σ and α < m

FPGAs can perform better than ASICs if the logic mapped onto them is optimized for each problem instance.Unfortunately, this advantage is often canceled by the long time needed by CAD tools to generate problem instance dependent logic and the time required to configure the FPGAs.In this paper, a novel approach for runtime mapping is proposed that utilizes self-reconfigurability of multicontext FPGAs to achieve very high speedups over existing approaches.The key idea is to design and map logic onto a multicontext FPGA that in turn maps problem instance dependent logic onto other contexts of the same FPGA.As a result, CAD tools need to be used just once for each problem and not once for every problem instance as is usually done.To demonstrate the feasibility of our approach, a detailed implementation of the KMP string matching algorithm is presented which involves runtime construction of a finite state machine.We implement the KMP algorithm on a conventional FPGA (Xilinx XC 6216) and use it to obtain accurate estimates of performance on a multicontext device.Speedups in mapping time of M lo6 over CAD tools and more than 1800 over a program written specifically for FSM generation were obtained.Significant speedups were obtained in overall execution time as well, including a speedup ranging from 3 to 16 times over a software implementation of the KMP algorithm running on a Sun Ultra 1 Model 140 workstation.

Several new number representations based on the residue number system are presented which use the smallest prime numbers as moduli and are suited for parallel computations on a reconfigurable mesh architecture. It is shown how to convert in O(1) time any integer ranging between 0 and n-1, from any commonly used representation to any new representation proposed in the paper (and vice versa) using an n/spl times/O(log/sup 2/n/log log n) reconfigurable mesh. In particular, some of the previously known conversion techniques are improved. Moreover, as a by product, it is shown how to compute in O(1) time the prefix sums of n bits improving previously known results. Applications to the summation and prefix sums of N h-bit integers are also considered. The summation and the prefix sums can be computed in O(1) time using O(h log N+log/sup 2/N/log log N)/spl times/Nh and O(h/sup 2/+log/sup 2/ N/log(h+log N))/spl times/O(N(h+log N)) reconfigurable meshes, respectively, improving all previously known results for most values of h including, for instance, h=O(log N).