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Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect geometric and material features of an object through RF waves. On the other hand, the PWE focuses on the the conversion of the wireless RF propagation in a controllable, by software, entity through the utilization of Reconfigurable Intelligent Surfaces (RISs), which can have a controllable interaction with impinging RF waves. In that sense, this dynamic synergy leverages the potential of RF Imaging to detect the structure of an object through RF wavefronts and the PWE's ability to selectively replicate those RF wavefronts from one spatial location to wherever an XR-RF mobile user is presently located. Then the captured wavefront, through appropriate hardware, is mapped to the visual representation of the object through machine learning models. As a key aspect of the XR-RF's system workflow is the wavefront copying mechanism, this work introduces a new PWE configuration algorithm for XR-RF. Moreover, it is shown that the waveform replication process inevitably yields imprecision in the replication process. After statistical analysis, based on simulation results, it is shown that this imprecision can be effectively modeled by the gamma distribution., Comment: This paper was presented in the Seventh International Balkan Conference on Communications and Networking (BalkanCom'24)

The cellular network plays a pivotal role in providing Internet access, since it is the only global-scale infrastructure with ubiquitous mobility support. To manage and maintain large-scale networks, mobile network operators require timely information, or even accurate performance forecasts. In this paper, we propose LightningNet, a lightweight and distributed graph-based framework for forecasting cellular network performance, which can capture spatio-temporal dependencies that arise in the network traffic. LightningNet achieves a steady performance increase over state-of-the-art forecasting techniques, while maintaining a similar resource usage profile. Our architecture ideology also excels in the respect that it is specifically designed to support IoT and edge devices, giving us an even greater step ahead of the current state-of-the-art, as indicated by our performance experiments with NVIDIA Jetson.

The usage of error handling in Solidity smart contracts is vital because smart contracts perform transactions that should be verified. Transactions that are not carefully handled, may lead to program crashes and vulnerabilities, implying financial loss and legal consequences. While Solidity designers attempt to constantly update the language with new features, including error-handling (EH) features, it is necessary for developers to promptly absorb how to use them. We conduct a large-scale empirical study on 283K unique open-source smart contracts to identify patterns regarding the usage of Solidity EH features over time. Overall, the usage of most EH features is limited. However, we observe an upward trend (> 60%) in the usage of a Solidity-tailored EH feature, i.e., require. This indicates that designers of modern programming languages may consider making error handling more tailored to the purposes of each language. Our analysis on 102 versions of the Solidity documentation indicates the volatile nature of Solidity, as the language changes frequently, i.e., there are changes on EH features once or twice a year. Such frequent releases may confuse smart contract developers, discouraging them to carefully read the Solidity documentation, and correctly adopt EH features. Furthermore, our findings reveal that nearly 70% of the examined smart contracts are exposed to potential failures due to missing error handing, e.g., unchecked external calls. Therefore, the use of EH features should be further supported via a more informative documentation containing (1) representative and meaningful examples and (2) details about the impact of potential EH misuses.

In the evolving landscape of sixth-generation (6G) wireless networks, unmanned aerial vehicles (UAVs) have emerged as transformative tools for dynamic and adaptive connectivity. However, dynamically adjusting their position to offer favorable communication channels introduces operational challenges in terms of energy consumption, especially when integrating advanced communication technologies like reconfigurable intelligent surfaces (RISs) and full-duplex relays (FDRs). To this end, by recognizing the pivotal role of UAV mobility, the paper introduces an energy-aware trajectory design for UAV-mounted RISs and UAV-mounted FDRs using the decode and forward (DF) protocol, aiming to maximize the network minimum rate and enhance user fairness, while taking into consideration the available on-board energy. Specifically, this work highlights their distinct energy consumption characteristics and their associated integration challenges by developing appropriate energy consumption models for both UAV-mounted RISs and FDRs that capture the intricate relationship between key factors such as weight, and their operational characteristics. Furthermore, a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem is formulated, considering the power dynamics of both systems, while assuring that the UAV energy is not depleted mid-air. Finally, simulation results underscore the importance of energy considerations in determining the optimal trajectory and scheduling and provide insights into the performance comparison of UAV-mounted RISs and FDRs in UAV-assisted wireless networks.

On 24 February 2022, Russia invaded Ukraine, starting what is now known as the Russo-Ukrainian War, initiating an online discourse on social media. Twitter as one of the most popular SNs, with an open and democratic character, enables a transparent discussion among its large user base. Unfortunately, this often leads to Twitter's policy violations, propaganda, abusive actions, civil integrity violation, and consequently to user accounts' suspension and deletion. This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of the user accounts that may lead to this. Toward this goal, we have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. We extract the categories of shared content of the suspended accounts and explain their characteristics, through the extraction of text embeddings in junction with cosine similarity clustering. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin and Ethereum fraud, spam, and advertisement campaigns. Additionally, we apply a machine learning methodology including a SHapley Additive explainability model to understand and explain how user accounts get suspended.

Twitter, as one of the most popular social networks, provides a platform for communication and online discourse. Unfortunately, it has also become a target for bots and fake accounts, resulting in the spread of false information and manipulation. This paper introduces a semi-automatic machine learning pipeline (SAMLP) designed to address the challenges correlated with machine learning model development. Through this pipeline, we develop a comprehensive bot detection model named BotArtist, based on user profile features. SAMLP leverages nine distinct publicly available datasets to train the BotArtist model. To assess BotArtist's performance against current state-of-the-art solutions, we select 35 existing Twitter bot detection methods, each utilizing a diverse range of features. Our comparative evaluation of BotArtist and these existing methods, conducted across nine public datasets under standardized conditions, reveals that the proposed model outperforms existing solutions by almost 10%, in terms of F1-score, achieving an average score of 83.19 and 68.5 over specific and general approaches respectively. As a result of this research, we provide a dataset of the extracted features combined with BotArtist predictions over the 10.929.533 Twitter user profiles, collected via Twitter API during the 2022 Russo-Ukrainian War, over a 16-month period. This dataset was created in collaboration with [Shevtsov et al., 2022a] where the original authors share anonymized tweets on the discussion of the Russo-Ukrainian war with a total amount of 127.275.386 tweets. The combination of the existing text dataset and the provided labeled bot and human profiles will allow for the future development of a more advanced bot detection large language model in the post-Twitter API era.

A primary objective of the forthcoming sixth generation (6G) of wireless networking is to support demanding applications, while ensuring energy efficiency. Programmable wireless environments (PWEs) have emerged as a promising solution, leveraging reconfigurable intelligent surfaces (RISs), to control wireless propagation and deliver exceptional quality-ofservice. In this paper, we analyze the performance of a network supported by zero-energy RISs (zeRISs), which harvest energy for their operation and contribute to the realization of PWEs. Specifically, we investigate joint energy-data rate outage probability and the energy efficiency of a zeRIS-assisted communication system by employing three harvest-and-reflect (HaR) methods, i) power splitting, ii) time switching, and iii) element splitting. Furthermore, we consider two zeRIS deployment strategies, namely BS-side zeRIS and UE-side zeRIS. Simulation results validate the provided analysis and examine which HaR method performs better depending on the zeRIS placement. Finally, valuable insights and conclusions for the performance of zeRISassisted wireless networks are drawn from the presented results.

Reconfigurable intelligent surfaces (RISs) are artificial planar structures able to offer a unique way of manipulating propagated wireless signals. Commonly composed of a number of reconfigurable passive cell components and basic electronic circuits, RISs can almost freely perform a set of wave modification functionalities, in order to realize programmable wireless environments (PWEs). However, a more energy-efficient way to realize a PWE is through dynamically relocating static metasurfaces that perform a unique functionality. In this paper, we employ a UAV swarm to dynamically deploy a set of lowcost passive metasurfaces that are able to perform only one electromagnetic functionality, but with the benefit of requiring no power. Specifically, the UAV-mounted static metasurfaces are carefully positioned across the sky to create cascaded channels for improved user service and security hardening. The performance evaluation results, based on

Reconfigurable Intelligent Surfaces (RIS) constitute a promising technology that could fulfill the extreme performance and capacity needs of the upcoming 6G wireless networks, by offering software-defined control over wireless propagation phenomena. Despite the existence of many theoretical models describing various aspects of RIS from the signal processing perspective (e.g., channel fading models), there is no open platform to simulate and study their actual physical-layer behavior, especially in the multi-RIS case. In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially designing a basic unit cell, and then proceeding to progressively model and simulate multiple and larger RISs. The platform can be used for producing verifiable stochastic models for wireless communication in multi-RIS deployments, such as vehicle-to-everything (V2X) communications in autonomous vehicles and cybersecurity schemes, while its code is freely available to the public.

Modern Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices, with the data acquisition and processing infrastructure setting restrictions in terms of computational power and energy resources. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies, yielding a reduced set of observations. Nevertheless, this can hamper dramatically subsequent decision-making, such as forecasting. To address this problem, in this work we evaluate short-term forecasting in highly underdetermined cases, i.e., the number of sensor streams is much higher than the number of observations. Several statistical, machine learning and neural network-based models are thoroughly examined with respect to the resulting forecasting accuracy on five different real-world datasets. The focus is given on a unified experimental protocol especially designed for short-term prediction of multiple time series at the IoT edge. The proposed framework can be considered as an important step towards establishing a solid forecasting strategy in resource constrained IoT applications., Comment: Accepted for publication in the "Edge-Fog-Cloud Machine Learning for Smart Cities Applications" Special Session at the European Signal Processing Conference (EUSIPCO) 2022

On 24 February 2022, Russia invaded Ukraine, also known now as Russo-Ukrainian War. We have initiated an ongoing dataset acquisition from Twitter API. Until the day this paper was written the dataset has reached the amount of 57.3 million tweets, originating from 7.7 million users. We apply an initial volume and sentiment analysis, while the dataset can be used to further exploratory investigation towards topic analysis, hate speech, propaganda recognition, or even show potential malicious entities like botnets.

Modern commodity computing systems are composed by a number of different heterogeneous processing units, each of which has its own unique performance and energy characteristics. However, the majority of current network packet processing frameworks targets only a specific processing unit (either the CPU or accelerator), leaving the remaining computational resources under-utilized or even idle. In this paper, we propose an adaptive scheduling approach for network packet processing applications, that supports any heterogeneous and asymmetric architectures that can be found in a commodity high-end hardware setup. Our scheduler not only distributes the workloads to the appropriate devices in the system to achieve the desired performance results, but also enables the multiplexing of diverse network packet processing applications that execute concurrently, eliminating the interference effects introduced at runtime. The evaluation results show that our scheduler is able to tackle interferences in the shared hardware resources as well to respond quickly to dynamic fluctuations (e.g., application overloads, traffic bursts, infrastructural changes, etc.) that may occur at real time.

Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious handling by various bots. The malicious handling phenomenon expands in online discourse, especially during the electoral periods, where except the legitimate bots used for dissemination and communication purposes, the goal is to manipulate the public opinion and the electorate towards a certain direction, specific ideology, or political party. This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. To this end, a supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm, where the hyper-parameters are tuned via cross-validation. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions by calculating feature importance, using the game theoretic-based Shapley values. Experimental evaluation on distinct Twitter datasets demonstrate the superiority of our approach, in terms of bot detection accuracy, when compared against a recent state-of-the-art Twitter bot detection method.

A very large number of people use Online Social Networks daily. Such platforms thus become attractive targets for agents that seek to gain access to the attention of large audiences, and influence perceptions or opinions. Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting maximum influence. Botnets may be used to better infiltrate the social graph over time and to create an illusion of community behavior, amplifying their message and increasing persuasion. This paper investigates Twitter botnets, their behavior, their interaction with user communities and their evolution over time. We analyzed a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period of 36 months. We detected over a million events where seemingly unrelated accounts tweeted nearly identical content at nearly the same time. We filtered these concurrent content injection events and detected a set of 1,850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same software. We found botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze statistical differences between bot accounts and human users, as well as botnet interaction with user communities and Twitter trending topics.

Intelligent surfaces exert deterministic control over the wireless propagation phenomenon, enabling novel capabilities in performance, security and wireless power transfer. Such surfaces come in the form of rectangular tiles that cascade to cover large surfaces such as walls, ceilings or building facades. Each tile is addressable and can receive software commands from a controller, manipulating an impinging electromagnetic wave upon it by customizing its reflection direction, focus, polarization and phase. A new problem arises concerning the orchestration of a set of tiles towards serving end-to-end communication objectives. Towards that end, we propose a novel intelligent surface networking algorithm based on interpretable neural networks. Tiles are mapped to neural network nodes and any tile line-of-sight connectivity is expressed as a neural network link. Tile wave manipulation functionalities are captured via geometric reflection with virtually rotatable tile surface norm, thus being able to tunable distribute power impinging upon a tile over the corresponding neural network links, with the corresponding power parts acting as the link weights. A feedforward/backpropagate process optimizes these weights to match ideal propagation outcomes (normalized network power outputs) to wireless user emissions (normalized network power inputs). An interpretation process translates these weights to the corresponding tile wave manipulation functionalities., Comment: The article received funding from the EU H2020 FETOPEN-RIA project VISORSURF (EU736876). Published at IEEE Transactions on Communications: https://ieeexplore.ieee.org/document/9151237

The presidential elections in the United States on 3 November 2020 have caused extensive discussions on social media. A part of the content on US elections is organic, coming from users discussing their opinions of the candidates, political positions, or relevant content presented on television. Another significant part of the content generated originates from organized campaigns, both official and by astroturfing. In this study, we obtain approximately 17.5M tweets containing 3M users, based on prevalent hashtags related to US election 2020, as well as the related YouTube links, contained in the Twitter dataset, likes, dislikes and comments of the videos and conduct volume, sentiment and graph analysis on the communities formed. Particularly, we study the daily traffic per prevalent hashtags, plot the retweet graph from July to September 2020, show how its main connected component becomes denser in the period closer to the elections and highlight the two main entities ('Biden' and 'Trump'). Additionally, we gather the related YouTube links contained in the previous dataset and perform sentiment analysis. The results on sentiment analysis on the Twitter corpus and the YouTube metadata gathered, show the positive and negative sentiment for the two entities throughout this period. The results of sentiment analysis indicate that 45.7% express positive sentiment towards Trump in Twitter and 33.8% positive sentiment towards Biden, while 14.55% of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7% positive sentiment towards Biden. Our analysis fill the gap between the connection of offline events and their consequences in social media by monitoring important events in real world and measuring public volume and sentiment before and after the event in social media.

Programmable wireless environments (PWEs) utilize electromagnetic metasurfaces to transform wireless propagation into a software-controlled resource. In this work we study the effects of user device mobility on the efficiency of PWEs. An analytical model is proposed, which describes the potential misalignment between user-emitted waves and the active PWE configuration, and can constitute the basis for studying queuing problems in PWEs. Subsequently, a novel, beam steering approach is proposed which can effectively mitigate the misalignment effects. Ray-tracing-based simulations evaluate the proposed scheme., Comment: In proceedings of IEEE ICASSP 2020. This work was funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN-RIA), grant EU736876, project VISORSURF (http://visorsurf.eu)

We present the concept and electromagnetic aspects of HyperSurFaces (HSFs), artificial, ultrathin structures with software controlled electromagnetic properties. The HSFs key unit is the metasurface, a plane with designed subwavelength features whose electromagnetic response can be tuned via voltage-controlled continuously-tunable electrical elements that provide local control of the surface impedance and advanced functionalities, such as tunable perfect absorption or wavefront manipulation. A nanonetwork of controllers enables software defined HSFs control related to the emerging Internet of Things paradigm.

Websites are constantly adapting the methods used, and intensity with which they track online visitors. However, the wide-range enforcement of GDPR since one year ago (May 2018) forced websites serving EU-based online visitors to eliminate or at least reduce such tracking activity, given they receive proper user consent. Therefore, it is important to record and analyze the evolution of this tracking activity and assess the overall "privacy health" of the Web ecosystem and if it is better after GDPR enforcement. This work makes a significant step towards this direction. In this paper, we analyze the online ecosystem of 3rd-parties embedded in top websites which amass the majority of online tracking through 6 time snapshots taken every few months apart, in the duration of the last 2 years. We perform this analysis in three ways: 1) by looking into the network activity that 3rd-parties impose on each publisher hosting them, 2) by constructing a bipartite graph of "publisher-to-tracker", connecting 3rd parties with their publishers, 3) by constructing a "tracker-to-tracker" graph connecting 3rd-parties who are commonly found in publishers. We record significant changes through time in number of trackers, traffic induced in publishers (incoming vs. outgoing), embeddedness of trackers in publishers, popularity and mixture of trackers across publishers. We also report how such measures compare with the ranking of publishers based on Alexa. On the last level of our analysis, we dig deeper and look into the connectivity of trackers with each other and how this relates to potential cookie synchronization activity.

Software-defined metasurfaces are electromagnetically ultra-thin, artificial components that can provide engineered and externally controllable functionalities. The control over these functionalities is enabled by the metasurface tunability, which is implemented by embedded electronic circuits that modify locally the surface resistance and reactance. Integrating controllers within the metasurface cells, able to intercommunicate and adaptively reconfigure it, thus imparting a desired electromagnetic operation, opens the path towards the creation of an artificially intelligent (AI) fabric where each unit cell can have its own sensing, programmable computing, and actuation facilities. In this work we take a crucial step towards bringing the AI metasurface technology to emerging applications, in particular exploring the wireless mm-wave intercell communication capabilities in a software-defined HyperSurface designed for operation is the microwave regime. We examine three different wireless communication channels within the landscape of the reflective metasurface: Firstly, in the layer where the control electronics of the HyperSurface lie, secondly inside a dedicated layer enclosed between two metallic plates, and, thirdly, inside the metasurface itself. For each case we examine the physical implementation of the mm-wave transponder nodes, we quantify communication channel metrics, and we identify complexity vs. performance trade-offs.

Software-defined metasurfaces (SDMs) comprise a dense topology of basic elements called meta-atoms, exerting the highest degree of control over surface currents among intelligent panel technologies. As such, they can transform impinging electromagnetic (EM) waves in complex ways, modifying their direction, power, frequency spectrum, polarity and phase. A well-defined software interface allows for applying such functionalities to waves and inter-networking SDMs, while abstracting the underlying physics. A network of SDMs deployed over objects within an area, such as a floorplan walls, creates programmable wireless environments (PWEs) with fully customizable propagation of waves within them. This work studies the use of machine learning for configuring such environments to the benefit of users within. The methodology consists of modeling wireless propagation as a custom, interpretable, back-propagating neural network, with SDM elements as nodes and their cross-interactions as links. Following a training period the network learns the propagation basics of SDMs and configures them to facilitate the communication of users within their vicinity., Comment: In proceedings of IEEE SPAWC 2019 - Special Session on Signal Processing Advances for Emerging Transceiver Hardware. This work was funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN), grant EU736876, project VISORSURF (http://www.visorsurf.eu)

Programmable, intelligent surfaces can manipulate electromagnetic waves impinging upon them, producing arbitrarily shaped reflection, refraction and diffraction, to the benefit of wireless users. Moreover, in their recent form of HyperSurfaces, they have acquired inter-networking capabilities, enabling the Internet of Material Properties with immense potential in wireless communications. However, as with any system with inputs and outputs, accurate sensing of the impinging wave attributes is imperative for programming HyperSurfaces to obtain a required response. Related solutions include field nano-sensors embedded within HyperSurfaces to perform minute measurements over the area of the HyperSurface, as well as external sensing systems. The present work proposes a sensing system that can operate without such additional hardware. The novel scheme programs the HyperSurface to perform compressed sensing of the impinging wave via simple one-antenna power measurements. The HyperSurface can jointly be programmed for both wave sensing and wave manipulation duties at the same time. Evaluation via simulations validates the concept and highlight its promising potential., Comment: Published at IEEE DCOSS 2019 / IoT4.0 workshop (https://www.dcoss.org/workshops.html). Funded by the European Union via the Horizon 2020: Future Emerging Topics - Research and Innovation Action call (FETOPEN-RIA), grant EU736876, project VISORSURF (http://www.visorsurf.eu)

The traditional cycle of industrial products has been linear since its inception. Raw resources are acquired, processed, distributed, used and ultimately disposed of. This linearity has led to a dangerously low efficiency degree in resource use, and has brought forth serious concerns for the viability of our natural ecosystem. Circular economy is introducing a circular workflow for the lifetime of products. It generalizes the disposal phase, reconnecting it to manufacturing, distribution and end-use, thus limiting true deposition to the environment. This process has not been extended so far to software. Nonetheless, the development of software follows the same phases, and also entails the use-and waste-of considerable resources. This include human effort, as well as human and infrastructure sustenance products such as food, traveling and energy. This paper introduces circular economy principles to the software development, and particularly to network management logic and security. It employs a recently proposed concept-the Socket Store-which is an online store distributing end-user network logic in modular form. The Store modules act as mediators between the end-user network logic and the network resources. It is shown that the Socket Store can implement all circular economy principles to the software life-cycle, with considerable gains in resource waste., Comment: Published at IEEE DCOSS 2019 / smaCE workshop (https://www.dcoss.org/workshops.html). Funded by the European Union via projects "CE-IoT: A Framework for Pairing Circular Economy and IoT" (Marie Sklodowska-Curie RISE action, GA EU777855) and "VISORSURF: A Hardware Platform for Software-driven Functional Metasurfaces" (Future Emerging Topics - FETOPEN-RIA, GA EU736876)

HyperSurfaces (HSFs) are devices whose electromagnetic (EM) behavior is software-driven, i.e., it can be defined programmatically. The key components of this emerging technology are the metasurfaces, artificial layered materials whose EM properties depend on their internal subwavelength structuring. HSFs merge metasurfaces with a network of miniaturized custom electronic controllers, the nanonetwork, in an integrated scalable hardware platform. The nanonetwork receives external programmatic commands expressing the desired end-functionality and appropriately alters the metasurface configuration thus yielding the respective EM behavior for the HSF. In this work, we will present all the components of the HSF paradigm, as well as highlight the underlying challenges and future prospects., Comment: 3 pages, 1 figure, 12th International Congress on Artificial Materials for Novel Wave Phenomena (METAMATERIALS 2018)

The exposure of location data constitutes a significant privacy risk to users as it can lead to de-anonymization, the inference of sensitive information, and even physical threats. In this paper we present LPAuditor, a tool that conducts a comprehensive evaluation of the privacy loss caused by publicly available location metadata. First, we demonstrate how our system can pinpoint users' key locations at an unprecedented granularity by identifying their actual postal addresses. Our experimental evaluation on Twitter data highlights the effectiveness of our techniques which outperform prior approaches by 18.9%-91.6% for homes and 8.7%-21.8% for workplaces. Next we present a novel exploration of automated private information inference that uncovers "sensitive" locations that users have visited (pertaining to health, religion, and sex/nightlife). We find that location metadata can provide additional context to tweets and thus lead to the exposure of private information that might not match the users' intentions. We further explore the mismatch between user actions and information exposure and find that older versions of the official Twitter apps follow a privacy-invasive policy of including precise GPS coordinates in the metadata of tweets that users have geotagged at a coarse-grained level (e.g., city). The implications of this exposure are further exacerbated by our finding that users are considerably privacy-cautious in regards to exposing precise location data. When users can explicitly select what location data is published, there is a 94.6% reduction in tweets with GPS coordinates. As part of current efforts to give users more control over their data, LPAuditor can be adopted by major services and offered as an auditing tool that informs users about sensitive information they (indirectly) expose through location metadata.

Programmable wireless environments enable the software-defined propagation of waves within them, yielding exceptional performance potential. Several building-block technologies have been implemented and evaluated at the physical layer. The present work contributes a network-layer scheme to configure such environments for multiple users and objectives, and for any physical-layer technology. Supported objectives include any combination of Quality of Service and power transfer optimization, eavesdropping and Doppler effect mitigation, in multi-cast or uni-cast settings. Additionally, a graph-based model of programmable environments is proposed, which incorporates core physical observations and efficiently separates physical and networking concerns. Evaluation takes place in a specially developed, free simulation tool, and in a variety of environments. Performance gains over regular propagation are highlighted, reaching important insights on the user capacity of programmable environments., Comment: This work is part of project VISORSURF: A HyperVisor for Metasurface Functionalities (www.visorsurf.eu). Funded by the European Union Horizon 2020, under the Future Emerging Technologies - Research and Innovation Actions call (Grant Agreement EU 736876)

Although digital advertising fuels much of today's free Web, it typically does so at the cost of online users' privacy, due to the continuous tracking and leakage of users' personal data. In search for new ways to optimize the effectiveness of ads, advertisers have introduced new advanced paradigms such as cross-device tracking (CDT), to monitor users' browsing on multiple devices and screens, and deliver (re)targeted ads in the most appropriate screen.Unfortunately, this practice leads to greater privacy concerns for the end-user. Going beyond the state-of-the-art, we propose a novel methodology for detecting CDT and measuring the factors affecting its performance, in a repeatable and systematic way. This new methodology is based on emulating realistic browsing activity of end-users, from different devices, and thus triggering and detecting cross-device targeted ads. We design and build Talon a CDT measurement framework that implements our methodology and allows experimentation with multiple parallel devices, experimental setups and settings. By employing Talon, we perform several critical experiments, and we are able to not only detect and measure CDT with average AUC score of 0.78-0.96, but also to provide significant insights about the behavior of CDT entities and the impact on users' privacy. In the hands of privacy researchers, policy makers and end-users, Talon can be an invaluable tool for raising awareness and increasing transparency on tracking practices used by the ad-ecosystem.

Wireless communication environments comprise passive objects that cause performance degradation and eavesdropping concerns due to anomalous scattering. This paper proposes a new paradigm, where scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Through the proposed programmable wireless environments, the path loss, multi-path fading and interference effects can be controlled and mitigated. Moreover, the eavesdropping can be prevented via novel physical layer security capabilities. The core technology of this new paradigm is the concept of metasurfaces, which are planar intelligent structures whose effects on impinging electromagnetic waves are fully defined by their micro-structure. Their control over impinging waves has been demonstrated to span from 1 GHz to 10 THz. This paper contributes the software-programmable wireless environment, consisting of several HyperSurface tiles (programmable metasurfaces) controlled by a central server. HyperSurfaces are a novel class of metasurfaces whose structure and, hence, electromagnetic behavior can be altered and controlled via a software interface. Multiple networked tiles coat indoor objects, allowing fine-grained, customizable reflection, absorption or polarization overall. A central server calculates and deploys the optimal electromagnetic interaction per tile, to the benefit of communicating devices. Realistic simulations using full 3D ray-tracing demonstrate the groundbreaking performance and security potential of the proposed approach in 2.4 GHz and 60 GHz frequencies., Comment: This work was partially funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN), grant EU736876, project VISORSURF. admin note: significant overlap with arXiv:1805.06677

The proliferation of web applications has essentially transformed modern browsers into small but powerful operating systems. Upon visiting a website, user devices run implicitly trusted script code, the execution of which is confined within the browser to prevent any interference with the user's system. Recent JavaScript APIs, however, provide advanced capabilities that not only enable feature-rich web applications, but also allow attackers to perform malicious operations despite the confined nature of JavaScript code execution. In this paper, we demonstrate the powerful capabilities that modern browser APIs provide to attackers by presenting MarioNet: a framework that allows a remote malicious entity to control a visitor's browser and abuse its resources for unwanted computation or harmful operations, such as cryptocurrency mining, password-cracking, and DDoS. MarioNet relies solely on already available HTML5 APIs, without requiring the installation of any additional software. In contrast to previous browser-based botnets, the persistence and stealthiness characteristics of MarioNet allow the malicious computations to continue in the background of the browser even after the user closes the window or tab of the initial malicious website. We present the design, implementation, and evaluation of a prototype system, MarioNet, that is compatible with all major browsers, and discuss potential defense strategies to counter the threat of such persistent in-browser attacks. Our main goal is to raise awareness regarding this new class of attacks, and inform the design of future browser APIs so that they provide a more secure client-side environment for web applications.

The present study proposes the use of intelligent metasurfaces in the design of products, as enforcers of circular economy principles. Intelligent metasurfaces can tune their physical properties (electromagnetic, acoustic, mechanical) by receiving software commands. When incorporated within products and spaces they can mitigate the resource waste caused by inefficient, partially optimized designs and security concerns. Thus, circular economy and fast-paced product design become compatible. The study begins by considering electromagnetic metamaterials, and proposes a complete methodology for their deployment. Finally, it is shown that the same principles can be extended to the control of mechanical properties of objects, exemplary enabling the micro-management of vibrations and heat, with unprecedented circular economy potential., Comment: Accepted for publication at IEEE CAMAD 2018 Special Session on Circular Economy. Partially funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN), grant EU736876, project VISORSURF (http://www.visorsurf.eu)

Nano-IoT enables a wide range of ground-breaking technologies, but face implementation challenges due to the extremity of the scale. Space restrictions pose severe power supply considerations, to the point where just a few packet transmissions are sufficient to deplete state-of-the-art supplies. In turn, this translates to difficulties in developing efficient protocols even for basic operations, such as addressing and routing. The present work proposes a new network adapter architecture that can address these challenges. The BitSurfing adapter does not generate packets and, hence, abolishes the need for the corresponding transmission circuitry and power consumption. Instead, it relies on an external symbol generator. The BitSurfing adapter reads incoming symbols, waiting for intended messages to appear in the symbol stream. A short (1-bit), low-energy pulse is then emitted to notify neighboring nodes. BitSurfing adapters are shown to exhibit perpetual (and even battery-less) operation, ability to operate without medium access control, while being completely transparent to applications. Moreover, their operation is event-driven, allowing for clock-less implementations. The new adapters are evaluated in a simulated multi-hop nano-IoT network and are shown to offer nearly-perfect packet delivery rates and practically no collisions, under any congestion level., Comment: Paper accepted for publication at IEEE CAMAD 2018 main track. Funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN), grant EU736876, project VISORSURF (http://www.visorsurf.eu)

This article introduces an approach that could tame wireless channels, making their behavior deterministic and software-defined. We investigate the novel idea of HyperSurfaces, which are software-controlled metamaterials embedded in any surface in the environment. HyperSurfaces are materials that interact with electromagnetic waves in a fully software-defined fashion, even unnaturally. Coating walls, doors, furniture and other objects with HyperSurfaces constitutes the overall behavior of an indoor wireless environment programmable. Thus, the electromagnetic behavior of the environment as a whole can be controlled and tailored to the needs of mobile devices within it., Comment: Paper accepted as viewpoint article in Communications of the ACM. (Accepted on 1-Mar-2018). This work was funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN-RIA), grant EU736876, project VISORSURF: HyperSurfaces-A Hardware Platform for Software-driven Functional Metasurfaces (http://www.visorsurf.eu/)

Electromagnetic waves undergo multiple uncontrollable alterations as they propagate within a wireless environment. Free space path loss, signal absorption, as well as reflections, refractions and diffractions caused by physical objects within the environment highly affect the performance of wireless communications. Currently, such effects are intractable to account for and are treated as probabilistic factors. The paper proposes a radically different approach, enabling deterministic, programmable control over the behavior of the wireless environments. The key-enabler is the so-called HyperSurface tile, a novel class of planar meta-materials which can interact with impinging electromagnetic waves in a controlled manner. The HyperSurface tiles can effectively re-engineer electromagnetic waves, including steering towards any desired direction, full absorption, polarization manipulation and more. Multiple tiles are employed to coat objects such as walls, furniture, overall, any objects in the indoor and outdoor environments. An external software service calculates and deploys the optimal interaction types per tile, to best fit the needs of communicating devices. Evaluation via simulations highlights the potential of the new concept., Comment: Paper accepted for publication at the IEEE Communications Magazine. This work was funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN-RIA), grant EU736876, project VISORSURF: HyperSurfaces-A Hardware Platform for Software-driven Functional Metasurfaces (http://www.visorsurf.eu/)

Wireless communication environments are unaware of the ongoing data exchange efforts within them. Moreover, their effect on the communication quality is intractable in all but the simplest cases. The present work proposes a new paradigm, where indoor scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Moreover, the controlled scattering can surpass natural behavior, exemplary overriding Snell's law, reflecting waves towards any custom angle (including negative ones). Thus, path loss and multi-path fading effects can be controlled and mitigated. The core technology of this new paradigm are metasurfaces, planar artificial structures whose effect on impinging electromagnetic waves is fully defined by their macro-structure. The present study contributes the software-programmable wireless environment model, consisting of several HyperSurface tiles controlled by a central, environment configuration server. HyperSurfaces are a novel class of metasurfaces whose structure and, hence, electromagnetic behavior can be altered and controlled via a software interface. Multiple networked tiles coat indoor objects, allowing fine-grained, customizable reflection, absorption or polarization overall. A central server calculates and deploys the optimal electromagnetic interaction per tile, to the benefit of communicating devices. Realistic simulations using full 3D ray-tracing demonstrate the groundbreaking potential of the proposed approach in 2.4 GHz and 60 GHz frequencies., Comment: This paper appears at the 19TH IEEE WOWMOM 2018, JUNE 12-15, 2018. (Technical program: http://it.murdoch.edu.au/wowmom2018/technical_program.html) This work was funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN-RIA), grant EU736876, project VISORSURF (http://www.visorsurf.eu) : HyperSurfaces-A Hardware Platform for Software-driven Functional Metasurfaces

A developer of mobile or desktop applications is responsible for implementing the network logic of his software. Nonetheless: i) Developers are not network specialists, while pressure for emphasis on the visible application parts places the network logic out of the coding focus. Moreover, computer networks undergo evolution at paces that developers may not follow. ii) From the network resource provider point of view, marketing novel services and involving a broad audience is also challenge for the same reason. Moreover, the objectives of end-user networking logic are neither clear nor uniform. This constitutes the central optimization of network resources an additional challenge. As a solution to these problems, we propose the Socket Store. The Store is a marketplace containing end-user network logic in modular form. The Store modules act as intelligent mediators between the end-user and the network resources. Each module has a clear, specialized objective, such as connecting two clients over the Internet while avoiding transit networks suspicious for eavesdropping. The Store is populated and peer-reviewed by network specialists, whose motive is the visibility, practical applicability and monetization potential of their work. A developer first purchases access to a given socket module. Subsequently, he incorporates it to his applications under development, obtaining state-of-the-art performance with trivial coding burden. A full Store prototype is implemented and a critical data streaming module is evaluated as a driving case.

A recently proposed class of materials, called software-defined metamaterials, can change their electromagnetic behavior on demand, utilizing a nanonetwork embedded in their structure. The present work focuses on 2D metamaterials, known as metasurfaces, and their electromagnetically programmable counterparts, the HyperSurfaces. The particular focus of the study is to propose a nanonetworking protocol that can support the intended macroscopic functionality of a HyperSurface, such as sensing and reacting to impinging waves in a customizable manner. The novel protocol is derived analytically, using the Lyapunov drift minimization approach, taking into account nano-node energy, communication latency and complexity concerns. The proposed scheme is evaluated via simulations, covering both the macroscopic HyperSurface functionality and the microscopic, nanonetwork behavior.

In the fight against tax evaders and other cheats, governments seek to gather more information about their citizens. In this paper we claim that this increased transparency, combined with ineptitude, or corruption, can lead to widespread violations of privacy, ultimately harming law-abiding individuals while helping those engaged in criminal activities such as stalking, identity theft and so on. In this paper we survey a number of data sources administrated by the Greek state, offered as web services, to investigate whether they can lead to leakage of sensitive information. Our study shows that we were able to download significant portions of the data stored in some of these data sources (scraping). Moreover, for those data sources that were not amenable to scraping we looked at ways of extracting information for specific individuals that we had identified by looking at other data sources. The vulnerabilities we have discovered enable the collection of personal data and, thus, open the way for a variety of impersonation attacks, identity theft, confidence trickster attacks and so on. We believe that the lack of a big picture which was caused by the piecemeal development of these data sources hides the true extent of the threat. Hence, by looking at all these data sources together, we outline a number of mitigation strategies that can alleviate some of the most obvious attack strategies. Finally, we look at measures that can be taken in the longer term to safeguard the privacy of the citizens.