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Adversarial Obstruction of Millimeter Wave Links
Get PDFWe use a stochastic geometry approach to study an adversarial attack to the physical layer of millimeter wave communications, which is extremely vulnerable to blockage due to obstructions. Previous investigations have applied stochastic geometry to the study of millimeter wave communication scenarios with randomly located obstructions, but in this paper we investigate what happens if some of them are actually due to a malicious attacker. It turns out that, with just few strategically positioned obstructions, an adversary can significantly hinder the millimeter wave link operation and cause extremely high outage probabilities. As expected, scenarios with multiple reflections are more robust against this kind of attack, since they can exploit the diversity of reflected paths, despite their lower quality. Conversely, millimeter wave communications without (or with limited) multipath diversity are shown to be extremely fragile. We also investigate the impact on blockage of different parameters of the obstructions, including their number, shape, and size. Finally, we elaborate the applications of our findings to identify countermeasures against this attack.Ministry of Economic Affairs and Digital Transformation and the European Union NextGeneration-EU in the framework of the Spanish Recovery, Transformation and Resilience PlanTRUEpu
Cashing in on Contacts: Characterizing the OnlyFans Ecosystem
Get PDFAdult video-sharing has undergone dramatic shifts. New platforms that directly interconnect (often amateur) producers and consumers now allow content creators to promote material across the web and directly monetize the content they produce. OnlyFans is the most prominent example of this new trend. OnlyFans is a content subscription service where creators earn money from users who subscribe to their material. In contrast to prior adult platforms, OnlyFans emphasizes creator-consumer interaction for audience accumulation and maintenance. This results in a wide cross-platform ecosystem geared towards bringing consumers to creators’ accounts. In this paper, we inspect this emerging ecosystem, focusing on content creators and the third-party platforms they connect to.TRUEpu
Characterizing and Modeling Mobile Networks User Traffic at Millisecond Level
Get PDFThe availability of datasets has been instrumental to drive advances in several disciplines like computer vision, image processing, and natural language processing. However, in the context of mobile traffic, data is often not available because of diverse reasons including data sensitivity, legal considerations and business competition. The lack of dataset availability restrains the research advance at large.
In this paper, we make a twofold contribution. On the one hand, we make available a large dataset of mobile traffic from multiple Base Stations (BSs). The key distinct feature of the dataset is in the nature of the data, which is based on real LTE traffic information decoded from control channel information at the millisecond level. On the other hand, we carry out an in-depth characterization of user traffic and study how widely adopted probability distributions for mobile traffic do apply at short-term scales. Our analysis shows that mobile data traffic exhibits self-similarity and the number of Radio Resource Control (RRC) connected users exhibits a bi-modal distribution. Overall, our contribution key to verify and reproduce research outcomes as well as driving advances of Artificial Intelligence (AI)/Machine Learning (ML) applied
to mobile networks.Ministerio de Ciencia e InnovaciónMinisterio de Empleo y Economía SocialBest Paper Runner-Up AwardTRUEpu
Adaptative generalisation over a value hierarchy for the k-anonymisation of Origin-Destination matrices
Get PDFThe study of transportation relies on mobility data, containing information on the whereabouts and movements of individuals in a study area. These data are often represented in the simple form of origin-destination (OD)-matrices, which are a valuable indicator for the management of transportation networks but also present risks to the privacy of the individuals. In particular, the significant size (i.e., number of distinct flows) and high number of modalities (produced by a high-resolution zoning) of OD-matrices call for an adapted, fast algorithm that can efficiently anonymise them. In this paper, we develop a lightweight approach for the k-anonymisation of OD-matrices that exploits the low dimension of the data to explore a larger solution space than regular generalisation algorithms, while keeping relevant restrictions of the search space in order to be scalable on matrices with high number of flows. We apply it to a variety of real-world large-scale O-D matrices collected by the New York City Taxi and Limousine Commission and derived from the Data for Development (D4D) challenge organised by Orange in Senegal and Coˆte d’Ivoire. Compared to an extensive benchmark of regular generalisation algorithms and mobility anonymisation state-of-the-art, we show that our method is 27% more precise and 9 times faster than comparable approaches able to scale on the same datasets.TRUEpu
Optimizing Fairness in Cellular Networks with Mobile Drone Relays
Get PDFAiding the ground cellular network with aerial base stations carried by drones has experienced an intensive raise of interest in the past years. Reconfigurable air-to-ground channels enable aerial stations to enhance users’ access links by means of seeking good line-of-sight connectivity while hovering in the air. In this article, we propose an analytical framework for the 3D placement of a fleet of coordinated drone relays. This framework optimizes network performance in terms of user throughput fairness, expressed through the α-fairness metric. The optimization problem is formulated as a mixed-integer non-convex program, which is intractable. Hence, we propose an extremal-optimization-based algorithm, Parallelized Alpha-fair Drone Deployment (PADD), which solves the problem online, in low-degree polynomial time. We evaluate our proposal by means of numerical simulations over the real topology of a dense city. We discuss the advantages of integrating drone relay stations in current networks and test several resource scheduling approaches in both static and dynamic scenarios, including with progressively larger and denser crowds.TRUEpu
Explainability Analysis in Predictive Models Based on Machine Learning Techniques on the Risk of Hospital Readmissions
Get PDFPurpose: Analyzing the risk of re-hospitalization of patients with chronic diseases allows the healthcare institutions can deliver accurate preventive care to reduce hospital admissions, and the planning of the medical spaces and resources. Thus, the research question is: Is it possible to use artificial intelligence to study the risk of re-hospitalization of patients?
Methods: This article presents several models to predict when a patient can be hospitalized again, after its discharge. In addition, an explainability analysis is carried out with the predictive models to extract information to determine the degree of importance of the predictors/descriptors. Particularly, this article makes a comparative analysis of different explainability techniques in the study context.
Results: The best model is a classifier based on decision trees with an F1-Score of 83% followed by LGMB with an F1-Score of 67%. For these models, Shapley values were calculated as a method of explainability. Concerning the quality of the explainability of the predictive models, the stability metric was used. According to this metric, more variability is evidenced in the explanations of the decision trees, where only 4 attributes are very stable (21%) and 1 attribute is unstable. With respect to the LGBM-based model, there are 12 stable attributes (63%) and no unstable attributes. Thus, in terms of explainability, the LGBM-based model is better.
Conclusions: According to the results of the explanations generated by the best predictive models, LGBM-based predictive model presents more stable variables. Thus, it generates greater confidence in the explanations it provides.TRUEpu
Stories from a Customized Honeypot for the IoT
Get PDFSince the inception of the Internet of Things (IoT), the security measures implemented on its devices have been too weak to ensure the appropriate protection of the data that they handle. Appealed by this, cybercriminals continuously seek out for vulnerable units to control, leading to attacks spreading through networks and infecting a high number of devices. On top of that, while the IoT has evolved to provide a higher degree of security, the techniques used by attackers have done so as well, which has led to the need of continuously studying the way in which these attacks are performed to gather significant knowledge for the development of the pertinent security measures.
In view of this, we analyze the state of IoT attacks by developing a high-interaction honeypot for SSH and Telnet services that simulates a custom device with the ARM architecture. This study is carried out in two steps. Firstly, we analyze and classify the interaction between the attacker and the devices by clustering the commands that they sent in the compromised Telnet and SSH sessions. Secondly, we study the malware samples that are downloaded and executed in each session and classify them based on the sequence of system calls that they execute at runtime. In addition, apart from studying the active data generated by the attacker, we extract the information that is left behind after a connection with the honeypot by inspecting the metadata associated with it. In total, more than 1,578 malicious samples were collected after 9,926 unique IP addresses interacted with the system, with the most downloaded malware family being Hajime, with 70.5% of samples belonging to it, and also detecting others such as Mirai, Gafgyt, Dofloo and Xorddos.MCIN/AEI/10.13039/501100011033 and ESF "The European Social Fund invests in your future"TRUEpu
An analysis of fake social media engagement services
Get PDFFake engagement services allow users of online social media and other web platforms to illegitimately increase their online reach and boost their perceived popularity. Driven by socio-economic and even political motivations, the demand for fake engagement services has increased in the last years, which has incentivized the rise of a vast underground market and support infrastructure. Prior research in this area has been limited to the study of the infrastructure used to provide these services (e.g., botnets) and to the development of algorithms to detect and remove fake activity in online targeted platforms. Yet, the platforms in which these services are sold (known as panels) and the underground markets offering these services have not received much research attention. To fill this knowledge gap, this paper studies Social Media Management (SMM) panels, i.e., reselling platforms—often found in underground forums—in which a large variety of fake engagement services are offered. By daily crawling 86 representative SMM panels for 4 months, we harvest a dataset with 2.8 M forum entries grouped into 61k different services. This dataset allows us to build a detailed catalog of the services for sale, the platforms they target, and to derive new insights on fake social engagement services and its market. We then perform an economic analysis of fake engagement services and their trading activities by automatically analyzing 7k threads in underground forums. Our analysis reveals a broad range of offered services and levels of customization, where buyers can acquire fake engagement services by selecting features such as the quality of the service, the speed of delivery, the country of origin, and even personal attributes of the fake account (e.g., gender). The price analysis also yields interesting empirical results, showing significant disparities between prices of the same product across different markets. These observations suggest that the market is still undeveloped and sellers do not know the real market value of the services that they offer, leading them to underprice or overprice their services.TRUEpu
LeakageScatter: Backscattering LiFi-leaked RF Signals
Get PDFRadio-Frequency (RF) backscatter has emerged as a low-power communication technique. Backscatter systems either rely on active signal generators (spectrum efficient, but dedicated infrastructure) or existing ambient wireless transmissions (existing infrastructure, but spectrum inefficient). In this paper, we aim to make RF backscatter spectrum efficient and at the same time work with existing infrastructure. We propose to leverage the deployment of LiFi networks built upon LED bulbs for pervasive RF backscatter. We experimentally demonstrate that LiFi, which passively leaks RF signals, can be exploited as a radio carrier generator for low-power RF backscatter. We further design LeakageScatter, the first backscatter system operating in the ISM band and exploiting LiFi-leaked RF signals, without the need to actively generate the carrier wave. We customize the design of the loop at the LiFi transmitter, as well as the coil antennas at the tag and RF backscatter receiver, to optimize the system performance. We propose to opportunistically enable the oscillator of the backscatter tag in the software that could reduce the energy consumption on backscattering by up to 75%. Experimental results show that LeakageScatter achieves a backscattering distance up to 10 m and 18 m in indoor and outdoor scenarios, respectively, without using a dedicated RF carrier generator.Ministerio de Asuntos Económicos y Transformación DigitalTRUEpu
Log: It’s Big, It’s Heavy, It’s Filled with Personal Data! Measuring the Logging of Sensitive Information in the Android Ecosystem
Get PDFAndroid offers a shared system that multiplexes all logged data from all system components, including both the operating system and the console output of apps that run on it. A security mechanism ensures that user-space apps can only read the log entries that they create, though many "privileged" apps are exempt from this restriction. This includes preloaded system apps provided by Google, the phone manufacturer, the cellular carrier, as well as those sharing the same signature. Consequently, Google advises developers to not log sensitive information to the system log.
In this work, we examined the logging of sensitive data in the Android ecosystem. Using a field study, we show that most devices log some amount of user-identifying information. We show that the logging of "activity" names can inadvertently reveal information about users through their app usage. We also tested whether different smartphones log personal identifiers by default, examined preinstalled apps that access the system logs, and analyzed the privacy policies of manufacturers that report collecting system logs.TRUEpu