Measurement and Analysis of the Swarm Social Network With Tens of Millions of Nodes

Social graphs have been widely used for representing the relationship among users in online social networks (OSNs). As crawling an entire OSN is resource-and time-consuming, most of the existing works only pick a sampled subgraph for study. However, this may introduce serious inaccuracy into the analytic results, not to mention that some important metrics cannot even be calculated. In this paper, we crawl the entire social network of Swarm, a leading mobile social app with more than 60 million users, using a distributed approach. Based on the crawled massive user data, we conduct a data-driven study to get a comprehensive picture of the whole Swarm social network. This paper provides a deep analysis of social interactions between Swarm users, and reveals the relationship between social connectivity and check-in activities.Peer reviewe

I Know Where You are and What You are Sharing: Exploiting P2P Communications to Invade Users' Privacy

In this paper, we show how to exploit real-time communication applications to determine the IP address of a targeted user. We focus our study on Skype, although other real-time communication applications may have similar privacy issues. We first design a scheme that calls an identified targeted user inconspicuously to find his IP address, which can be done even if he is behind a NAT. By calling the user periodically, we can then observe the mobility of the user. We show how to scale the scheme to observe the mobility patterns of tens of thousands of users. We also consider the linkability threat, in which the identified user is linked to his Internet usage. We illustrate this threat by combining Skype and BitTorrent to show that it is possible to determine the file-sharing usage of identified users. We devise a scheme based on the identification field of the IP datagrams to verify with high accuracy whether the identified user is participating in specific torrents. We conclude that any Internet user can leverage Skype, and potentially other real-time communication systems, to observe the mobility and file-sharing usage of tens of millions of identified users.Comment: This is the authors' version of the ACM/USENIX Internet Measurement Conference (IMC) 2011 pape

Amplifying the Prediction of Team Performance through Swarm Intelligence and Machine Learning

Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. The present research examines the effects of group personality composition on team performance and investigates the impact of measuring GPC through ASI systems. 541 participants, across 111 groups, were administered a set of well-accepted and vetted psychometric assessments to capture the personality configurations and social sensitivities of teams. While group-level personality averages explained 10% of the variance in team performance, when group personality composition was measured through human swarms, it was able to explain 29% of the variance, representing a 19% amplification in predictive capacity. Finally, a series of machine learning models were applied and trained to predict group effectiveness. Multivariate Linear Regression and Logistic Regression achieved the highest performance exhibiting 0.19 mean squared error and 81.8% classification accuracy

DeepPredict : A zone preference prediction system for online lodging platforms

Publisher Copyright: © The author(s) 2021.Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user's booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (Pals) recommendation are mainly focused on users' historical records in the same city, while in practice, the historical records of a user in the same city would be very sparse. (2) Since each city has its own specific geographical entities, it is hard to extract the structured geographical features of accommodation in different cities. Towards the difficulties, we propose DeepPredict, a zone preference prediction system. To tackle the first challenge, DeepPredict involves users' historical records in all the cities and uses a deep learning based method to process them. For the second challenge, DeepPredict uses HERE places API to get the information of pals nearby, and processes the information with a unified way to get it. Also, the description of each accommodation might include some useful information, thus we use Sent2Vec, a sentence embedding algorithm, to get the embedding of accommodation description. Using a real-world dataset collected from Airbnb, DeepPredict can predict the zone preferences of users' next bookings with a remarkable performance. DeepPredict outperforms the state-of-the-art algorithms by 60% in macro Fl-score.Peer reviewe

Study of Peer-to-Peer Network Based Cybercrime Investigation: Application on Botnet Technologies

The scalable, low overhead attributes of Peer-to-Peer (P2P) Internet protocols and networks lend themselves well to being exploited by criminals to execute a large range of cybercrimes. The types of crimes aided by P2P technology include copyright infringement, sharing of illicit images of children, fraud, hacking/cracking, denial of service attacks and virus/malware propagation through the use of a variety of worms, botnets, malware, viruses and P2P file sharing. This project is focused on study of active P2P nodes along with the analysis of the undocumented communication methods employed in many of these large unstructured networks. This is achieved through the design and implementation of an efficient P2P monitoring and crawling toolset. The requirement for investigating P2P based systems is not limited to the more obvious cybercrimes listed above, as many legitimate P2P based applications may also be pertinent to a digital forensic investigation, e.g, voice over IP, instant messaging, etc. Investigating these networks has become increasingly difficult due to the broad range of network topologies and the ever increasing and evolving range of P2P based applications. In this work we introduce the Universal P2P Network Investigation Framework (UP2PNIF), a framework which enables significantly faster and less labour intensive investigation of newly discovered P2P networks through the exploitation of the commonalities in P2P network functionality. In combination with a reference database of known network characteristics, it is envisioned that any known P2P network can be instantly investigated using the framework, which can intelligently determine the best investigation methodology and greatly expedite the evidence gathering process. A proof of concept tool was developed for conducting investigations on the BitTorrent network.Comment: This is a thesis submitted in fulfilment of a PhD in Digital Forensics and Cybercrime Investigation in the School of Computer Science, University College Dublin in October 201

Experimental analysis of the socio-economic phenomena in the BitTorrent ecosystem

BitTorrent is the most successful Peer-to-Peer (P2P) application and is responsible for a major portion of Internet traffic. It has been largely studied using simulations, models and real measurements. Although simulations and modelling are easier to perform, they typically simplify analysed problems and in case of BitTorrent they are likely to miss some of the effects which occur in real swarms. Thus, in this thesis we rely on real measurements. In the first part of the thesis we present the summary of measurement techniques used so far and we use it as a base to design our tools that allow us to perform different types of analysis at different resolution level. Using these tools we collect several large-scale datasets to study different aspects of BitTorrent with a special focus on socio-economic aspects. Using our datasets, we first investigate the topology of real BitTorrent swarms and how the traffic is actually exchanged among peers. Our analysis shows that the resilience of BitTorrent swarms is lower than corresponding random graphs. We also observe that ISP policies, locality-aware clients and network events (e.g., network congestion) lead to locality-biased composition of neighbourhood in the swarms. This means that the peer contains more neighbours from local provider than expected from purely random neighbours selection process. Those results are of interest to the companies which use BitTorrent for daily operations as well as for ISPs which carry BitTorrent traffic. In the next part of the thesis we look at the BitTorrent from the perspective of the content and content publishers in a major BitTorrent portals. We focus on the factors that seem to drive the popularity of the BitTorrent and, as a result, could affect its associated traffic in the Internet. We show that a small fraction of publishers (around 100 users) is responsible for more than two-thirds of the published content. Those publishers can be divided into two groups: (i) profit driven and (ii)fake publishers. The former group leverages the published copyrighted content (typically very popular) on BitTorrent portals to attract content consumers to their web sites for financial gain. Removing this group may have a significant impact on the popularity of BitTorrent portals and, as a result, may affect a big portion of the Internet traffic associated to BitTorrent. The latter group is responsible for fake content, which is mostly linked to malicious activity and creates a serious threat for the Bit- Torrent ecosystem and for the Internet in general. To mitigate this threat, in the last part of the thesis we present a new tool named TorrentGuard for the early detection of fake content that could help to significantly reduce the number of computer infections and scams suffered by BitTorrent users. This tool is available through web portal and as a plugin to Vuze, a popular BitTorrent client. Finally, we present MYPROBE, the web portal that allows to query our database and to gather different pieces of information regarding BitTorrent content publishers. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------BitTorrent es la aplicación peer-to-peer para compartición de ficheros de mayor éxito y responsable de una fracción importante del tráfico de Internet. Trabajos previos han estudiado BitTorrent usando técnicas de simulación, modelos analíticos y medidas reales. Aunque las técnicas analíticas y de simulación son más sencillas de aplicar, típicamente presentan versiones simplificadas de los problemas analizados y en el caso concreto de BitTorrent pueden obviar aspectos o interacciones fundamentales que ocurren en los enjambres de BitTorrent. Por lo tanto, en esta tesis utilizaremos como pilar de nuestra investigación técnicas de medidas reales. En primer lugar presentaremos un resumen de las técnicas de medidas usadas hasta el momento en el ámbito de BitTorrent que suponen la base teórica para el diseño de nuestras propias herramientas de medida que nos permitirán analizar enjambres reales de BitTorrent. Usando los datos obtenidos con estas herramientas estudiaremos aspectos diferentes de BitTorrent con un enfoque especial de los aspectos socioeconómicos. En la primera parte de la tesis, realizaremos un estudio detallado de la topología de los enjambres reales de BitTorrent así como de detalles acerca de las interacciones entre peers. Nuestro análisis demuestra que la resistencia de la topología de los enjambres reales de BitTorrent es menor que la ofrecida por grafos aleatorios equivalentes. Además, los resultados revelan que las políticas de los Provedores de Internet junto con la incipiente utilización de clientes de BitTorrent modificados y otros efectos en la red (p.ej. congestión) hacen que los enjambres reales de BitTorrent presentan una composicin de localidad. Es decir, un nodo tiene un número de vecinos dentro de su mismo Proveedor de Internet mayor del que obtendría en una topología puramente aleatoria. Estos resultados son de interés para las empresas que utilizan BitTorrent en sus operaciones, así como para los Provedores de Internet responsables de transportar el tráfico de BitTorrent. En la segunda parte de la tesis, analizamos los aspectos de publicación de contenido en los mayores portales de BitTorrent. En concreto, los resultados presentados muestran que sólo un pequeño grupo de publicadores (alrededor de 100) es responsable de hacer disponible más de dos tercios del contenido publicado. Además estos publicadores se pueden dividir en dos grupos: (i) aquellos con incentivos económicos y (ii) publicadores de contenido falso. El primer grupo hace disponible contenido protegido por derechos de autor (que es típicamente muy popular) en los principales portales de BitTorrent con el objetivo de atraer a los consumidores de dicho contenido a sus propios sitios web y obtener un beneficio económico. La eliminación de este grupo puede tener un impacto importante en la popularidad de los principales portales de BitTorrent así como en el tráfico generado por BitTorrent en Internet. El segundo grupo es responsable de la publicación de contenidos falsos. La mayor parte de dichos contenidos están asociados a una actividad maliciosa (p.ej. la distribución de software malicioso) y por tanto suponen una seria amenaza para el ecosistema de BitTorrent, en particular, y para Internet en general. Para minimizar los efectos de la amenaza que presentan estos publicadores, en la última parte de la tesis presentaremos una nueva herramienta denominada TorrentGuard para la pronta detección de contenidos falsos. Esta herramienta puede accederse a través de un portal web y a través de un plugin del cliente de BitTorrent Vuze. Finalmente, presentamos MYPROBE, un portal web que permite consultar una base de datos con información actualizada sobre los publicadores de contenidos en BitTorrent

Advanced photonic and electronic systems WILGA 2016

Young Researchers Symposium WILGA on Photonics Applications and Web Engineering has been organized since 1998, two times a year. Subject area of the Wilga Symposium are advanced photonic and electronic systems in all aspects: theoretical, design and application, hardware and software, academic, scientific, research, development, commissioning and industrial, but also educational and development of research and technical staff. Each year, during the international Spring edition, the Wilga Symposium is attended by a few hundred young researchers, graduated M.Sc. students, Ph.D. students, young doctors, young research workers from the R&D institutions, universities, innovative firms, etc. Wilga, gathering through years the organization experience, has turned out to be a perfect relevant information exchange platform between young researchers from Poland with participation  of international guests, all active in the research areas of electron and photon technologies, electronics, photonics, telecommunications, automation, robotics and information technology, but also technical physics. The paper summarizes the achievements of the 38th Spring Edition of 2016 WILGA Symposium, organized in Wilga Village Resort owned by Warsaw University of technology

Hydrolink 2021/2. Artificial Intelligence

Topic: Artificial Intelligenc