Reverse Engineering Socialbot Infiltration Strategies in Twitter

Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots $-$ automated accounts that mimic real users $-$ seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics and strategies (e.g., gender specified in the profile, how active they are, the method used to generate their tweets, and the group of users they interact with), and investigate the extent to which these bots are able to infiltrate the Twitter social network. Our results show that even socialbots employing simple automated mechanisms are able to successfully infiltrate the network. Additionally, using a $2^k$ factorial design, we quantify infiltration effectiveness of different bot strategies. Our analysis unveils findings that are key for the design of detection and counter measurements approaches

Demystifying NFT Promotion and Phishing Scams

The popularity and hype around purchasing digital assets such as art, video, and music in the form of Non-fungible tokens (NFTs) has rapidly made them a lucrative investment opportunity, with NFT-based sales surpassing $25B in 2021 alone. However, the volatility and scarcity of NFTs, combined with the general lack of familiarity with the technical aspects of this ecosystem, encourage the spread of several scams. The success of an NFT is majorly impacted by its online virality. There have been sparse reports about scammers emulating this virality by either promoting their fraudulent NFT projects on social media or imitating other popular NFT projects. This paper presents a longitudinal analysis of 439 unique Twitter accounts that consistently promote fraudulent NFT collections through giveaway competitions and 1,028 NFT phishing attacks. Our findings indicate that most accounts interacting with these promotions are bots, which can rapidly increase the popularity of the fraudulent NFT collections by inflating their likes, followers, and retweet counts. This leads to significant engagement from real users, who then proceed to invest in the scams. On the other hand, we identify two novel attack vectors which are utilized by NFT phishing scams to steal funds and digital assets from the victim's wallet. We also identify several gaps in the prevalent anti-phishing ecosystem by evaluating the performance of popular anti-phishing blocklists and security tools against NFT phishing attacks. We utilize our findings to develop a machine learning classifier that can automatically detect NFT phishing scams at scale

Oportunidades, riesgos y aplicaciones de la inteligencia de fuentes abiertas en la ciberseguridad y la ciberdefensa

The intelligence gathering has transformed significantly in the digital age. A qualitative leap within this domain is the sophistication of Open Source Intelligence (OSINT), a paradigm that exploits publicly available information for planned and strategic objectives. The main purpose of this PhD thesis is to motivate, justify and demonstrate OSINT as a reference paradigm that should complement the present and future of both civilian cybersecurity solutions and cyberdefence national and international strategies. The first objective concerns the critical examination and evaluation of the state of OSINT under the current digital revolution and the growth of Big Data and Artificial Intelligence (AI). The second objective is geared toward categorizing security and privacy risks associated with OSINT. The third objective focuses on leveraging the OSINT advantages in practical use cases by designing and implementing OSINT techniques to counter online threats, particularly those from social networks. The fourth objective embarks on exploring the Dark web through the lens of OSINT, identifying and evaluating existing techniques for discovering Tor onion addresses, those that enable the access to Dark sites hosted in the Tor network, which could facilitate the monitoring of underground sites. To achieve these objectives, we follow a methodology with clearly ordered steps. Firstly, a rigorous review of the existing literature addresses the first objective, focusing on the state of OSINT, its applications, and its challenges. This serves to identify existing research gaps and establish a solid foundation for an updated view of OSINT. Consequently, a critical part of the methodology involves assessing the potential security and privacy risks that could emerge from the misuse of OSINT by cybercriminals, including using AI to enhance cyberattacks, fulfilling the second objective. Thirdly, to provide practical evidence regarding the power of OSINT, we work in a Twitter use case in the context of the 2019 Spanish general election, designing and implementing OSINT methods to understand the behaviour and impact of automated accounts. Through AI and social media analysis, this process aims to detect social bots in the wild for further behaviour characterization and impact assessment, thus covering the third objective. The last effort is dedicated to the Dark web, reviewing different works in the literature related to the Tor network to identify and characterize the techniques for gathering onion addresses essential for accessing anonymous websites, completing the fourth objective. This comprehensive methodology led to the publication of five remarkable scientific papers in peer-reviewed journals, collectively forming the basis of this PhD thesis. As main conclusions, this PhD thesis underlines the immense potential of OSINT as a strategic tool for problem-solving across many sectors. In the age of Big Data and AI, OSINT aids in deriving insights from vast, complex information sources such as social networks, online documents, web pages and even the corners of the Deep and Dark web. The practical use cases developed in this PhD thesis prove that incorporating OSINT into cybersecurity and cyberdefence is increasingly valuable. Social Media Intelligence (SOCMINT) helps to characterize social bots in disinformation contexts, which, in conjunction with AI, returns sophisticated results, such as the sentiment of organic content generated in social media or the political alignment of automated accounts. On the other hand, the Dark Web Intelligence (DARKINT) enables gathering the links of anonymous Dark web sites. However, we also expose in this PhD thesis that the development of OSINT carries its share of risks. Open data can be exploited for social engineering, spear-phishing, profiling, deception, blackmail, spreading disinformation or launching personalized attacks. Hence, the adoption of legal and ethical practices is also important.La recolección de inteligencia ha sufrido una transformación significativa durante la era digital. En particular, podemos destacar el auge y sofisticicación de la Inteligencia de Fuentes Abiertas (OSINT, por sus siglas en inglés de Open Source Intelligence), paradigma que recolecta y analiza la información públicamente disponible para objetivos estratégicos y planificados. El cometido principal de esta tesis doctoral es motivar, justificar y demostrar que OSINT es un paradigma de referencia para complementar el presente y futuro de las soluciones de ciberseguridad civiles y las estrategias de ciberdefensa nacionales e internacionales. El primer objetivo es examinar y evaluar el estado de OSINT en el contexto actual de revolución digital y crecimiento del Big Data y la Inteligencia Artificial (IA). El segundo objetivo está orientado a categorizar los riesgos de seguridad y privacidad asociados con OSINT. El tercer objetivo se centra en aprovechar las ventajas de OSINT en casos de uso prácticos, diseñando e implementando técnicas de OSINT para contrarrestar amenazas online, particularmente aquellas provenientes de las redes sociales. El cuarto objetivo es explorar la Dark web, buscando identificar y evaluar técnicas existentes para descubrir las direcciones aleatorias de las páginas alojadas en la red Tor. Para alcanzar estos objetivos seguimos una metodología con pasos ordenados. Primero, para abordar el primer objetivo, realizamos una revisión rigurosa de la literatura existente, centrándonos en el estado de OSINT, sus aplicaciones y sus desafíos. A continuación, en relación con el segundo objetivo, evaluamos los posibles riesgos de seguridad y privacidad que podrían surgir del mal uso de OSINT por parte de ciberdelincuentes, incluido el uso de IA para mejorar los ciberataques. En tercer lugar, para proporcionar evidencia práctica sobre el poder de OSINT, trabajamos en un caso de uso de Twitter en el contexto de las elecciones generales españolas de 2019, diseñando e implementando métodos de OSINT para entender el comportamiento y el impacto de las cuentas automatizadas. A través de la IA y el análisis de redes sociales, buscamos detectar bots sociales en Twitter para una posterior caracterización del comportamiento y evaluación del impacto, cubriendo así el tercer objetivo. Luego, dedicamos otra parte de la tesis al cuarto objetivo relacionado con la Dark web, revisando diferentes trabajos en la literatura de la red Tor para identificar y caracterizar las técnicas para recopilar direcciones onion, esenciales para acceder a sitios web anónimos de la red Tor. Esta metodología llevó a la publicación de cinco destacados artículos científicos en revistas revisadas por pares, formando colectivamente la base de esta tesis doctoral. Como principales conclusiones, esta tesis doctoral subraya el inmenso potencial de OSINT como herramienta estratégica para resolver problemas en muchos sectores. En la era de Big Data e IA, OSINT extrae conocimiento a partir de grandes y complejas fuentes de información en abierto como redes sociales, documentos online, páginas web, e incluso en la Deep y Dark web. Por otro lado, los casos prácticos desarrollados evidencian que la incorporación de OSINT en ciberseguridad y ciberdefensa es cada vez más valiosa. La Inteligencia de Redes Sociales (SOCMINT, por sus siglas en inglés Social Media Intelligence) ayuda a caracterizar bots sociales en contextos de desinformación. Por su parte, la Inteligencia de la Web Oscura (DARKINT, por sus siglas en inglés Dark Web Intelligence) permite recopilar enlaces de sitios anónimos de la Dark web. Sin embargo, esta tesis expone como el desarrollo de OSINT lleva consigo una serie de riesgos. Los datos abiertos pueden ser explotados para ingeniería social, spear-phishing, perfilado, engaño, chantaje, difusión de desinformación o lanzamiento de ataques personalizados. Por lo tanto, la adopción de prácticas legales y éticas es también imprescindible