M. Zago, P. Nespoli, D. Papamartzivanos, M. Gil Pérez, F. Gómez Mármol, G. Kambourakis, and G. Martínez Pérez, "Screening Out Social Bots Interference: Are There Any Silver Bullets?,"
IEEE Commununications Magagazine, vol. 19, no. August, pp. 2–8, 2019.
Social networks are a primary source of news and information that can be steered, dis- torted, and influenced. Recent scandals such as the one involving Cambridge Analytics proved that social media users are prone to such direct manipulation. Among the weapons available to perform these anti-democracy attacks, Social Bots are beyond question the most powerful one. These autonomous entities constitute coordinat- ed armies that sneakily manipulate and deceive real users. Thus, our research identifies five major challenges that the research community needs to face toward tackling Social Bots activities in four individual but comparable scenarios. To address these key challenges, we propose, elaborate, and evaluate a mix of remedies in the form of a proof-of-concept platform combining the agility of artificial intelligence with the expertise of human analysts to detect and shield against Social Bots interference.
M. Zago, M. Gil Pérez, and G. Martínez Pèrez,
"Scalable detection of botnets based on DGA: Efficient Feature Discovery Process in Machine Learning Techniques,"
Soft Computing, 2019, pp. 1-21.
Botnets are evolving, and their covert modus operandi, based on cloud technologies such as the virtualisation and the dynamic fast-flux addressing, has been proved challenging for classic intrusion detection systems and even the so-called next-generation firewalls. Moreover, dynamic addressing has been spotted in the wild in combination with pseudo-random domain names generation algorithm (DGA), ultimately leading to an extremely accurate and effective disguise technique. Although these concealing methods have been exposed and analysed to great extent in the past decade, the literature lacks some important conclusions and common-ground knowledge, especially when it comes to Machine Learning (ML) solutions. This research horizontally navigates the state of the art aiming to polish the feature discovery process, which is the single most time-consuming part of any ML approach. Results show that only a minor fraction of the defined features are indeed practical and informative, especially when considering 0-day botnet identification. The contributions described in this article will ease the detection process, ultimately enabling improved and more scalable solutions for DGA-based botnets detection.
P. Nespoli, M. Zago, A. Huertas Celdrán, M. Gil Pérez, F. Gómez Mármol, and F. J. García Clemente,
"PALOT: Profiling and Authenticating Users Leveraging Internet of Things,"
Sensors, , vol. 19, no. 12, p. 2832, 2019
Continuous authentication was introduced to propose novel mechanisms to validate users’ identity and address the problems and limitations exposed by traditional techniques. However, this methodology poses several challenges that remain unsolved. In this paper, we present a novel framework, PALOT, that leverages IoT to provide context-aware, continuous and non-intrusive authentication and authorization services. To this end, we propose a formal information system model based on ontologies, representing the main source of knowledge of our framework. Furthermore, to recognize users’ behavioral patterns within the IoT ecosystem, we introduced a new module called “confidence manager”. The module is then integrated into an extended version of our early framework architecture, IoTCAF, which is consequently adapted to include the above-mentioned component. Exhaustive experiments demonstrated the efficacy, feasibility and scalability of the proposed solution.
