8,588 research outputs found
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Recent studies in social media spam and automation provide anecdotal
argumentation of the rise of a new generation of spambots, so-called social
spambots. Here, for the first time, we extensively study this novel phenomenon
on Twitter and we provide quantitative evidence that a paradigm-shift exists in
spambot design. First, we measure current Twitter's capabilities of detecting
the new social spambots. Later, we assess the human performance in
discriminating between genuine accounts, social spambots, and traditional
spambots. Then, we benchmark several state-of-the-art techniques proposed by
the academic literature. Results show that neither Twitter, nor humans, nor
cutting-edge applications are currently capable of accurately detecting the new
social spambots. Our results call for new approaches capable of turning the
tide in the fight against this raising phenomenon. We conclude by reviewing the
latest literature on spambots detection and we highlight an emerging common
research trend based on the analysis of collective behaviors. Insights derived
from both our extensive experimental campaign and survey shed light on the most
promising directions of research and lay the foundations for the arms race
against the novel social spambots. Finally, to foster research on this novel
phenomenon, we make publicly available to the scientific community all the
datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science
Track, Perth, Australia, 3-7 April, 2017
DNA-inspired online behavioral modeling and its application to spambot detection
We propose a strikingly novel, simple, and effective approach to model online
user behavior: we extract and analyze digital DNA sequences from user online
actions and we use Twitter as a benchmark to test our proposal. We obtain an
incisive and compact DNA-inspired characterization of user actions. Then, we
apply standard DNA analysis techniques to discriminate between genuine and
spambot accounts on Twitter. An experimental campaign supports our proposal,
showing its effectiveness and viability. To the best of our knowledge, we are
the first ones to identify and adapt DNA-inspired techniques to online user
behavioral modeling. While Twitter spambot detection is a specific use case on
a specific social media, our proposed methodology is platform and technology
agnostic, hence paving the way for diverse behavioral characterization tasks
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