6,531 research outputs found
Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic
This work proposes and analyzes the use of keystroke biometrics for content
de-anonymization. Fake news have become a powerful tool to manipulate public
opinion, especially during major events. In particular, the massive spread of
fake news during the COVID-19 pandemic has forced governments and companies to
fight against missinformation. In this context, the ability to link multiple
accounts or profiles that spread such malicious content on the Internet while
hiding in anonymity would enable proactive identification and blacklisting.
Behavioral biometrics can be powerful tools in this fight. In this work, we
have analyzed how the latest advances in keystroke biometric recognition can
help to link behavioral typing patterns in experiments involving 100,000 users
and more than 1 million typed sequences. Our proposed system is based on
Recurrent Neural Networks adapted to the context of content de-anonymization.
Assuming the challenge to link the typed content of a target user in a pool of
candidate profiles, our results show that keystroke recognition can be used to
reduce the list of candidate profiles by more than 90%. In addition, when
keystroke is combined with auxiliary data (such as location), our system
achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a
background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362
Dark Web Activity Classification Using Deep Learning
In contemporary times, people rely heavily on the internet and search engines
to obtain information, either directly or indirectly. However, the information
accessible to users constitutes merely 4% of the overall information present on
the internet, which is commonly known as the surface web. The remaining
information that eludes search engines is called the deep web. The deep web
encompasses deliberately hidden information, such as personal email accounts,
social media accounts, online banking accounts, and other confidential data.
The deep web contains several critical applications, including databases of
universities, banks, and civil records, which are off-limits and illegal to
access. The dark web is a subset of the deep web that provides an ideal
platform for criminals and smugglers to engage in illicit activities, such as
drug trafficking, weapon smuggling, selling stolen bank cards, and money
laundering. In this article, we propose a search engine that employs deep
learning to detect the titles of activities on the dark web. We focus on five
categories of activities, including drug trading, weapon trading, selling
stolen bank cards, selling fake IDs, and selling illegal currencies. Our aim is
to extract relevant images from websites with a ".onion" extension and identify
the titles of websites without images by extracting keywords from the text of
the pages. Furthermore, we introduce a dataset of images called Darkoob, which
we have gathered and used to evaluate our proposed method. Our experimental
results demonstrate that the proposed method achieves an accuracy rate of 94%
on the test dataset.Comment: 11 pages , 16 figures , 2 tables , New Dataset For DarkWeb Activity
Classificatio
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