2 research outputs found
Perceptual Hashing applied to Tor domains recognition
The Tor darknet hosts different types of illegal content, which are monitored
by cybersecurity agencies. However, manually classifying Tor content can be
slow and error-prone. To support this task, we introduce Frequency-Dominant
Neighborhood Structure (F-DNS), a new perceptual hashing method for
automatically classifying domains by their screenshots. First, we evaluated
F-DNS using images subject to various content preserving operations. We
compared them with their original images, achieving better correlation
coefficients than other state-of-the-art methods, especially in the case of
rotation. Then, we applied F-DNS to categorize Tor domains using the Darknet
Usage Service Images-2K (DUSI-2K), a dataset with screenshots of active Tor
service domains. Finally, we measured the performance of F-DNS against an image
classification approach and a state-of-the-art hashing method. Our proposal
obtained 98.75% accuracy in Tor images, surpassing all other methods compared.Comment: To be published on the JNIC 2020 Conference. Already published
research summar
Classification of Spam Emails through Hierarchical Clustering and Supervised Learning
Spammers take advantage of email popularity to send indiscriminately
unsolicited emails. Although researchers and organizations continuously develop
anti-spam filters based on binary classification, spammers bypass them through
new strategies, like word obfuscation or image-based spam. For the first time
in literature, we propose to classify spam email in categories to improve the
handle of already detected spam emails, instead of just using a binary model.
First, we applied a hierarchical clustering algorithm to create SPEMC-K
(SPam EMail Classification), the first multi-class dataset, which contains
three types of spam emails: Health and Technology, Personal Scams, and Sexual
Content. Then, we used SPEMC-K to evaluate the combination of TF-IDF and
BOW encodings with Na\"ive Bayes, Decision Trees and SVM classifiers. Finally,
we recommend for the task of multi-class spam classification the use of (i)
TF-IDF combined with SVM for the best micro F1 score performance, ,
and (ii) TD-IDF along with NB for the fastest spam classification, analyzing an
email in ms.Comment: 4 pages, 2 figures, to be published in conference JNIC 202