Abstract:
The rise of advanced mobile technology has brought
about the widespread presence of mobile devices in our society.
These portable and versatile gadgets have become essential items
for individuals due to their convenience and capabilities. As
technology continues to play a pivotal role in modern life, an evergrowing
number of people rely on mobile devices for almost all
life activities including crucial financial activities and business
routines. However, the increasing popularity of mobile devices
has also exposed users to a heightened risk of falling victim to
fraudulent schemes. Perpetrators have been exploiting mobile
users by pretending to present authentic and legitimate requests
and opportunities, leading to the divulgence of personal and
sensitive information. These deceitful activities have seen a significant
increase, affecting individuals of various ages, educational
backgrounds, and levels of technological literacy. Additionally,
malicious actors employ advanced methods to conceal their
identities, making it challenging to prevent and counter these
attacks. Two prevalent yet under-addressed issues in this context
are vishing and smishing. This research study introduces a
system designed to detect vishing and smishing attempts more
accurately. The system analyzes the reputation of suspicious
artifacts in messages and call conversations using third party
threat intelligence services. Further, it employs natural language
processing and machine learning techniques to examine the
content of voice calls and SMS messages. It identifies suspicious
elements such as keywords and phrases commonly used in
phishing attacks, sensitive information as well as the context of
the content.
