How the Power of Machine -- Machine Learning, Data Science and NLP Can
Be Used to Prevent Spoofing and Reduce Financial Risks
Abstract
This paper discusses the potential of machine learning, data science,
and natural language processing (NLP) in mitigating the incidence of
spoofing and financial risks hinged on cyber threats. Another one is
spoofing; it is the act of impersonating legitimate entities to gain
unauthorized information and it is indeed a threat to the public and
companies to some extent. The research introduces two primary
methodologies to combat spoofing: an email filtering system using a
machine learning algorithm and an encryption and decryption system using
a Caesar Cipher and Python programming language. It distinguishes
between approved domains and unapproved domains by using machine
learning and successfully filters out phishing emails from reaching the
intended clients. This study also illustrates how to conduct email
domain verification using MongoDB Atlas, which a database is containing
approved vendors’ domains, to reduce spoofing. Specifically,
incorporating NLP helps the system analyze raw data to categorize it and
identify patterns potentially leading to a spoofing attempt, enhancing
the spoofing detection and prevention of the system. The paper also
presents arguments that require awareness and integration of new
technologies in the security frameworks. Hence, incorporating machine
learning, data science, and NLP presents robust, versatile, and
cost-effective solutions to enhance cybersecurity and ultimately protect
vital information and organizations’ monetary loss due to cybercrimes.