Natural Language Processing Technique in Email Phishing

This report discusses the different types of phishing attacks and their characteristics. It also describes the Natural Language Processing Technique in Email Phishing and how it helps in detecting and preventing email phishing attacks. The report explains the NLP process and its techniques like Part of Speech Tagging and Word Stemming. It also provides examples of how NLP can detect phishing attacks in emails.

Running head: NATURAL LANGUAGE PROCESSING TECHNIQUE IN EMAIL PHISHING
Natural Language Processing Technique in Email Phishing
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1NATURAL LANGUAGE PROCESSING TECHNIQUE IN EMAIL PHISHING
Table of Contents
Introduction......................................................................................................................................2
Types of phishing attacks............................................................................................................2
Natural Language Processing Technique in Email Phishing.......................................................2
Conclusion.......................................................................................................................................4
Reference.........................................................................................................................................5

2NATURAL LANGUAGE PROCESSING TECHNIQUE IN EMAIL PHISHING
Introduction
Networking attacks are a major threat nowadays. It occurs since malicious hackers make
use of the network vulnerabilities to intrude in the networks by phishing emails (Reddy, Reddy
and Ebenezar 2016). The report would include the types of phishing attacks that affect
organizations and people and their characteristics. Further, it would describe the Natural
Language Technique in email phishing that provides help in detecting and preventing email
phishing.
Types of phishing attacks
The different types of phishing attacks by mails are, a) Deceptive Phishing, where the
system has masked messages that are received as fictitious account information send with a hope
that the user might click on the link to be redirected to a malicious site phishing their information
(Aggarwal, Kumar and Sudarsan 2014). b) Session Hijacking, where phishing attacks target a
user while they are about to enter their personal data and performs unauthorized actions right at
that time. c) Web Trojans, where phishing techniques collect user information to transfer to the
phisher. d) Data Theft, where data from a user’s computer can easily be stolen without the
knowledge of the user. e) Content injection phishing, where hackers may even replace the
content of the original website with a bogus replica in this type of phishing.
Natural Language Processing Technique in Email Phishing
Natural Language Processing is a phishing detection and prevention technique that works
on the language derivation technique spoken by humans. In this process, a computer is made to
function in the similar way as a human so that is can process a naturally spoken language by a

3NATURAL LANGUAGE PROCESSING TECHNIQUE IN EMAIL PHISHING
human (Aggarwal, Kumar and Sudarsan 2014). The NLP process includes the techniques like
Part of Speech Tagging or PoS and Word Stemming amongst few others.
As for instance, PoS Tagging process tags each word in a text with the proper PoS in
each circumstance. This depends on the parts of a speech. For example, the word plant can be
used as “I shall plant a tree” and as “This plant has ended vacancy troubles in this area”. The PoS
tagging depends on the circumstances the words are being used (Aggarwal, Kumar and Sudarsan
2014). Like in this case, it means a kind of work in the first sentence and a factory in the second.
However, PoS do not depend on just the English language but all other languages spoken by
humans all over the globe.
Detecting an email phishing attack by NLP method is done in many ways. These
processes are:
a) Detecting of the absence of names in the email is easily detectable when a
recipient’s name is not used in an email and deliberately or mistakenly that name
is nowhere to be found later in the entire email.
b) If an email mentions or demands money then a phishing attack can be easily
detectable since it would be followed by currency symbol or a name.
c) This kind of phishing technique is easily detectable by NLP method if it is found
that the email has a word or a phrase that would ask the user to reply to the mail.
d) Some words in an email can easily be detectable as phishing attacks with the help
of NLP technique. These words would invoke the sense of urgency to reply or
click on a provided. The words could be now, today, right now, straightaway,
shortly, quickly, immediately and others like this.

4NATURAL LANGUAGE PROCESSING TECHNIQUE IN EMAIL PHISHING
Therefore, with the help of NLP, the messages that malicious and suspicious email
contains can be detected and prevented easily before an unsuspecting user clicks on the
malevolent links that may redirect him or her to a phishing attack.
Conclusion
Therefore, it can be concluded from the above report that phishing attacks are malicious
and can harm a network user in many ways. These phishing attacks can be of various types.
However, amongst many techniques, with the help of Natural Language Processing or NLP
technique, email phishing attacks may be intervened according to the language it contains.

5NATURAL LANGUAGE PROCESSING TECHNIQUE IN EMAIL PHISHING
Reference
Aggarwal, S., Kumar, V. and Sudarsan, S.D., 2014, September. Identification and detection of
phishing emails using natural language processing techniques. In Proceedings of the 7th
International Conference on Security of Information and Networks (p. 217). ACM.
McGeehan, R., Popov, L.T., Palow, C.W., Read, R.J. and Keyani, P., Facebook Inc,
2017. Preventing phishing attacks based on reputation of user locations. U.S. Patent 9,576,119.
Reddy, V.R., Reddy, C.M. and Ebenezar, M., 2016. A Study on Anti-Phishing Techniques.
Suganya, V., 2016. A Review on Phishing Attacks and Various Anti Phishing
Techniques. International Journal of Computer Applications (0975–8887) Volume.