Abstract:
Abstract: Phishing is performed by trying to trick the victim
into accessing any computing information that looks original and
then instructing them to send important data to
unrestricted/unwanted private resources. For prevention, it is
essential to develop a phishing detection system. Recent phishing
detection systems are based on data mining and machine
learning techniques. Most of the related work literature requires
the collection of previous phishing attack logs, analyzing them
creating a list of such activities, and blocking traffic from such
sources. However, this is a cumbersome task because the data
size is very large, continues changing, and is dynamic in nature.
[1]. Instead of using a single algorithm approach, it would be
better to use a hybrid approach. A hybrid approach would be
better at mitigating phishing attacks because the classification of
different formats of data is handled; whether the intruder wants
to use images or textural input to gain into another user system
for phishing. Hybrid recommendation decision trees enhance any
of the machine learning and deep learning algorithms'
performance. The decision path of the model followed a series of
if/else/then statements that connect the predicted class from the
root of the tree through the branches of the tree to detect true
positives and false negatives of phishing attempts. 10 decision
trees were considered and used the features to train the
recommendation decision regression model. The developed
hybrid recommendation decision tree approach provided an
overall true positive rate of the model of 92.28 % and a false
negative rate is 7.4%.
