Detecting Spam in Email using Cyber Security

Abstract

Due to its cost-effectiveness and efficiency,
email is being used more often for everyday
commercial transactions and general
communication, which leaves it open to
assaults like spamming. Junk emails, often
known as spam emails, are unwanted
communications that are almost similar and
are sent at random to several recipients.
Bayesian Logistic Regression, Hidden Naïve
Bayes, Radial Basis Function (RBF) Network,
Voted Perceptron, Lazy Bayesian Rule, Logit
Boost, Rotation Forest, NNge, Logistic Model
Tree, REP Tree, Naïve Bayes, Multilayer
Perceptron, Random Tree, and J48 are
among the classification algorithms whose
performance is examined in this study. Using
the WEKA data mining tool, the algorithms'
performance was evaluated in terms of
Accuracy, Precision, Recall, F-Measure, Root
Mean Squared Error, Receiver Operator
Characteristics Area, and Root Relative
Squared Error. There was no use of feature
selection or performance-boosting techniques
to provide a fair assessment of the
classification algorithms' performance.
According to the research, there are several
classification algorithms that, when
thoroughly investigated using feature
selection techniques, can produce email
classification results that are more accurate.
The classifier with the greatest accuracy,
Rotation Forest, is determined to be 94.2%.
While no algorithm was able to sort spam
emails with 100% accuracy. Rotation Forest
came closest to producing the most accurate
result