A spam filter is a program that is used to detect unsolicited and unwanted email and prevent those messages from getting to a user’s inbox. E-mail spam, known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted e-mail messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. Spam is prevalent on the Internet because the transaction cost of electronic communications is radically less than any alternate form of communication. There are many spam filters using different approaches to identify the incoming message as spam, ranging from white list / black list, Bayesian analysis, keyword matching, mail header analysis, postage, legislation, and content scanning etc. Even though we are still flooded with spam emails everyday. This is not because the filters are not powerful enough, it is due to the swift adoption of new techniques by the spammers and the inflexibility of spam filters to adapt the changes. In our work, we employed supervised machine learning techniques to filter the email spam messages. Widely used supervised machine learning techniques namely C 4.5 Decision tree classifier, Multilayer Perceptron, Naïve Bayes Classifier are used for learning the features of spam emails and the model is built by training with known spam emails and legitimate emails. The results of the models are discussed.
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
1.2 PROBLEM STATEMENT
1.3 AIM AND OBJECTIVES OF THE STUDY
1.4 SCOPE OF THE STUDY
1.5 SIGNIFICANCE OF THE STUDY
1.6 MOTIVATION OF THE STUDY
1.7 BENEFIT OF THE STUDY
CHAPTER TWO
LITERATURE REVIEW
2.1 OVERVIEW OF SPAM FILTERING
2.2 TYPES OF SPAM FILTERS
2.3 REVIEW OF SPAM FILTERING METHODS
2.4 INBOUND AND OUTBOUND FILTERING
2.5 REVIEW OF RELATED STUDIES
2.6 SPAM FILTER ARCHITECTURE AND METHODS
CHAPTER THREE
METHODOLOGY
3.1 INTRODUCTION
3.2 SOURCES OF DATA
3.3 DATA COLLECTION PROCESS
3.4 TREE CLASSIFIER MODEL
3.5 FEATURE EXTRACTION
CHAPTER FOUR
4.1 EXPERIMENT AND RESULT
CHAPTER FIVE
5.1 CONCLUSION
5.2 RECOMMENDATION
REFERENCES
Spam Filtering Using Machine Learning Techniques Implemented With Design Tree Classifier. (n.d.). UniTopics. https://www.unitopics.com/project/material/spam-filtering-using-machine-learning-techniques-implemented-with-design-tree-classifier/
“Spam Filtering Using Machine Learning Techniques Implemented With Design Tree Classifier.” UniTopics, https://www.unitopics.com/project/material/spam-filtering-using-machine-learning-techniques-implemented-with-design-tree-classifier/. Accessed 22 November 2024.
“Spam Filtering Using Machine Learning Techniques Implemented With Design Tree Classifier.” UniTopics, Accessed November 22, 2024. https://www.unitopics.com/project/material/spam-filtering-using-machine-learning-techniques-implemented-with-design-tree-classifier/
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