Efficient Harmful Email Identification Using Neural Network

Abstract

Phishing is a form of online fraud that aims to steal a user's sensitive information such as online banking passwords or credit card numbers. In this paper, we present a technique to quickly detect suspected email using Neural Network Pruning approach. The goal is to determine whether the email is suspected or legitimate. A Multilayer feedforward neural network with Pruning Strategy is used for Feature Extraction and extracted features are used for identifying email as phishing email. Pruning Strategy extracts important features which are playing a key role in identifying phishing mail which looks similar to a legitimate one. To verify the feasibility of the proposed approach experimental evaluation has been performed using a dataset composed of phishing emails along with legitimate emails. The experimental results are satisfactory in terms of false positives and false negatives. The results of conducted test indicated good identification rate with very short processing time.

Evaluating the priority of email using machine learning

“…In [10], Nizamani apply some classification algorithms such as SVM, Naïve Bayes, J48, and CCM using different sets of features to detect email phishing. Kathsirvalavakumar et al [11] propose a multilayer neural network structure for email phishing detection. A data preprocessing stage is added to this neural network to reduce the number of input features, and hence reduce the computational cost of the system.…”

A Framework for Vietnamese Email Phishing Detection

“…In [10], Nizamani apply some classification algorithms such as SVM, Naïve Bayes, J48, and CCM using different sets of features to detect email phishing. Kathsirvalavakumar et al [11] propose a multilayer neural network structure for email phishing detection. A data preprocessing stage is added are very limited in terms of number of publications as well as the efficiency.…”