Phishing email detection technique by using hybrid features

Email provides convenience of communicating to such large number of people, especially for businessman. However, more attacks are launched to target electronic communication user in order to harvest credentials information from them for illegal purpose used. The most commonly phishing method is initialed by sending out email to user tends to make the user believe that they are communicating with trusted enttify, and deceive them into providing personal information. Recently, there are a lot of research have been done to overcome the phishing emails problem. This project aim to design a phishing email detection technique and focus on feature selection. The proposed method contains content-based feature. URL-based feature and behavior-based feature, which total nine feature sets. The proposed method has been evaluated on a set of 500 phishing emails and 500 legitimate emails. The proposed method obtain overall occuracy 97.25% with 1% false negative rate and 5% false positive rate. The proposed method able to classify more occurotely than the hybrid feature proposed by Hamid et al.. This evidence that two newly add on feature sets, hyperlink feature and return path feature are potential indicator. The quite promising result is motivated future work to mine the attacker behavior and explore more about behavior-based feature

Phishing Email Detection Technique by using Hybrid Features

Phishing emails is growing at an alarming rate in this few years. It has caused tremendous financial losses to internet users. Phishing techniques getting more advance everyday and this has created great challenge to the existing anti-phishing techniques. Hence, in this paper, we proposed to detect phishing emails through hybrids features. The hybrid features consist of content-based, URL-based, and behaviorbased features. Based on a set of 500 phishing emails and 500 legitimate emails, the proposed method achieved overall accuracy of 97.25% and error rate of 2.75%. This promising result verify the effectiveness of the proposed hybrid features in detecting phishing email

Intelligent Detection for Cyber Phishing Attacks using Fuzzy rule-Based Systems

Cyber phishing attacks are increasing rapidly, causing the world economy monetary losses. Although various phishing detections have been proposed to prevent phishing, there is still a lack of accuracy such as false positives and false negatives causing inadequacy in online transactions. This study constructs a fuzzy rule model utilizing combined features based on a fuzzy inference system to tackle the foreseen inaccuracy in online transactions. The importance of the intelligent detection of cyber phishing is to discriminate emerging phishing websites with a higher accuracy. The experimental results achieved an excellent accuracy compared to the reported results in the field, which demonstrates the effectiveness of the fuzzy rule model and the feature-set. The findings indicate that the new approach can be used to discriminate between phishing and legitimate websites. This paper contributes by constructing a fuzzy rule model using a combined effective feature-set that has shown an excellent performance. Phishing deceptions evolve rapidly and should therefore be updated regularly to keep ahead with the changes

Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward

FCSIT Research Bulletin 2016

The FCSIT Research Bulletin is an annual publication of the Faculty of Computer Science and Information Technology, UNIMAS. The purpose of FCSIT Research Bulletin is to disseminate information that represent the current state of the research activities, publications, research findings, training, conferences and seminar conducted by the academicians in the faculty