Deakin University
Browse

File(s) under permanent embargo

Hybrid feature selection for phishing email detection

Version 2 2024-06-03, 22:10
Version 1 2014-10-28, 09:35
chapter
posted on 2024-06-03, 22:10 authored by I Hamid, Jemal AbawajyJemal Abawajy
Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. Through an analysis of a number of phishing and ham email collected, this paper focused on fundamental attacker behavior which could be extracted from email header. It also put forward a hybrid feature selection approach based on combination of content-based and behavior-based. The approach could mine the attacker behavior based on email header. On a publicly available test corpus, our hybrid features selections are able to achieve 96% accuracy rate. In addition, we successfully tested the quality of our proposed behavior-based feature using the information gain.

History

Chapter number

26

Pagination

266-275

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783642246494

Language

eng

Publication classification

B1 Book chapter

Copyright notice

2011, Springer-Verlag Berlin

Extent

38

Editor/Contributor(s)

Xiang Y, Cuzzocrea A, Hobbs M, Zhou W

Publisher

Springer

Place of publication

Berlin, Germany

Title of book

Algorithms and architectures for parallel processing

Series

Lecture notes in computer science; v. 7017

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC