Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Support vector machines: relevance feedback and information retrieval
Information Processing and Management: an International Journal
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Detection of phishing webpages based on visual similarity
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Protecting Users against Phishing Attacks
The Computer Journal
Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)
IEEE Transactions on Dependable and Secure Computing
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
Examining the impact of website take-down on phishing
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
A comparison of machine learning techniques for phishing detection
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
Email Categorization Using (2+1)-Tier Classification Algorithms
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
On the Effectiveness of Techniques to Detect Phishing Sites
DIMVA '07 Proceedings of the 4th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Online phishing classification using adversarial data mining and signaling games
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
PhishCatch - A Phishing Detection Tool
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 02
Countermeasure Techniques for Deceptive Phishing Attack
NISS '09 Proceedings of the 2009 International Conference on New Trends in Information and Service Science
Detecting Phishing Emails Using Hybrid Features
UIC-ATC '09 Proceedings of the 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing
A multi-model approach to the detection of web-based attacks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web security
New filtering approaches for phishing email
Journal of Computer Security - EU-Funded ICT Research on Trust and Security
An evaluation of machine learning-based methods for detection of phishing sites
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Phishing Using a Modified Bayesian Technique
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Information Source-Based Classification of Automatic Phishing Website Detectors
SAINT '11 Proceedings of the 2011 IEEE/IPSJ International Symposium on Applications and the Internet
Phishing Email Feature Selection Approach
TRUSTCOM '11 Proceedings of the 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications
Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach
IEEE Transactions on Neural Networks
A multi-tier ensemble construction of classifiers for phishing email detection and filtering
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Phishing attacks continue to pose serious risks for consumers and businesses as well as threatening global security and the economy. Therefore, developing countermeasures against such attacks is an important step towards defending critical infrastructures such as banking. Although different types of classification algorithms for filtering phishing have been proposed in the literature, the scale and sophistication of phishing attacks have continued to increase steadily. In this paper, we propose a new approach called multi-tier classification model for phishing email filtering. We also propose an innovative method for extracting the features of phishing email based on weighting of message content and message header and select the features according to priority ranking. We will also examine the impact of rescheduling the classifier algorithms in a multi-tier classification process to find out the optimum scheduling. A detailed empirical performance and analysis of the proposed algorithm is present. The results of the experiments show that the proposed algorithm reduces the false positive problems substantially with lower complexity.