By Topic

Phishing detection using stochastic learning-based weak estimators

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Justin Zhan ; North Carolina A&T State University, USA ; Lijo Thomas

Phishing attack has been a serious concern to online banking and e-commerce Websites. This paper proposes a method to detect and filter phishing emails in dynamic environment by applying a family of weak estimators. Anomaly detection identifies observations that deviate from the normal behavior of a system and is achieved by identifying the phenomena that characterize the “normal” observation. The new observations are classified either a normal or abnormal based on the characteristics of data learnt. Most of the anomaly detection works with the assumption that the underlying distributions of observations are stationary, where this assumption is relevant to many applications. However some detection problem occurs within environments that are non-stationary. One good example to demonstrate the information is by identifying anomalous temperature pattern in meteorology that takes into account the seasonal changes of normal observations. It is necessary that anomalous observations are identified even with the changes or acquire the ability to adapt to the variations in non-stationary environments. Our experimental results show the feasibility and effectiveness of our approach.

Published in:

Computational Intelligence in Cyber Security (CICS), 2011 IEEE Symposium on

Date of Conference:

11-15 April 2011