ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login

PHISHING SPAM EMAIL ONTOLOGICAL DETECTION

Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.9, No. 4)

Publication Date:

Authors : ; ;

Page : 51-54

Keywords : TME specific feature extraction; NTME congestive packet losses; Random forest classifier.;

Source : Downloadexternal Find it from : Google Scholarexternal

Abstract

Targeted Malicious Email (TME) has become more dangerous because it gathers user sensitive information. Beyond spam and phishing designed to trick users into revealing information, TME exploits computer networks and gathers sensitive information. It targets on single users and is designed to appear legitimate and trustworthy. Persistent threat features such as threat actor locale and weaponization tools along with recipient-oriented features such as reputation and role are leveraged with supervised data classification algorithms to demonstrate new techniques for detection of targeted malicious email. We propose a new email filtering technique using random forest classifier and Naïve Bayesian classification. A compromised router detection protocol is developed to identify congestive packet losses. We also develop feature extraction procedure to identify TME specific features. Naïve Bayesian classification is used to classify mails as either TME or trusted mail for user security avoiding frauds. A Naïve Bayesian classifier is a simple probabilistic classifier based on applying Bayesian theorem with strong (Naive) independence assumptions.

Last modified: 2020-05-05 09:14:04