E-mail inspection and mitigation systems are necessary in today's world due to frequent bombardment of adversarial attacks leverage phishing techniques. The process and accuracy in identifying a phishing attack present significant challenges due to data encryption hindering the ability to conduct signature matching, context analysis of a message, and synchronization of alerts in distributed detection systems. The author recognizes a grand challenge that the increase in the number of data analysis systems corresponds to an overall increase in the delivery time delay of an e-mail message. This work enhances PhishLimiter as a solution to combat phishing attacks using machine learning techniques to analyze 27 e-mail features and Software-Defined Networking (SDN) to optimize network transactions. PhishLimiter uses a two-lane inspection approach of Store-and-Forward (SF) and Forward-and-Inspect (FI) to distinguish whether traffic is held for analysis or immediately forwarded to the destination. The results of the work demonstrated PhishLimiter as a viable solution to combat Phishing attacks while minimizing delivery time of e-mail messages.
Library of Congress Subject Headings
Phishing--Prevention; Electronic mail systems--Security measures; Machine learning
Computing Security (MS)
Department, Program, or Center
Department of Computing Security (GCCIS)
Chin, Tommy, "A Reputation Score Driven E-Mail Mitigation System" (2020). Thesis. Rochester Institute of Technology. Accessed from
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