Minimizing Human Elements in Phishing Attacks by Integrating Administrative Controls to Phishing Detection Systems

Abstract

Phishing emails are a prevalent threat in the current cybersecurity landscape and industry- standard phishing detection system employed are considered insufficient in preventing phishing attacks as the detection system prioritizes on filtering and blocking phishing emails. This research aims to propose an enhanced phishing detection system addressing the limitation of the existing system deployed by integrating administrative countermeasures to reduce the number of successful phishing attacks. Regarding research methodology, this research will focus on gathering both quantitative and qualitative data by distributing surveys comprising of open-ended and close-ended questions and employing stratified sampling method to draw conclusions on the data collected. In conclusion, the proposed system highlights signs of phishing in an email which alleviates human error resulting in lower rate of successful phishing attacks. Future recommendation regarding the proposed system involves using an advanced Artificial Intelligence branch like Deep Learning and adding semantic analysis capability to the proposed system.