OptiPhishDetect: Optimized Phishing Detection through Learning based GCN with Scoring Model
International journal of intelligent engineering and systems · 2023
Phishing detection is a critical component of cyber security, aiming to safeguard users from malicious attempts to deceive and exploit.In this research, proposed an innovative approach that combines an adaptive threshold optimization technique with a learning-based graph convolutional network (GCN) and a scoring model to enhance phishing detection accuracy.The learning-based GCN is designed to analyze the intricate relationships within a graph structure, capturing nuanced dependencies between various entities such as email senders, recipients, and URLs.Leveraging this graph analysis, a scoring model assigns likelihood scores to instances being potential phishing attempts.Adaptive threshold optimization is a technique commonly used in feature selection to determine which features dynamically.In the context of phishing website detection, adaptive threshold optimization is used to select and prioritize the most informative features extracted from the websites.As a result of GCN, the model is able to differentiate between legitimate websites and phishing sites both locally and globally.In an effort to improve online security and safeguard users from phishing attacks, the proposed model is providing 99.3% accuracy in detecting phishing attacks.This enables a flexible and fine-tuned decision-making process, optimizing the trade-off between false positives and false negatives.