Phishing Webpage Detection Based on Multidimensional Features Driven by Deep Learning
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Date
2022
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Volume Title
Publisher
NHCE
Abstract
Phishing webpage detection is a crucial task in ensuring the security and privacy of online users. Phishing attacks involve fraudulent websites that mimic legitimate ones, with the goal of stealing sensitive information such as usernames, passwords, and credit card numbers. To combat this threat, researchers have developed various techniques for detecting and blocking phishing webpages. In this work, we provide an overview of the state-of-the-art in phishing webpage detection, including both traditional rule-based and machine learning-based approaches. We also discuss the challenges and future directions for improving the effectiveness and efficiency of phishing detection systems. Overall, our analysis highlights the importance of continuing research in this area to better protect online users from the ever-evolving threat of phishing attacks.
The detection of phishing webpages is a challenging task, as attackers continuously update their techniques to evade detection. Traditional rule-based approaches are limited in their ability to detect new and evolving attacks, and machine learning-based approaches require large amounts of labeled data to train the models accurately. Recent research has focused on developing more effective phishing detection systems that combine the strengths of both traditional rule-based and machine learning-based approaches. These systems use a variety of techniques, such as analyzing website content and structure, monitoring user behavior, and examining network traffic to detect phishing attacks.
Keywords: Phishing, website, labelled data, network traffic, machine learning.