Phishing Website Detection Using Machine Learning Techniques
Keywords:
Phishing website detection, Machine learning, Feature extraction, Online security, Anti-phishing techniquesAbstract
Phishing websites are a stern threat to online security, as they attempt to giveaway delicate data from unsuspecting workers. To fight this threat, scholars have developed various techniques. These algorithms can be skilled on large datasets of phishing and genuine websites to cram patterns and characteristics that distinguish between the two. These algorithms can then be used to recognize and tablet phishing websites before users can be victimized. On approach to involves feature removal, where various features of a website such as URL structure, domain age, and content are analyzed to identify phishing websites. Another approach involves to automatically cutting features and learn compound patterns in website data. Machine learning- based phishing website detection techniques have shown promising results, achieving high accuracy rates and outperforming traditional rule-based methods. With further research and development, these techniques have the possible to become an significant tool in the match beside online phishing attacks.
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Articles in this journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. This license permits others to copy, distribute, and adapt the work, provided it is for non-commercial purposes, and the original author and source are properly credited.
