Abstract: Phishing websites represent a significant threat to cyber security as they threaten the confidentiality, integrity and availability of both corporate and consumer data. These malicious sites often serve as an entry point for various cyber attacks. Despite extensive efforts by researchers over the years, effective detection of phishing sites remains a challenge. While some advanced solutions show promise, they often require extensive manual engineering of features and struggle to keep up with emerging phishing tactics.
Addressing this challenge requires strategies capable of automatically identifying phishing sites and quickly handling new, previously unseen attacks. One promising approach involves leveraging the wealth of data available on websites hosting these malicious activities. Machine learning is proving to be a powerful tool in this endeavor, offering a more automated and efficient approach compared to traditional methods.
In our research, we conducted a comprehensive literature review and proposed a new method for detecting phishing websites. This method involves extracting features from web pages and using machine learning algorithms for classification. Using a data set specifically designed for this purpose, we aim to develop a robust and adaptive system capable of accurately identifying phishing attempts, including zero-day attacks.
Through this work, we aim to improve cybersecurity measures by providing a reliable method for identifying phishing attempts, including new and previously unseen attacks. By leveraging the wealth of data available on phishing hosting websites, our approach aims to improve detection accuracy and reduce the risk of data breaches. Ultimately, our goal is to strengthen defenses against phishing attacks and protect sensitive information from unauthorized access.
Keywords: Phishing, Malicious, Cyber Security, Threat, Automation, Security
Cite:
Mr. V. Ravikanth, Madimi Deekshitha, Palla Gnaneswar, Mallepogu Hari, Anumala Dinesh, "PHISHING ALERT USING MACHINE LEARNING", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 4, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13420.
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DOI:
10.17148/IJARCCE.2024.13420
