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RSET students developed Deep Learning solution to detect phishing

RSET computer science students have developed a Deep Learning-based solution to identify phishing attacks.

To counter rising cases of social engineering attacks, a four-member team of computer science students from Rajagiri School of Engineering and Technology (RSET) have developed a deep learning-based solution. The primary intent is to detect websites with phishing motives that are spread widely across various social platforms. This solution is taken into consideration by Kerala Police Cyberdome and will be released (in the process) as an additional feature in the BSafe application.

Ever since the pandemic last year, the probability of companies and individuals being hit by phishing attacks has increased manifold. Since then, many innocent WhatsApp users who unknowingly have clicked on innocuous-looking links have become prey to phishing attacks. Phishing attacks are intended to steal information like login credentials or credit card numbers by an act of spoofing humans by whaling, Email phishing, or vishing.

Realizing the importance of this problem, RSET students developed a deep learning-based solution as a part of their B.Tech final year project. As this solution was the need of the hour, it instantly caught the attention of Kerala Police Cyberdome, which is currently in the process of adding this project as a module to their existing application — BSafe — alerting fraud and spam calls.

Read More: Attackers Use Artificial Intelligence Generated Deepfake For Phishing Campaigns

Sangeetha Jamal, Assistant Professor, RSET, guided four-member teams: Nithin Valiyaveedu, Roshan Reju, Nithin K.M, and Vysakh Murali. “Unlike the existing models that detect phishing attacks only based on website URL, their solution also collects HTML and includes a script to classify a malicious website. They have also developed a browser extension to quickly run phishing checks on a given URL,” said Nithin Vailyaveedu, team member.

This team further reached Cyberdome with two presentations that impressed Cyberdome representatives about the solution to counter phishing. The team has shared their API (application programming interface) code of solution with Cyberdome for adding it as a module in the BSafe app.

Cyberdome sources said, “BSafe — a mobile and web-based application, is capturing a number of scams in a database, helping users of the app to alert/block such numbers automatically. We plan to incorporate the phishing attack detection as an additional feature in the same app (under process).”

Unlike existing machine learning-based models for phishing attacks, Mr. Roshan said their innovation is more advanced, accurate, and has greater computation power. Although all youngsters have got a job, they have not marketed their solution as a separate product and chose to associate with Cyberdome for the greater benefit of the public.

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Amit Kulkarni
Amit Kulkarni
Engineer | Academician | Data Science | Technical Writer Interested in ML Algorithms, Artificial Intelligence, and the implementation of new technology.


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