Building ScamLens AI: My Exploration of Artificial Intelligence in Phishing and Social Engineering Detection

Zain Abuzaid — Sat, 08 Aug 2026 22:19:03 +0000

My name is Zain Nofan Abuzaid, زين نوفان ابوزيد. I am an Information Technology student and software developer from Jordan. My main interests are artificial intelligence, cybersecurity, social engineering, phishing detection, computer vision and software development.

I am interested in using technology to solve practical problems. I also try to connect my software projects with the topics I study and research.

One of the projects that reflects this interest is ScamLens AI.

About ScamLens AI

ScamLens AI is an AI assisted cybersecurity project that focuses on analyzing suspicious digital content and identifying possible signs of phishing and social engineering.

The idea came from my interest in understanding how artificial intelligence can help people recognize suspicious messages before they interact with them.

The project can be used as a concept for analyzing messages, emails, URLs and screenshots. The goal is not only to say whether something is safe or suspicious but also to explain the reasons behind the result.

For example, a suspicious message may contain an urgent request, ask for sensitive information or try to impersonate a trusted organization.

Instead of only giving the user a risk score, the system should explain what made the content suspicious.

Connection With My Research

ScamLens AI is connected to my research work on the role of artificial intelligence in detecting social engineering and phishing attacks.

My research focuses on how AI can help identify suspicious patterns in digital communication and how these systems can support users when they encounter potentially harmful content.

The research provides the background for understanding the problem while ScamLens AI is a practical way of exploring how some of these ideas could be implemented in software.

This is the main connection between my research and development work. I am interested in taking a research topic and turning it into something that can be tested and developed as a real software project.

Why Phishing Detection Is Difficult

Phishing is not always based on obvious technical indicators.

Many attacks try to manipulate the person receiving the message. They may create urgency, fear or trust and encourage the user to act without checking the information.

For example, a message may say:

Your account has been suspended. Verify your identity immediately using the link below.

The message is trying to make the user react quickly instead of thinking about whether the request is legitimate.

This is one of the reasons I am interested in the connection between cybersecurity and human behavior.

How ScamLens AI Could Analyze Suspicious Content

The concept can combine several types of information instead of relying on one indicator.

The first part is language analysis. The system can look for things such as urgent requests, threats, requests for passwords or financial information, account verification requests and impersonation.

The second part is URL analysis. When a link is available, characteristics such as the domain structure, unusual URLs, lookalike domains, suspicious subdomains and other indicators can be considered.

The third part is social engineering analysis. The system can look for common manipulation techniques such as authority, urgency, fear and financial incentives.

Using multiple indicators can provide a more useful assessment than relying on a single feature.

Why Explainability Matters

I believe that an AI security tool should explain its result.

A result such as:

Risk: 92%

does not tell the user why the message was considered suspicious.

A more useful result would explain that the message contains artificial urgency, requests sensitive information, appears to impersonate a trusted organization or contains suspicious URL characteristics.

This makes the system more useful as a decision support tool.

The purpose is not to replace human judgment but to give the user more information before making a decision.

My Other Technical Work

ScamLens AI is part of my wider work in Information Technology.

Another project I have worked on is VisionGuard Kids, which explores the use of Python, OpenCV and MediaPipe in an early vision screening application.

I have also worked on web and mobile development projects and explored game development through PIXEL ADV PLATFORMER.

These projects are different from each other but they are connected by the same interest in building software that has a practical purpose.

My Research Interests

My current interests include artificial intelligence, cybersecurity, phishing detection, social engineering, machine learning, computer vision, explainable AI, responsible AI and software development.

I am particularly interested in the area where artificial intelligence and cybersecurity meet.

I want to understand how AI can be used to analyze threats that target people and how the results can be presented in a way that users can actually understand.

Responsible AI

There are also limitations to using AI for cybersecurity.

A system can produce false positives or false negatives. It can also make mistakes when the available information is incomplete.

Privacy is another important issue because users may provide private messages, emails or screenshots for analysis.

For these reasons, ScamLens AI should be considered an AI assisted security concept rather than a system that can guarantee that every prediction is correct.

Any future implementation would need proper testing, suitable datasets and clear evaluation methods.

Future Research

I am interested in exploring several directions in the future.

These include machine learning based phishing classification, natural language processing, analysis of screenshots and text together, explainable AI for cybersecurity, human centered security interfaces and privacy preserving analysis.

I am also interested in evaluating these approaches using real world phishing datasets instead of relying only on theoretical examples.

Conclusion

My interest in artificial intelligence and cybersecurity comes from a simple question: how can intelligent software help people make safer decisions when they encounter suspicious digital content?

My research on artificial intelligence and phishing detection gives me a way to study this question from a research perspective.

ScamLens AI gives me a way to explore the same problem through software development.

This connection between research and practical development is the direction I want to continue working on.

About Me

Zain Nofan Abuzaid

زين نوفان ابوزيد

Information Technology student and software developer from Jordan.

Research interests: Artificial Intelligence, Cybersecurity, Social Engineering, Phishing Detection, Computer Vision, Explainable AI and Responsible AI.

ORCID: 0009-0005-7170-4667

GitHub: zainzaina

Related research: The Role of Artificial Intelligence in Detecting Social Engineering and Phishing Attacks

Project: ScamLens AI

DEV Community: Zain Abuzaid

Building ScamLens AI: My Exploration of Artificial Intelligence in Phishing and Social Engineering Detection

About ScamLens AI

Connection With My Research

Why Phishing Detection Is Difficult

How ScamLens AI Could Analyze Suspicious Content

Why Explainability Matters

My Other Technical Work

My Research Interests

Responsible AI

Future Research

Conclusion