Mohammed Abdulrahman Alalyani

cybersecurity professional dedicated to protecting digital assets and strengthening modern security systems. My goal is to expand my expertise through real-world projects, continuous learning, and contributing to secure digital environments.

I have hands-on experience in building and developing security-focused projects, including Face-Crypt-Cloud, a biometric authentication system that leverages artificial intelligence and cloud technologies to enhance user identity verificationو my core areas of interest span several domains in cybersecurity, including:

• Digital Forensics
• Penetration Testing
• Network Security
• Cyber Threat Analysis
• Development of Secure and Scalable Security Solutions

• Python
• Bash
• PHP

• Wireshark
• Burp Suite
• Metasploit
• Nmap

• Docker
• Git
• Linux

• OpenCV
• Flask

Face-Crypt-Cloud is an advanced biometric authentication system designed to replace traditional password-based security with AI-driven facial recognition. Built on a secure, cloud-native architecture, it ensures high availability, data integrity, and seamless user verification.

The system processes facial data through an AI engine, extracts encrypted embeddings, and stores them securely in a cloud database. The authentication flow is designed to be stateless and highly scalable, allowing for concurrent verification requests without compromising performance or security.

graph LR
    User[User Interface] -->|Facial Data| AI[AI/ML Engine]
    AI -->|Extract Embeddings| Encrypt[Encryption Module]
    Encrypt -->|Secure Storage| DB[(Cloud Database)]
    AI -->|Verification| Auth[Authentication Service]
    Auth -->|Access Token| User

URLWraith is a specialized cybersecurity tool designed for deep URL analysis and proactive threat detection. It acts as a defensive layer against phishing, malware distribution, and malicious redirects by employing a multi-layered analysis engine. The architecture is built to process high volumes of URLs in real-time, providing actionable threat intelligence without introducing latency into the user's workflow.

The system ingests a target URL, runs it through a combination of heuristic analysis, pattern matching, and AI-based classification, and cross-references the results against known threat intelligence databases to deliver a definitive verdict.

graph LR
    Input[URL Input] --> Scanner[Analysis Engine]
    Scanner --> Heuristics[Heuristic Analysis]
    Scanner --> Pattern[Pattern Matching]
    Scanner --> AI[AI/ML Classifier]
    Heuristics --> Aggregator[Threat Aggregator]
    Pattern --> Aggregator
    AI --> Aggregator
    Aggregator --> DB[(Threat Intelligence DB)]
    Aggregator --> Output[Verdict: Safe / Suspicious / Malicious]

For questions, feedback, or support:

• Email: malredfan@gmail.com
• Instagram: @malredfan
• X: @malredfan
• LinkedIn: malredfan