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👋 Hamed Haddadpajouh

Hello there! I'm Hamed, a fervent researcher and developer with a profound interest in cybersecurity and privacy. Currently, I'm pouring my passion and expertise into LiboBerry, advancing the cybersecurity realm and championing the privacy of LLMs.

🚀 Current Endeavors

  • 🍓 LiboBerry: As a co-founder, I'm at the forefront, driving cutting-edge research and development projects to bolster cybersecurity tools and methodologies. Visit LiboBerry
  • 🔒 Privacy of LLMs: Delving deep into the nuances of LLM privacy, crafting solutions to shield user data, and upholding the sanctity of confidentiality.

📘 Noteworthy Research & Contributions

  • A Method and System for Adversarial Malware Threat Prevention and Adversarial Sample Generation: This invention offers a firewall to protect AI-based malware detection systems against adversarial attacks. It's a significant stride in the realm of cybersecurity, ensuring robust protection against evolving threats. US Patented

📌 Make your IoT environments robust against adversarial machine learning malware threats: a code-cave approach [NDSS2024]

📌 A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks
📌 A deep recurrent neural network-based approach for Internet of Things malware threat hunting
📌 A survey on IoT security: Requirements, challenges, and solutions
📌 Two-tier network anomaly detection model: a machine learning approach
📌 Cryptocurrency malware hunting: A deep recurrent neural network approach

🌟 Past Adventures

  • 🛡 Griffinix: Spearheaded a turnkey Artificial Intelligence startup to fortify critical infrastructure. A triumphant exit!
  • 📱 Appsaz: Donned the hat of a Product Manager/Owner, steering Appsaz - a versatile online mobile application generator system.

💌 Connect with Me

Hamed's Projects

iotmalware icon iotmalware

This project was conducted to create a very first malware dataset for IoT application

osxmalware icon osxmalware

This project belongs to our research on OS X malware detection based on machine learning techniques.

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