Skip to content
View hamedhpajouh's full-sized avatar
🎯
Focusing
🎯
Focusing

Highlights

  • Pro

Block or report hamedhpajouh

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
hamedhpajouh/README.md

πŸ‘‹ Hamed Haddadpajouh

Hello there! I'm Hamed, a fervent researcher and developer deeply interested in cybersecurity and privacy. Currently, I'm pouring my passion and expertise into LiboBerry, advancing cybersecurity and championing LLMs' privacy.

πŸš€ Current Endeavors

  • πŸ“ 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
  • πŸ”’ Privacy of LLMs: Delving deep into the nuances of LLM privacy, crafting solutions to shield user data, and upholding the sanctity of confidentiality.

πŸ“˜ 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

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

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

πŸ“Š Our Public Datasets for Machine Learning Tasks

  • IoT Malware Detection: A comprehensive dataset for opcode-based analysis of IoT malware. It includes various features for developing and testing malware detection algorithms. Access the Dataset
    πŸ“„ Citation: Haddadpajouh, H., et al. "A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting" 2018. [Paper Link]

🌟 Past Adventures

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

πŸ’Œ Connect with Me

Pinned Loading

  1. IoTMalware IoTMalware Public

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

    5 2

  2. OSXMalware OSXMalware Public

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

    3 2

  3. CyberScienceLab/Our-Datasets CyberScienceLab/Our-Datasets Public

    33 12

  4. CyberScienceLab/mkmv-iot CyberScienceLab/mkmv-iot Public

    Python 1