AI & Cybersecurity

by Youakim Badr

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By leveraging research activities built around service computing and smart services for the Internet of Things (IoT), Dr. Badr’s current research strategy aims at developing devices and new chains of data analytical models for designing and deploying “Secure and Trustworthy AI Service Systems.” This research investigates a multidisciplinary approach to design and deploy smart and secure service systems enabled by AI and Blockchains. Research areas include:

 

Smart Services and Devices – The key challenge under this topic revolves around new analytic methods and techniques to embed intelligent capabilities in cloud-based services or devices services to ensure their cyber security and enable them with cognitive tasks to better interact and assist human. Projects:

• • General Purpose Conversional AI Expert
    • Team: Atharva Mungee (MS) and Dr. Robin Qiu
  • Reinforcement Learning-based Energy Consumption Controller
    • Team: Anchal Gupta (MS), Dr. Robin Qiu, Dr. Ashkan Negahban
  • Reinforcement Learning-based-Intrusion Detection at the Edge
    • Team: Wahid Khan Abzal (research assistant), Jesús Pacheco (University of Sonora, Mexico)

Blockchains and AI – The integration of Blockchains and AI is still a largely undiscovered area and their combination has the potential to build new services in ways never thought possible. Projects include:

• • Integration of AI and Blockchains 3.0
    • Team: Dr. Partha Mukherjee, Vineeth Suhas Challagali (research assistant)
  • Blockchain Data Analytics
    • Team: Dr. Partha Mukherjee, Akash Singh Baghel (research assistant)
  • Cryptocurrencies Exchange Rates Forecasting
    • Team: Dr. Partha Mukherjee, Gauravi Bhalchandra Patil (research assistant), Mokkapati, Yogitha Siva (research assistant)
  • “Trustworthy Blockchain-based Personal Lending”
    • Dissertation of Wisnu Uriawan (PhD candidate, INSA de Lyon, France), co-supervised with Dr. L. Brunie and Dr. O. Hassan (INSA-Lyon)

Trustworthiness and AI Systems – Recent advances in AI outperform many cognitive tasks and become omnipresent in decisions-making systems, self-driving cars, and critical systems. AI systems also presents risks and biases and we must carefully consider their safety, trustworthiness, robustness and dependability. This research focuses on AI risk management to identify vulnerability and assess and mitigate risks at design and deployment time. Projects:

• • Risk Management Framework of AI Systems
    • Team: Dr. Salim Hariri (The University of Arizona)
  • AutoBias Platform
    • Team: Rahul Sharma (research assistant) and Suraj Bondugula (research assistant)