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  2. Supervisor: Dr. Kabeer Ahmed Bhatti
Supervisor: Dr. Kabeer Ahmed Bhatti

 

Distributed AI for Enhanced Security in IoT Devices

 

Supervisor: Dr. Kabeer Ahmed Bhatti, Assistant Professor

Campus/School/Dept: BUIC

Supervisory Record:   

  • PhD Produced: Nil
  • PhD Enrolled:  Nil
  • MS/MPhil Produced: Nil
  • MS/MPhil Enrolled: Nil

 

Topic Brief Description:

The Internet of Things (IoT), which links billions of gadgets to the Internet, is quickly changing everyday lives and industries. However, there are serious security risks associated with the growing number of connected devices, such as denial-of-service attacks, illegal access, and data breaches. The size, complexity, and real-time nature of IoT devices make traditional centralized security measures inadequate. By empowering devices to jointly identify and eliminate threats in real-time, distributed artificial intelligence (AI) presents a viable strategy to improve the security of Internet of Things networks. By utilizing decentralized data processing and intelligent decision-making, this research seeks to investigate the application of distributed AI for improved security in Internet of Things devices.

 

Research Objectives/Deliverables:

  1. To investigate existing Distributed AI techniques (e.g., federated learning, edge computing, blockchain, and anomaly detection) and their potential applications for IoT security.
  2. To evaluate how distributed AI models can detect and mitigate cyber threats in IoT networks in a decentralized manner.

 

Research Questions: 

  1. How can Distributed AI be used to enhance the security of IoT devices by enabling collaborative threat detection, real-time, and decision-making?
  2. What are the best practices for implementing Distributed AI algorithms in resource-constrained IoT devices?
  3. How can Distributed AI ensure privacy while maintaining robust security in IoT systems?

 

Candidate’s Eligibility Profile:

  1. The applicant must have an MS/MPhil/Equivalent degree in electrical engineering with CGPA > 3.0. Besides, applicants must have a strong background in mathematics, optimization theory and related fields.
  2. Experience with programming languages such as C/C++, MATLAB, Network Simulator-III or Python is advantageous. Candidates should thrive in an international environment and have excellent communication skills to actively contribute to team research efforts.
  3. Proficiency in spoken and written English is essential. We value independence and responsibility while promoting teamwork and collaboration among colleagues.
  4. At least one journal/conference publication.