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Ph.D. position in Computer Science & Information Systems are currently available with a full scholarship for students enrolled in a Joint Ph.D. program at the BITS Pilani, and RMIT University, Australia.

Application Deadline: April 17, 2024, for July 2024 intake

How to Apply: Send in your complete application using the following link:

Desirable qualifications: B. Tech/ M.Tech in Computer Science,

Candidates having eagerness to research cyber security, good English writing skills, knowledge of computer networks and distributed systems, ML would be preferred. 


For candidates enrolled in a Joint Ph.D. between RMIT University and BITS Pilani:

  • BITS Pilani Ph.D. fellowship:
    • INR 45,800/- per month for student with a higher degree of BITS Pilani or its equivalent
    • INR 42,800/- per month (during course work), INR 45,800/- per month (after coursework completion) for student with an integrated first degree of BITS Pilani or its equivalent
  • Receive a full RMIT tuition fee scholarship for the duration of your enrolment
  • Benefit from the world – class research facilities in India and Melbourne
  • Travel to Australia for up to one year of candidature and be supported by an Australian stipend for the duration of your time in Melbourne.
  • Candidates admitted to the program are jointly supervised by faculty from BITS and RMIT.


Project ID: BITSRMIT024B001258

Project title: Ransomware Attack Detection and Mitigation using

Project Team: Prof. Haribabu Kotakula, BITS Pilani | Prof. Iqbal Gondal, RMIT University, Australia

Description: Ransomware attacks have become increasingly sophisticated and prevalent, causing significant disruptions and financial losses to organisations worldwide. Sophisticated malware attacks take months to infiltrate the target networks. During this period, there is ample opportunity for the organisations to detect them at an early stage. Behaviour-based approaches monitor system calls, file changes, registry changes, or network activities over a period of time, extract features, and use ML-based methods to classify the behaviour as benign or malignant. Existing behaviour-based approaches are not scalable and are not able to detect attacks in real-time. The project proposes to implement behaviour-based detection approaches using SmartNICs. For more information write to: