, Diksha Goel | Research Scientist

Diksha Goel

Diksha Goel

Research Scientist | CSIRO's Data61

About Me

Dr. Diksha Goel is a Research Scientist at CSIRO’s Data61 in Melbourne, working at the intersection of Cybersecurity, Artificial Intelligence, Game Theory, and Graph Data Mining. Her research focuses on developing scalable, AI-driven security solutions to defend against evolving cyber threats and protect critical infrastructure. She designs autonomous defense systems that integrate machine learning, game-theoretic modeling, and graph analytics to proactively detect vulnerabilities and respond to attacks. Her work bridges academic research with practical application and has influenced both policy and real-world cybersecurity practices.

She earned her Ph.D. in Computer Science from the University of Adelaide, where she developed advanced frameworks for cyber resilience, threat mitigation, and intelligent decision-making in security. Her research has been published in leading cybersecurity journals and conferences. Previously, as a Postdoctoral Fellow at CSIRO’s Data61, she contributed to Australia’s national cyber defense efforts through the Cyber Security Cooperative Research Centre (CSCRC), translating foundational research into solutions for government and industry.

Driven by a commitment to security innovation, Dr. Goel continues to push the boundaries of AI-powered defense, designing systems that are not only technically advanced but also resilient, responsible, and ready for real-world deployment.

Research

My research develops AI-driven, graph-based, and game-theoretic cybersecurity methods to protect critical infrastructures through autonomous defense, intelligent networking, and strategic threat modeling.

For a comprehensive list of my publications, please click here.

Autonomous Cyber Defense

  • Autonomous, context-aware security responses using LLMs.
  • RL and deep learning for real-time threat detection and mitigation.
  • Continual learning to evolve with emerging attack patterns.

Intelligent Network Security

  • Graph-based learning for real-time anomaly and intrusion detection.
  • Scalable, adaptive defenses for complex, distributed networks.
  • Resilient architectures for fault-tolerant and secure communication.

Game-Theoretic Cyber Defense

  • Stackelberg and evolutionary game models for adaptive threat response.
  • Adversarial reasoning under incomplete information and strategic deception.
  • Multi-agent defense optimization through iterative strategy refinement.

Education

Doctor of Philosophy

2019 - 2023

Master of Technology

2016 - 2018

Bachelor of Technology

2011 - 2015