Quantum Security and Cyber Defense Advances

The field of cyber security is rapidly evolving to address the emerging threats of quantum computing and increasingly sophisticated malware. Researchers are exploring innovative approaches to secure cloud infrastructures, including hybrid cryptographic transition strategies and proactive risk mitigation. Meanwhile, advances in reinforcement learning and graph-based models are enabling more effective automated cyber defense systems. These systems can reason about network interactions and adapt to new environments, outperforming traditional models. Additionally, there is a growing focus on understanding and mitigating the risks of fault injection attacks and quantum malware, with researchers developing new taxonomies and ontologies to analyze and defend against these threats. Noteworthy papers in this area include:

  • Future-Proofing Cloud Security Against Quantum Attacks, which proposes a layered security framework for cloud infrastructures.
  • Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents, which introduces a novel approach to automated cyber defense using graph-based reinforcement learning.
  • SoK: A Systematic Review of Malware Ontologies and Taxonomies and Implications for the Quantum Era, which explores the fundamental nature and implications of quantum malware.

Sources

Future-Proofing Cloud Security Against Quantum Attacks: Risk, Transition, and Mitigation Strategies

Failure Modes and Effects Analysis: An Experience from the E-Bike Domain

Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents

SoK: A Beginner-Friendly Introduction to Fault Injection Attacks

SoK: A Systematic Review of Malware Ontologies and Taxonomies and Implications for the Quantum Era

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation

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