Deception in Network Defences using Unpredictability

Jassim Happa, Thomas Bashford-Rogers, Alastair Janse van Rensburg, Michael Goldsmith, Sadie Creese

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a novel method that aims to improve upon existing moving-target defences by making them unpredictably reactive using probabilistic decision-making. We postulate that unpredictability can improve network defences in two key capacities: (1), by re-configuring the network in direct response to detected threats, tailored to the current threat and a security posture, and (2): by deceiving adversaries using pseudo-random decision-making (from a set of acceptable set of responses), potentially leading to adversary delay and failure. Decisions are performed automatically, based on reported events (e.g. IDS alerts), security posture, mission processes, and states of assets. Using this codified form of situational awareness, our system can respond differently to threats each time attacker activity is observed, acting as a barrier to further attacker activities. We demonstrate feasibility with both anomaly- and misuse-based detection alerts, for a historical dataset (playback), and a real-time network simulation where asset-to-mission mappings are known. Our findings suggest that unpredictability yields promise as a new approach to deception in laboratory settings. Further research will be necessary to explore unpredictability in production environments.
Original languageEnglish
Article number29
Pages (from-to)1-26
Number of pages26
JournalDigital Threats: Research and Practice
Publication statusPublished - 15 Oct 2021


  • Network Defences
  • Decision Trees
  • Situational Awareness
  • Simulation

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