RADAMS: Defending Against Proactive Attention Attacks

Linan Huang, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingChapter


Following the definition in Sect., in this chapter, we identify and formally define a new type of proactive attention attacks called Informational Denial-of-Service (IDoS) attacks that generate a large volume of feint attacks to overload human operators and hide real attacks among feints. We incorporate human factors (e.g., levels of expertise, stress, and efficiency) and empirical psychological results (e.g., the Yerkes–Dodson law and the sunk cost fallacy) to model the operators’ attention dynamics and their decision-making processes along with the real-time alert monitoring and inspection. To assist human operators in dismissing the feints and escalating the real attacks timely and accurately, we develop a Resilient and Adaptive Data-driven alert and Attention Management Strategy (RADAMS) that de-emphasizes alerts selectively based on the abstracted category labels of the alerts. RADAMS uses Reinforcement Learning (RL) to achieve a customized and transferable design for various human operators and evolving IDoS attacks. The integrated modeling and theoretical analysis lead to the Product Principle of Attention (PPoA), fundamental limits, and the tradeoff among crucial human and economic factors.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
Number of pages15
StatePublished - 2023

Publication series

NameSpringerBriefs in Computer Science
VolumePart F267
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776


  • Alert fatigue
  • Cognitive load
  • Feint attacks
  • Proactive attention vulnerability
  • Reinforcement learning
  • Risk analysis

ASJC Scopus subject areas

  • General Computer Science


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