  Attacks exploiting human attentional vulnerability have posed severe threats
to cybersecurity. In this work, we identify and formally define a new type of
proactive attentional 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 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. Experimental results corroborate that
the proposed strategy outperforms the default strategy and can reduce the IDoS
risk by as much as 20%. Besides, the strategy is resilient to large variations
of costs, attack frequencies, and human attention capacities. We have
recognized interesting phenomena such as attentional risk equivalency,
attacker's dilemma, and the half-truth optimal attack strategy.
