Quantifying the Engagement Effectiveness of Cyber Cognitive Attacks
Authors: Bonnie Rushing, Shouhuai Xu
Published: 2025-10-17
arXiv: 2510.15805
Source: arXiv (cs.CY / cs.HC / cs.IT / cs.SI)
Abstract
As disinformation-driven cognitive attacks become increasingly sophisticated, the ability to quantify their impact is essential for advancing cybersecurity defense strategies. This paper presents a novel framework for measuring the engagement effectiveness of cognitive attacks by introducing a weighted interaction metric that accounts for both the type and volume of user engagement relative to the number of attacker-generated transmissions. Applying this model to real-world disinformation campaigns across social media platforms, we demonstrate how the metric captures not just reach but the behavioral depth of user engagement. Our findings provide new insights into the behavioral dynamics of cognitive warfare and offer actionable tools for researchers and practitioners seeking to assess and counter the spread of malicious influence online.
Why This Work Matters
A persistent gap in Cognitive Warfare analysis is the absence of standardized, empirically grounded metrics for measuring IO effectiveness. Without measurement, counter-IO interventions cannot be evaluated, IO campaigns cannot be comparatively assessed, and the relationship between attacker investment and defender harm remains opaque. This paper directly addresses that gap with a behavioral engagement metric that distinguishes between shallow reach (impressions) and deep behavioral impact (engagement type × volume weighted against attacker transmission count).
The authors — Bonnie Rushing and Shouhuai Xu — are the same team behind the VCA cognitive warfare framework (see Rushing, Hersch & Xu (2026)), giving this paper a coherent theoretical lineage: VCA provides the framework for understanding cognitive attack structure; this paper provides the measurement instrument for assessing attack effectiveness.
Core Concepts and Contributions
Weighted Interaction Metric (WIM): The central contribution. Rather than measuring IO effectiveness as raw reach (impressions, follower count), WIM weights different interaction types (shares, comments, likes, reposts) by their behavioral depth — interactions that require greater cognitive engagement from users (comments, shares) are weighted more heavily than passive engagement (likes). This is then normalized against the number of attacker-generated transmissions, yielding an efficiency ratio: how much behavioral response does each attacker action generate?
Behavioral depth distinction: The paper distinguishes between:
- Reach: How many unique users encountered the content
- Engagement breadth: Total engagement actions across the reached population
- Engagement depth: Weighted behavioral response per attacker transmission
This three-tier distinction is operationally significant: a campaign that achieves high reach but shallow engagement may have less cognitive impact than a lower-reach campaign with deep engagement among target audiences.
Real-world validation: Applied to documented disinformation campaigns across social media platforms, demonstrating that the WIM captures variance in campaign effectiveness that simple reach metrics miss — including campaigns with low reach but high behavioral activation of a specific target population.
Connections
- Cognitive Warfare — the threat environment being measured
- Disinformation Campaign — the specific campaign type analyzed
- Cognitive Warfare: Definition, Framework — Rushing, Hersch & Xu (2026) — conceptual framework this measurement paper complements
- Behavioral Outcomes of Human Cognitive Security — Allred et al. (2026) — behavioral modeling complement; where this paper measures attacker-side effectiveness, Allred et al. model defender-side behavioral outcomes
- Algorithmic Amplification — the platform mechanism that transforms attacker transmissions into cascading engagement