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Our team has selected five prominent country cases that are frequently mentioned in contemporary literature as potential targets of hybrid threats. By utilising these cases, we aim to demonstrate and validate the explanatory and predictive potential of the Hybrid Threat Indicator (HTI).
Disclaimer
With the pilot project Heidelberg Hybrid Threat Indicator, we aim to strengthen and expand the understanding of hybrid threats.
Hybrid threats are rightly associated in public discourse with cyberattacks, damage to undersea cables, or disinformation campaigns. However, our research shows that hybrid threats are more than the sum of their individual parts. On a more abstract level, the complexity and deniability of hybrid threats create new spaces for action for actors who seek to harm democratic systems.
The results of our pilot project therefore go beyond merely listing individual hybrid events. Instead, we present a data-driven model that quantifies the dynamics behind these events and makes them available for multiple countries. One component of the indicator is the POLECAT dataset, which has been discontinued as of July 2024. Within the scope of the pilot, further updates are not possible.
Risk of a hybrid threat
The warning signal indicates unusual deviations in the complexity of a threat against a state. The complexity of a threat is determined by how atypical the combinations of instruments used by an actor are, relative to a reference value. The higher the value (i.e., the more atypical the combination of instruments are compared to a reference value), the greater the chance of a material hybrid threat.
Complexity of the hybrid behaviour
Hybrid actors use complexity to mask their intentions. This complexity arises from the multitude and diversity of the measured hybrid instruments. It is particularly evident when hybrid incidents simultaneously incorporate both friendly and hostile situational roles. The higher the value, the greater the complexity.
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Complexity follows a trend. The long-term increase from 2023 onwards is also due to the fact that the number of measured instruments during this period is lower, leading to greater uncertainty about the actual diversity.
Energy Dependency
Deniability
Ambiguity Mixture
Combinations of instruments can be assigned to typical interaction patterns. The ambiguity cocktail shows the ratio of these interaction patterns for an affected state in any given month. Unlike the measure of complexity, it does not take the sequence into account, but rather the absolute cocktail.
Interaction patterns?
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Calculating complexity relies upon a network of sequences. The network sequence shows which instruments were directed against a state and how different instruments were utilised in combination. You can use the slider to display the sequence network for a specific month. Different colours indicate how instruments may be associated with certain patterns of interaction. When instruments with different colours are closely linked, the complexity is higher.
You can find more information on the indicators here here →