Doctoral research fellow  Hannah Engeler

While the cyber domain is known to serve as an ‘off-ramp’ to conventional conflicts, recent data show an increase in politically relevant cyber incidents from Russian state or state-sponsored actors against targets in the European Union. At the same time, the discourse between Russia and the EU has become increasingly hostile. This raises the question of whether a process of enmity is developing between Russia and the EU in cyberspace and, if so, why and how enmity remains, despite the fact that cyberspace is a domain in which enmity is difficult to sustain. I argue that the structural features of cybersecurity itself constitute a source of enmity that is inherently ambivalent. 

Those features are: 
(1) the difficulty of attributing and defending against cyber incidents, which requires detailed knowledge of the attacker profiles as well as tactics, techniques, and procedures (TTPs); 

(2) the attacker’s need to acquire detailed knowledge about the target’s vulnerability vectors; and

 (3) the difficulty to differentiate between offense and defense in cyberspace. Consequently, if enmity in cyberspace appears, it is deeply
entangling as it necessarily entails a (negative) fascination with and continuous learning about the other.
 

The thesis is grounded in a theoretical understanding of enmity as a socially co-constituted process – enemization – unfolding along a continuum. This theorization offers a conceptual lens to assess enmity in cyberspace considering its ambivalent and fluid characteristics. To answer the research question, this thesis will conduct three analyses.

First, I will conduct a Latent Semantic Scaling analysis to measure the discursive co-constitution of Russian-EU enemization in cyberspace. Second, I will conduct a qualitative process analysis to uncover technical and institutional learning processes. Third, I will apply a mixed-methods approach combining computational topic and qualitative frame analysis to examine learning and mimicry processes in attribution patterns and norm references. I will then compare and match the results of the enemization analysis with the learning and mimicry processes and data on political cyber incidents. The findings of this thesis will enable the theorization of enemization under the specific conditions in cyberspace and its empirical assessment for the Russia-EU dyad. In particular, the final comparison of ambivalence patterns with the enemization process and cyber incidents will clarify how ambivalence and enemization are related in cyberspace and whether, under certain conditions, more or less ambivalent enemization may occur.