## 3 Methodology
### Implementation
Semantically describe the threat model (e.g., black box vs. white box access to model). 4. Semantically describe each scientific publication on adversarial attacks and defenses using the SPAR ontologies, as illustrated earlier in Listings A6, 3, and 4. 5. Semantically link the publication with the ML pipeline, the threat model, as well as the corresponding techniques or mitigations of MITRE ATLAS. 6. Semantically link how defenses have been broken with new attacks, or vice versa, with our proposed MITRE ATLAS Semantic Extension Ontology, as depicted in Figure 1.

To evaluate, a variety of queries can be directed to the description logic reasoner to solicit information. Typically, these description logic queries are expressed in the Manchester OWL (Web Ontology Language) syntax ([PERSON] and others, 2019), as illustrated in Figure 2. The queries may pertain to specific defenses that have been compromised, reported attacks for particular input data modalities (e.g., malware), operations of attacks within a black-box threat model context, identification of authors employing specific datasets, and similar topics of interest. In addition to analyzing the knowledge base with the Protege tool ([PERSON] and others, 2019), we have developed--as part of the Horizon Europe KINAITICS project (see [[https://kinaitics.eu](https://kinaitics.eu)]([https://kinaitics.eu](https://kinaitics.eu)) (accessed on 23 January 2024))--a proof-of-concept software framework to raise the level of abstraction for end-users and facilitate the collaboration with other security tools by offering

Figure 2: DL query examples in Manchester OWL syntax. The outputs are individuals that can be subsequently queried for more details ([PERSON] et al., 2020). the knowledge base and reasoning capabilities through a RESTful interface. This framework comprises a Java-based Spring Boot (version 3.2.2) back-end application and an HTML5 user interface built with Bootstrap (version 5.3.2), as illustrated in Figure 3, and running on top of OpenJDK (version 17.0.9). The back end utilizes OWL reasoners and their descriptive logic (DL) capabilities to assess the vulnerability of a specific AI-enabled application through predefined DL queries declared in the Manchester OWL syntax. The aspiration is to further enhance the proof-of-concept implementation by incorporating a dashboard interface akin to the MITRE ATLAS Navigator (see [[https://atlas.mitre.org/navigator/](https://atlas.mitre.org/navigator/)]([https://atlas.mitre.org/navigator/](https://atlas.mitre.org/navigator/)) (accessed on 23 January 2024)). ## 4 Evaluation

In this section, we validate our cybersecurity framework by leveraging the knowledge bases mentioned earlier and illustrate the main benefits as well as the practical feasibility of our framework to reason upon a large body of knowledge. Our framework and the benchmark application are executed in Ubuntu (version 23.10), operating on an HP ZBook Power 15.6 inch G8 Mobile Workstation PC featuring an 11 th Gen Intel Core i7-11800H running at 2.30 GHz and 32 GB RAM. ### Qualitative Evaluation: Adaptability of the Cybersecurity Framework

Modifying, adding, or removing an attack or defense is facilitated through instantiating ontology classes and properties. The ontology-based approach of our cybersecurity framework enables the incorporation of new attacks and defenses without necessitating changes to the software (see Figure 3). The rationale behind embedding a description logic-based ontology reasoner into our software, as opposed to implementing rigid if-then-else rules, is two-fold. Firstly, the reasoner infers implicit interdependencies between

Figure 3: Spring Boot-based cybersecurity framework for vulnerability assessment with MITRE ATLAS semantic extensions. attacks and defenses by leveraging semantic relationships, a task not easily achieved with hard-coded if-then-else rules.