Interoperability is a crucial aspect of cybersecurity science that enables various systems, applications, and technologies to securely exchange information and communicate with each other. Common Language is a core theme of cybersecurity science that aims to establish a consistent and reliable language to express the security aspects of system architecture, risk assessments, and core principles such as trust relocation. Interoperability is critical to achieving this goal.
Interoperability can be divided into four levels, namely foundational, structural, semantic, and organizational. The foundational level is the basic level of interoperability, which establishes interconnectivity between different systems and enables basic data exchange services. At this level, the focus is on establishing a common language for communicating data, which enables two systems to communicate with each other. This level is critical in cybersecurity science, as it allows systems to communicate with each other, such as a security information and event management (SIEM) system receiving log data from a firewall device.
The structural level of interoperability is concerned with the format, syntax, and organization of data exchange, including at the data field level for interpretation. Structural interoperability is about defining the format, syntax, and organization of data exchange. For instance, the use of XML and JSON enables developers to write code to work with the data in their applications or systems. At this level, structural standards such as OASIS Structured Threat Information eXpression (STIX) language can be used to describe cyber threat intelligence.
At the semantic level of interoperability, the focus is on creating a common vocabulary that enables accurate and reliable machine-to-machine communication across information silos. This level provides for common underlying models and codification of the data, including the use of data elements with standardized definitions from publicly available vocabularies, providing shared understanding and meaning to the user. Semantic interoperability enables systems to interpret data accurately, which is critical in cybersecurity science. For instance, an ontology can be created with OWL/RDF to represent the different types of cyber threats that an organization may face, including their characteristics and potential impact.
The organizational level of interoperability focuses on governance, policy, social, legal, and organizational considerations to facilitate the secure, seamless, and timely communication and use of data both within and between organizations, entities, and individuals. This level enables shared consent, trust, and integrated end-user processes and workflows.
Let's focus on and compare structural common languages like XML and JSON with semantic common languages like OWL and RDF to better understand the difference between these different types of common languages since this is where we're seeing a paradigm shift in cybersecurity.
Structural Common Languages: XML and JSON
Structural common languages such as XML and JSON are used to define the structure and format of data for inter-system communication. In the case of XML, data is defined using tags and attributes that are enclosed in angle brackets. JSON, on the other hand, uses a key-value pair format to represent data.
In structural common languages, the meaning of the data is not explicitly defined. Instead, the data is mapped to a specific structure that is agreed upon by both the sender and receiver. This means that developers need to have a common understanding of the data structure to be able to interpret the data correctly.
Developers have to hardwire the meaning of the data into the system using structural common languages. This makes it more challenging to ensure that all parties involved in the communication have the same understanding of the data structure. Any change to the data structure requires that all parties involved in the communication agree on the new structure to be able to interpret the data correctly.
Structural common languages are primarily used for machine-to-machine communication, and their primary focus is on the format and structure of the data. They are suitable for situations where the data format is unlikely to change, and where the meaning of the data is well-understood by all parties.
Semantic Common Languages: OWL and RDF
In contrast, semantic common languages such as OWL (Web Ontology Language) and RDF (Resource Description Framework) are used to define the meaning of data. They are used to provide a shared understanding of the data, which makes it easier for different systems to exchange and interpret data accurately.
In semantic common languages, the meaning of the data is explicitly defined using ontologies. An ontology is a formal specification of the concepts and relationships that are used to describe a particular domain. For example, an ontology for a medical domain might define concepts such as "disease," "symptom," and "treatment," along with the relationships between them.
In OWL, the concepts and relationships in an ontology are expressed using a logical formalism. This makes it possible for machines to reason about the data and make inferences to support data storytelling based on the relationships between concepts. For example, if an ontology includes the concept of "heart disease" and the relationship "causes," a machine can infer that a patient with "heart disease" is at risk of developing complications that are "caused" by the disease.
RDF, on the other hand, is a simpler language for describing resources and their relationships. RDF provides a framework for representing data as subject-predicate-object statements, which can be used to define the meaning of the data. For example, an RDF statement might describe a person as having a name and an email address.
The use of semantic common languages allows for more flexible and extensible data structures. Changes to the meaning of the data can be easily accommodated by updating the ontology, which describes the meaning of the data, rather than the data structure itself. This makes it easier to maintain a shared understanding of the data, even as it evolves over time.
In conclusion, the transition from foundational and structural levels of interoperability to the semantic level of interoperability is crucial to achieving the Common Language core theme of cybersecurity science. Technologies such as XML and JSON enable foundational and structural interoperability, while semantic interoperability involves knowledge engineering and ontologies such as OWL and RDF. Organizations must strive to achieve interoperability at all levels to enable effective communication and data exchange, which is critical for the success of cybersecurity science.