  Identification of cyber threats is one of the essential tasks for security
teams. Currently, cyber threats can be identified using knowledge organized
into various formats, enumerations, and knowledge bases. This paper studies the
current challenges of identifying vulnerabilities and threats in cyberspace
using enumerations and data about assets. Although enumerations are used in
practice, we point out several issues that still decrease the quality of
vulnerability and threat identification. Since vulnerability identification
methods are based on network monitoring and agents, the issues are related to
the asset discovery, the precision of vulnerability discovery, and the amount
of data. On the other hand, threat identification utilizes graph-based,
nature-language, machine-learning, and ontological approaches. The current
trend is to propose methods that utilize tactics, techniques, and procedures
instead of low-level indicators of compromise to make cyber threat
identification more mature. Cooperation between standards from threat,
vulnerability, and asset management is also an unresolved issue confirmed by
analyzing relationships between public enumerations and knowledge bases. Last,
we studied the usability of techniques from the MITRE ATT&CK knowledge base for
threat modeling using network monitoring to capture data. Although network
traffic is not the most used data source, it allows the modeling of almost all
tactics from the MITRE ATT&CK.
