Biblio

The IEC 61850 protocol suite for electrical sub-station automation enables substation configuration and design for protection, communication, and control. These power system applications can be formally verified through use of object models, common data classes, and message classes. The IEC 61850-7-420 DER (Distributed Energy Resource) extension further defines object classes for assets such as types of DER (e.g., energy storage, photovoltaic), DER unit controllers, and other DER-associated devices (e.g., inverter). These object classes describe asset-specific attributes such as state of charge, capacity limits, and ramp rate. Attributes can be fixed (rated capacity of the device) dynamic (state of charge), or binary (on or off, dispatched or off-line, operational or fault state). We sketch out a proposed ontology based on the 61850 and 61850-7-420 DER object classes to model threats against a micro-grid, which is an electrical system consisting of controllable loads and distributed generation that can function autonomously (in island mode) or connected to a larger utility grid. We consider threats against the measurements on which the control loop is based, as well as attacks against the control directives and the communication infrastructure. We use this ontology to build a threat model using the ADversary View Security Evaluation (ADVISE) framework, which enables identification of attack paths based on adversary objectives (for example, destabilize the entire micro-grid by reconnecting to the utility without synchronization) and helps identify defender strategies. Furthermore, the ADVISE method provides quantitative security metrics that can help inform trade-off decisions made by system architects and controls.

Given the growing sophistication of cyber attacks, designing a perfectly secure system is not generally possible. Quantitative security metrics are thus needed to measure and compare the relative security of proposed security designs and policies. Since the investigation of security breaches has shown a strong impact of human errors, ignoring the human user in computing these metrics can lead to misleading results. Despite this, and although security researchers have long observed the impact of human behavior on system security, few improvements have been made in designing systems that are resilient to the uncertainties in how humans interact with a cyber system. In this work, we develop an approach for including models of user behavior, emanating from the fields of social sciences and psychology, in the modeling of systems intended to be secure. We then illustrate how one of these models, namely general deterrence theory, can be used to study the effectiveness of the password security requirements policy and the frequency of security audits in a typical organization. Finally, we discuss the many challenges that arise when adopting such a modeling approach, and then present our recommendations for future work.