Aerospace and Electronic Systems Magazine November 2017 - 15
Asgari et al.
Figure 6.
CSIP dashboard.
Having defined a scenario of interest specified by the patterns
and/or keywords in a time interval, the operator using an advanced
mechanism of crawling and analysis can 1) acquire data from monitoring sources, 2) identify patterns related to a particular scenario,
and 3) extract generic or specific cases. The processing of the data
found in this way allows obtaining meta-data information, which
can subsequently be used for analysis. The results of investigations
conducted are immediately usable by analysts/operators through
the developed dashboard as shown in Figure 6. Once the relevant
information is obtained, the operator produces a security report
that can be sent to relevant domain authorities and disseminated
through the IDS module to other SMPs at the national or European
levels.
ATTACK EFFECT PREDICTION MODULE
The SMP serves as a central collector and analyzer of information
generated by a diverse set of security controls and event detectors.
The joint and sequential analysis of the received information is a
crucial task, as the data fusion [23] that is enabled by the SMP may
reduce the number of false alerts and enable temporal analysis of
the actions of the adversary.
The AEP is a decision support module that provides an assessment of the information received from different sensors (e.g. event
NOVEMBER 2017
detectors). The received information is used to address the following issues:
C
Is the system under attack?
C
What are the targets selected by the adversary?
C
What is the skill of the adversary?
In order to resolve the stated problems, the overall system should
be formally described. Following the approach used in network
security games [24], a directed graph structure is used as a system
descriptor. The graph encodes all SA as a subset of nodes and all
threat scenarios as a set of paths to these assets. Additionally, an
impact value for each type of attack for each security control is
given (or a set of values for different impact areas). Security controls and event detectors are linked to the nodes of the graph.
The model assumption is that the adversary selects a subset of
paths to the SA and security controls whereas event detectors may
detect the attacker's actions or mitigate the impact values. Using
the proposed graph model formalization, the state of the adversary may be described as a tuple (P,S,T). P stands for the position
(node in the graph), which may be empty in case of no attack taking place. S is the skill-vector of the adversary, which describes the
ability to overcome the security controls. T stands for the targeted
SA by the attacker. Thus, the system estimates the state of the ad-
IEEE A&E SYSTEMS MAGAZINE
15
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