421 research outputs found
Control Behavior Integrity for Distributed Cyber-Physical Systems
Cyber-physical control systems, such as industrial control systems (ICS), are
increasingly targeted by cyberattacks. Such attacks can potentially cause
tremendous damage, affect critical infrastructure or even jeopardize human life
when the system does not behave as intended. Cyberattacks, however, are not new
and decades of security research have developed plenty of solutions to thwart
them. Unfortunately, many of these solutions cannot be easily applied to
safety-critical cyber-physical systems. Further, the attack surface of ICS is
quite different from what can be commonly assumed in classical IT systems.
We present Scadman, a system with the goal to preserve the Control Behavior
Integrity (CBI) of distributed cyber-physical systems. By observing the
system-wide behavior, the correctness of individual controllers in the system
can be verified. This allows Scadman to detect a wide range of attacks against
controllers, like programmable logic controller (PLCs), including malware
attacks, code-reuse and data-only attacks. We implemented and evaluated Scadman
based on a real-world water treatment testbed for research and training on ICS
security. Our results show that we can detect a wide range of
attacks--including attacks that have previously been undetectable by typical
state estimation techniques--while causing no false-positive warning for
nominal threshold values.Comment: 15 pages, 8 figure
Design-Time Quantification of Integrity in Cyber-Physical-Systems
In a software system it is possible to quantify the amount of information
that is leaked or corrupted by analysing the flows of information present in
the source code. In a cyber-physical system, information flows are not only
present at the digital level, but also at a physical level, and to and fro the
two levels. In this work, we provide a methodology to formally analyse a
Cyber-Physical System composite model (combining physics and control) using an
information flow-theoretic approach. We use this approach to quantify the level
of vulnerability of a system with respect to attackers with different
capabilities. We illustrate our approach by means of a water distribution case
study
Stealthy Deception Attacks Against SCADA Systems
SCADA protocols for Industrial Control Systems (ICS) are vulnerable to
network attacks such as session hijacking. Hence, research focuses on network
anomaly detection based on meta--data (message sizes, timing, command
sequence), or on the state values of the physical process. In this work we
present a class of semantic network-based attacks against SCADA systems that
are undetectable by the above mentioned anomaly detection. After hijacking the
communication channels between the Human Machine Interface (HMI) and
Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a
fake view of the industrial process, deceiving the human operator into taking
manual actions. Our most advanced attack also manipulates the messages
generated by the operator's actions, reversing their semantic meaning while
causing the HMI to present a view that is consistent with the attempted human
actions. The attacks are totaly stealthy because the message sizes and timing,
the command sequences, and the data values of the ICS's state all remain
legitimate.
We implemented and tested several attack scenarios in the test lab of our
local electric company, against a real HMI and real PLCs, separated by a
commercial-grade firewall. We developed a real-time security assessment tool,
that can simultaneously manipulate the communication to multiple PLCs and cause
the HMI to display a coherent system--wide fake view. Our tool is configured
with message-manipulating rules written in an ICS Attack Markup Language (IAML)
we designed, which may be of independent interest. Our semantic attacks all
successfully fooled the operator and brought the system to states of blackout
and possible equipment damage
A Systematic Review of the State of Cyber-Security in Water Systems
Critical infrastructure systems are evolving from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks and artificial intelligence. Although this move improves sensing and control capacity and gives better integration with business requirements, it also increases the scope for attack from malicious entities that intend to conduct industrial espionage and sabotage against these systems. In this paper, we review the state of the cyber-security research that is focused on improving the security of the water supply and wastewater collection and treatment systems that form part of the critical national infrastructure. We cover the publication statistics of the research in this area, the aspects of security being addressed, and future work required to achieve better cyber-security for water systems
Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
Cyber-physical systems (CPSs) are widespread in critical domains, and
significant damage can be caused if an attacker is able to modify the code of
their programmable logic controllers (PLCs). Unfortunately, traditional
techniques for attesting code integrity (i.e. verifying that it has not been
modified) rely on firmware access or roots-of-trust, neither of which
proprietary or legacy PLCs are likely to provide. In this paper, we propose a
practical code integrity checking solution based on privacy-preserving black
box models that instead attest the input/output behaviour of PLC programs.
Using faithful offline copies of the PLC programs, we identify their most
important inputs through an information flow analysis, execute them on multiple
combinations to collect data, then train neural networks able to predict PLC
outputs (i.e. actuator commands) from their inputs. By exploiting the black box
nature of the model, our solution maintains the privacy of the original PLC
code and does not assume that attackers are unaware of its presence. The trust
instead comes from the fact that it is extremely hard to attack the PLC code
and neural networks at the same time and with consistent outcomes. We evaluated
our approach on a modern six-stage water treatment plant testbed, finding that
it could predict actuator states from PLC inputs with near-100% accuracy, and
thus could detect all 120 effective code mutations that we subjected the PLCs
to. Finally, we found that it is not practically possible to simultaneously
modify the PLC code and apply discreet adversarial noise to our attesters in a
way that leads to consistent (mis-)predictions.Comment: Accepted by the 29th ACM Joint European Software Engineering
Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE
2021
Learning-guided network fuzzing for testing cyber-physical system defences
The threat of attack faced by cyber-physical systems (CPSs), especially when
they play a critical role in automating public infrastructure, has motivated
research into a wide variety of attack defence mechanisms. Assessing their
effectiveness is challenging, however, as realistic sets of attacks to test
them against are not always available. In this paper, we propose smart fuzzing,
an automated, machine learning guided technique for systematically finding
'test suites' of CPS network attacks, without requiring any knowledge of the
system's control programs or physical processes. Our approach uses predictive
machine learning models and metaheuristic search algorithms to guide the
fuzzing of actuators so as to drive the CPS into different unsafe physical
states. We demonstrate the efficacy of smart fuzzing by implementing it for two
real-world CPS testbeds---a water purification plant and a water distribution
system---finding attacks that drive them into 27 different unsafe states
involving water flow, pressure, and tank levels, including six that were not
covered by an established attack benchmark. Finally, we use our approach to
test the effectiveness of an invariant-based defence system for the water
treatment plant, finding two attacks that were not detected by its physical
invariant checks, highlighting a potential weakness that could be exploited in
certain conditions.Comment: Accepted by ASE 201
- …