skip to main content

Title: Theory and implementation of dynamic watermarking for cybersecurity of advanced transportation systems
We consider a prototypical intelligent transportation system with a control law that is specifically designed to avoid collisions. We experimentally demonstrate that, nevertheless, an attack on a position sensor can result in collisions between vehicles. This is a consequence of the feeding of malicious sensor measurements to the controller and the collision avoidance module built into the system. This is an instance of the broader concern of cybersecurity vulnerabilities opened up by the increasing integration of critical physical infrastructures with the cyber system. We consider a solution based on “dynamic watermarking” of signals to detect and stop such attacks on cyber-physical systems. We show how dynamic watermarking can handle nonlinearities arising in vehicular models. We then experimentally demonstrate that employing this nonlinear extension indeed restores the property of collision freedom even in the presence of attacks.
Authors:
; ;
Award ID(s):
1646449
Publication Date:
NSF-PAR ID:
10037670
Journal Name:
2016 IEEE Conference on Communications and Network Security (CNS)
Page Range or eLocation-ID:
416-420
Sponsoring Org:
National Science Foundation
More Like this
  1. We address the problem of security of cyber-physical systems where some sensors may be malicious. We consider a multiple-input, multiple-output stochastic linear dynamical system controlled over a network of communication and computational nodes which contains (i) a controller that computes the inputs to be applied to the physical plant, (ii) actuators that apply these inputs to the plant, and (iii) sensors which measure the outputs of the plant. Some of these sensors, however, may be malicious. The malicious sensors do not report the true measurements to the controller. Rather, they report false measurements that they fabricate, possibly strategically, so as to achieve any objective that they may have, such as destabilizing the closed-loop system or increasing its running cost. Recently, it was shown that under certain conditions, an approach of “dynamic watermarking” can secure such a stochastic linear dynamical system in the sense that either the presence of malicious sensors in the system is detected, or the malicious sensors are constrained to adding a distortion that can only be of zero power to the noise already entering the system. The first contribution of this paper is to generalize this result to partially observed MIMO systems with both process and observationmore »noises, a model which encompasses some of the previous models for which dynamic watermarking was established to guarantee security. This result, similar to the prior ones, is shown to hold when the controller subjects the reported sequence of measurements to two particular tests of veracity. The second contribution of this paper is in showing, via counterexamples, that both of these tests are needed in order to secure the control system in the sense that if any one of these two tests of sensor veracity is dropped, then the above guarantee does not hold. The proposed approach has several potential applications, including in smart grids, automated transportation, and process control.« less
  2. The controllers for a cyber-physical system may be impacted by sensor measurement cyberattacks, actuator signal cyberattacks, or both types of attacks. Prior work in our group has developed a theory for handling cyberattacks on process sensors. However, sensor and actuator cyberattacks have a different character from one another. Specifically, sensor measurement attacks prevent proper inputs from being applied to the process by manipulating the measurements that the controller receives, so that the control law plays a role in the impact of a given sensor measurement cyberattack on a process. In contrast, actuator signal attacks prevent proper inputs from being applied to a process by bypassing the control law to cause the actuators to apply undesirable control actions. Despite these differences, this manuscript shows that we can extend and combine strategies for handling sensor cyberattacks from our prior work to handle attacks on actuators and to handle cases where sensor and actuator attacks occur at the same time. These strategies for cyberattack-handling and detection are based on the Lyapunov-based economic model predictive control (LEMPC) and nonlinear systems theory. We first review our prior work on sensor measurement cyberattacks, providing several new insights regarding the methods. We then discuss how those methodsmore »can be extended to handle attacks on actuator signals and then how the strategies for handling sensor and actuator attacks individually can be combined to produce a strategy that is able to guarantee safety when attacks are not detected, even if both types of attacks are occurring at once. We also demonstrate that the other combinations of the sensor and actuator attack-handling strategies cannot achieve this same effect. Subsequently, we provide a mathematical characterization of the “discoverability” of cyberattacks that enables us to consider the various strategies for cyberattack detection presented in a more general context. We conclude by presenting a reactor example that showcases the aspects of designing LEMPC.« less
  3. Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction lossmore »while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.« less
  4. In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite various prior studies on its security issues, all of them only consider attacks on cameraor LiDAR-based AD perception alone. However, production AD systems today predominantly adopt a Multi-Sensor Fusion (MSF) based design, which in principle can be more robust against these attacks under the assumption that not all fusion sources are (or can be) attacked at the same time. In this paper, we present the first study of security issues of MSF-based perception in AD systems. We directly challenge the basic MSF design assumption above by exploring the possibility of attacking all fusion sources simultaneously. This allows us for the first time to understand how much security guarantee MSF can fundamentally provide as a general defense strategy for AD perception. We formulate the attack as an optimization problem to generate a physically-realizable, adversarial 3D-printed object that misleads an AD system to fail in detecting it and thus crash into it. To systematically generate such a physical-world attack, we propose a novel attack pipeline that addresses two main design challenges: (1) non-differentiable target camera and LiDAR sensing systems, and (2) non-differentiable cell-level aggregated features popularly used in LiDAR-basedmore »AD perception. We evaluate our attack on MSF algorithms included in representative open-source industry-grade AD systems in real-world driving scenarios. Our results show that the attack achieves over 90% success rate across different object types and MSF algorithms. Our attack is also found stealthy, robust to victim positions, transferable across MSF algorithms, and physical-world realizable after being 3D-printed and captured by LiDAR and camera devices. To concretely assess the end-to-end safety impact, we further perform simulation evaluation and show that it can cause a 100% vehicle collision rate for an industry-grade AD system. We also evaluate and discuss defense strategies.« less
  5. Defense mechanisms against network-level attacks are commonly based on the use of cryptographic techniques, such as lengthy message authentication codes (MAC) that provide data integrity guarantees. However, such mechanisms require significant resources (both computational and network bandwidth), which prevents their continuous use in resource-constrained cyber-physical systems (CPS). Recently, it was shown how physical properties of controlled systems can be exploited to relax these stringent requirements for systems where sensor measurements and actuator commands are transmitted over a potentially compromised network; specifically, that merely intermittent use of data authentication (i.e., at occasional time points during system execution), can still provide strong Quality-of-Control (QoC) guarantees even in the presence of false-data injection attacks, such as Man-in-the-Middle (MitM) attacks. Consequently, in this work, we focus on integrating security into existing resource-constrained CPS, in order to protect against MitM attacks on a system where a set of control tasks communicates over a real-time network with system sensors and actuators. We introduce a design-time methodology that incorporates requirements for QoC in the presence of attacks into end-to-end timing constraints for real-time control transactions, which include data acquisition and authentication, real-time network messages, and control tasks. This allows us to formulate a mixed integer linear programming-basedmore »method for direct synthesis of schedulable tasks and message parameters (i.e., deadlines and offsets) that do not violate timing requirements for the already deployed controllers, while adding a sufficient level of protection against network-based attacks; specifically, the synthesis method also provides suitable intermittent authentication policies that ensure the desired QoC levels under attack. To additionally reduce the security-related bandwidth overhead, we propose the use of cumulative message authentication at time instances when the integrity of messages from subsets of sensors should be ensured. Furthermore, we introduce a method for the opportunistic use of the remaining resources to further improve the overall QoC guarantees while ensuring system (i.e., task and message) schedulability. Finally, we demonstrate applicability and scalability of our methodology on synthetic automotive systems as well as a real-world automotive case-study.« less