Industrial control systems (ICS) include systems that control industrial processes in critical infrastructure such as electric grids, nuclear power plants, manufacturing plans, water treatment systems, pharmaceutical plants, and building automation systems. ICS represent complex systems that contain an abundance of unique devices all of which may hold different types of software, including applications, firmware and operating systems. Due to their ability to control physical infrastructure, ICS have more and more become targets of cyber-attacks, increasing the risk of serious damage, negative financial impact, disruption to business operations, disruption to communities, and even the loss of life. Ethical hacking represents one way to test the security of ICS. Ethical hacking consists of using a cyber-attacker's perspective and a variety of cybersecurity tools to actively discover vulnerabilities and entry points for potential cyber-attacks. However, ICS ethical hacking represents a difficult task due to the wide variety of devices found on ICS networks. Most ethical hackers do not hold expertise or knowledge about ICS hardware, device computing elements, protocols, vulnerabilities found on these elements, and exploits used to exploit these vulnerabilities. Effective approaches are needed to reduce the complexity of ICS ethical hacking tasks. In this study, we use ontology modeling, a knowledge representation approach in artificial intelligence (AI), to model data that represent ethical hacking tasks of building automation systems. With ontology modeling, information is stored and represented in the form of semantic graphs that express individuals, their properties, and the relations between multiple individuals. Data are drawn from sources such as the National Vulnerability Database, ExploitDB, Common Weakness Enumeration (CWE), the Common Attack Pattern and Enumeration Classification (CAPEC), and others. We show, through semantic queries, how the ontology model can automatically link together entities such as software names and versions of ICS software, vulnerabilities found on those software instances, vulnerabilities found on the protocols used by the software, exploits found on those vulnerabilities, weaknesses that represent those vulnerabilities, and attacks that can exploit those weaknesses. The ontology modeling of ICS ethical hacking and the semantic queries run over the model can reduce the complexity of ICS hacking tasks.
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FALCON: Framework for Anomaly Detection in Industrial Control Systems
Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.
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- Award ID(s):
- 1846493
- NSF-PAR ID:
- 10222650
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 9
- Issue:
- 8
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 1192
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Detection of deception attacks is pivotal to ensure the safe and reliable operation of cyber-physical systems (CPS). Detection of such attacks needs to consider time-series sequences and is very challenging especially for autonomous vehicles that rely on high-dimensional observations from camera sensors. The paper presents an approach to detect deception attacks in real-time utilizing sensor observations, with a special focus on high-dimensional observations. The approach is based on inductive conformal anomaly detection (ICAD) and utilizes a novel generative model which consists of a variational autoencoder (VAE) and a recurrent neural network (RNN) that is used to learn both spatial and temporal features of the normal dynamic behavior of the system. The model can be used to predict the observations for multiple time steps, and the predictions are then compared with actual observations to efficiently quantify the nonconformity of a sequence under attack relative to the expected normal behavior, thereby enabling real-time detection of attacks using high-dimensional sequential data. We evaluate the approach empirically using two simulation case studies of an advanced emergency braking system and an autonomous car racing example, as well as a real-world secure water treatment dataset. The experiments show that the proposed method outperforms other detection methods, and in most experiments, both false positive and false negative rates are less than 10%. Furthermore, execution times measured on both powerful cloud machines and embedded devices are relatively short, thereby enabling real-time detection.
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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 methods 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.more » « less
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A water treatment center (WTC) removes contaminants and unwanted components from the water and makes the water more acceptable to the end-users. A modern WTC is equipped with different water sensors and uses a combination of wired/wireless communication network. During the water treatment process, controllers periodically collect sensor measurements and make important operational decisions. Since accuracy is vital, a WTC also uses different data validation mechanisms to validate the incoming sensor measurements. However, like any other cyber-physical system, water treatment facilities are prone to cyberattacks and an intelligent adversary can alter the sensors measurements stealthily, and corrupt the water treatment process. In this work, we propose WTC Checker (WTC2), an impact-aware formal analysis framework that demonstrates the impact of stealthy false data injection attacks on the water treatment sensors. Through our work, we demonstrate that if an adversary has sufficient access to sensor measurements and can evade the data validation process, he/she can compromise the sensors measurements, change the water disinfectant contact time, and inflict damage to the clean water production process. We model this attack as a constraint satisfaction problem (CSP) and encode it using Satisfiability Modulo Theories (SMT). We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on different synthetic test cases.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/electronics9081192
