Hardware Identification via Sensor Fingerprinting in a Cyber Physical System

Ahmed, Chuadhry Mujeeb; Mathur, Aditya P.

doi:10.1109/qrs-c.2017.89

Cited by 14 publications

(13 citation statements)

References 9 publications

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“…Analysis is carried out on 2 latching relays based on their operation timings. Another related work presented a preliminary study, on the idea of sensor fingerprinting in [86], using 2 sensors, based on correlation analysis with an accuracy of 86%. However, to the best of our knowledge, this article present the first attempt to present a rigorous analysis on a multitude of sensors specific to ICS.…”

Section: Software Based Fingerprintingmentioning

confidence: 99%

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Ahmed

Mathur

Ochoa

2020

ACM Trans. Priv. Secur.

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In recent years fingerprinting of various physical and logical devices has been proposed with the goal of uniquely identifying users or devices of mainstream IT systems such as PCs, Laptops and smart phones. On the other hand, the application of such techniques in Cyber-Physical Systems (CPS) is less explored due to various reasons, such as difficulty of direct access to critical systems and the cost involved in faithfully reproducing realistic scenarios. In this work we evaluate the feasibility of using fingerprinting techniques in the context of realistic Industrial Control Systems related to water treatment and distribution. Based on experiments conducted with 44 sensors of six different types, it is shown that noise patterns due to microscopic imperfections in hardware manufacturing can be used to uniquely identify sensors in a CPS with up to 97% accuracy. The proposed technique can be used in to detect physical attacks, such as the replacement of legitimate sensors by faulty or manipulated sensors. We also show that, unexpectedly, sensor fingerprinting can effectively detect advanced physical attacks such as analog sensor spoofing due to variations in received energy at the transducer of an active sensor. Also, it can be leveraged to construct a novel challenge-response protocol that exposes cyber-attacks.

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Section: Software Based Fingerprintingmentioning

confidence: 99%

“…Regions A, B and C in Figure 9 show, respectively, the sensor readings before, during, and after the attack. A detailed analysis on such an attack and preliminary results based on sensor fingerprint, is presented in form of a short paper [86].…”

Section: Appendix a Physical Sensor Swap Attack In Swat Testbedmentioning

confidence: 99%

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Ahmed

Mathur

Ochoa

2020

ACM Trans. Priv. Secur.

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“…In [16], the authors proposed sensor noise fingerprinting to detect attacks on physical components of a CPS with a focus on ultrasonic level sensors on the SWaT testbed. To calculate the noise fingerprint for a sensor, first they collected its data from the healthy runs which contained no attack.…”

Section: Related Workmentioning

confidence: 99%

PLC memory attack detection and response in a clean water supply system

Robles-Durazno

Moradpoor

McWhinnie

et al. 2019

International Journal of Critical Infrastructure Protection

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Industrial Control Systems (ICS) are frequently used in manufacturing and critical infrastructures like water treatment, chemical plants, and transportation schemes. Citizens tend to take modern-day conveniences such as trains, planes or tap water for granted without considering the critical systems involved for their operations. Interrupting these industries could lead to disastrous consequences, leading to financial losses or even costing human lives. For that reason, researchers have been actively investigating the threats targeting ICS. In this paper, the authors propose a mechanism of attack detection and mitigation for attacks focusing on the input memory of Programming Logic Controllers (PLCs). To help investigate this concept, a testbed that models a clean water supply system was built using components and technologies currently used in the industry. The mechanism supporting attack detection and response for the input memory is implemented within the PLC itself as part of its programming. The mechanism of response involves three different techniques: optimised datablocks, switching between control strategies and obtaining the sensor readings directly from its analogue channel. The results demonstrate the feasibility of the proposed approach along with the effectiveness of each response mechanism.

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“…For this reason, many researchers have started focusing on developing techniques and approaches for anomaly detection within ICS processes. Existing state-of-the-art research can be divided into two categories: (I) Formal methods based on mathematical models and behavior specification of processes [6][7][8][9][10], and (II) data driven approaches using: (a) machine learning (ML) [11][12][13], (b) deep learning (DL) [14][15][16][17], and (c) statistical modeling [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Any deviations from the created signatures are recognized as anomalies. This approach is highly susceptible to noise in the operating environment, which limits it to a small number of devices in a process [18].…”

Section: Introductionmentioning

confidence: 99%

FALCON: Framework for Anomaly Detection in Industrial Control Systems

et al. 2020

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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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Hardware Identification via Sensor Fingerprinting in a Cyber Physical System

Cited by 14 publications

References 9 publications

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PLC memory attack detection and response in a clean water supply system

FALCON: Framework for Anomaly Detection in Industrial Control Systems

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