What is it about?
For our everyday life, we are dependent on critical infrastructures, for example, water treatment systems, waste management plants, energy systems, autonomous transport, and communication systems. These systems are much more connected with each other, with people, and with the devices on the internet, than they used to be in the past. If not enough attention is given these critical services could fall prey in the hands of bad actors and it is even easier now since these systems are connected to the internet. We venture to ensure these systems work securely and safely and citizens can enjoy these critical services without any disruption.
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Why is it important?
The critical systems like industrial control systems or industrial internet of things have few major components, those include sensors to measure the physical surroundings, controllers to compute and keep the system in the desired state based on the sensor measurements, and the actuators to drive the physical system, e.g., an electric motor to run an autonomous train. The challenge is to ensure all the devices are securely operating as desired and have not been modified with malicious intent. Imagine if a sensor responsible to detect humans in an autonomous vehicle fails or gets compromised/hacked then the consequences can be catastrophic. Our work is in the direction to ensure that we can detect such a malicious or accidental failure of a sensor. Surprisingly we achieve our goal by using an undesirable feature called the "noise" of the sensor. Noise is defined as the inaccuracies in measurements due to sensors manufacturing imperfections. Therefore, it is shown that the noise for each sensor is different and makes a unique identifier for each sensor.
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This page is a summary of: NoiSense Print, ACM Transactions on Privacy and Security, January 2021, ACM (Association for Computing Machinery),
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Noise Matters: Using Sensor and Process Noise Fingerprint to Detect Stealthy Cyber Attacks and Authenticate sensors in CPS
A novel scheme is proposed to authenticate sensors and detect data integrity attacks in a Cyber Physical System (CPS). The proposed technique uses the hardware characteristics of a sensor and physics of a process to create unique patterns (herein termed as fingerprints) for each sensor. The sensor fingerprint is a function of sensor and process noise embedded in sensor measurements. Uniqueness in the noise appears due to manufacturing imperfections of a sensor and due to unique features of a physical process. To create a sensor's fingerprint a system-model based approach is used. A noise-based fingerprint is created during the normal operation of the system. It is shown that under data injection attacks on sensors, noise pattern deviations from the fingerprinted pattern enable the proposed scheme to detect attacks. Experiments are performed on a dataset from a real-world water treatment (SWaT) facility. A class of stealthy attacks is designed against the proposed scheme and extensive security analysis is carried out. Results show that a range of sensors can be uniquely identified with an accuracy as high as 98%. Extensive sensor identification experiments are carried out on a set of sensors in SWaT testbed. The proposed scheme is tested on a variety of attack scenarios from the reference literature which are detected with high accuracy.
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