<i>NoisePrint</i>

Ahmed, Chuadhry Mujeeb; Ochoa, Martín; Zhou, Jianying; Mathur, Aditya P.; Qadeer, Rizwan; Murguia, Carlos; Ruths, Justin

doi:10.1145/3196494.3196532

Cited by 53 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 87 ] is best work to consult. There is a sensor multitude consisting of pressure, level, quantity, and flow [ 88 ]. The work in question shows that unsupervised learning is better than supervised learning for attack detection in water treatment plants.…”

Section: Evil Eyes On the Water Purification And Distribution Environ...mentioning

confidence: 99%

A Comprehensive Study on Cyber Attacks in Communication Networks in Water Purification and Distribution Plants: Challenges, Vulnerabilities, and Future Prospects

Aslam,

Tufail,

Kim

et al. 2023

Sensors

View full text Add to dashboard Cite

In recent years, the Internet of Things (IoT) has had a big impact on both industry and academia. Its profound impact is particularly felt in the industrial sector, where the Industrial Internet of Things (IIoT), also known as Industry 4.0, is revolutionizing manufacturing and production through the fusion of cutting-edge technologies and network-embedded sensing devices. The IIoT revolutionizes several industries, including crucial ones such as oil and gas, water purification and distribution, energy, and chemicals, by integrating information technology (IT) with industrial control and automation systems. Water, a vital resource for life, is a symbol of the advancement of technology, yet knowledge of potential cyberattacks and their catastrophic effects on water treatment facilities is still insufficient. Even seemingly insignificant errors can have serious consequences, such as aberrant pH values or fluctuations in the concentration of hydrochloric acid (HCI) in water, which can result in fatalities or serious diseases. The water purification and distribution industry has been the target of numerous hostile cyber security attacks, some of which have been identified, revealed, and documented in this paper. Our goal is to understand the range of security threats that are present in this industry. Through the lens of IIoT, the survey provides a technical investigation that covers attack models, actual cases of cyber intrusions in the water sector, a range of security difficulties encountered, and preventative security solutions. We also explore upcoming perspectives, illuminating the predicted advancements and orientations in this dynamic subject. For industrial practitioners and aspiring scholars alike, our work is a useful, enlightening, and current resource. We want to promote a thorough grasp of the cybersecurity landscape in the water industry by combining key insights and igniting group efforts toward a safe and dependable digital future.

show abstract

Section: Evil Eyes On the Water Purification And Distribution Environ...mentioning

confidence: 99%

A Comprehensive Study on Cyber Attacks in Communication Networks in Water Purification and Distribution Plants: Challenges, Vulnerabilities, and Future Prospects

Aslam,

Tufail,

Kim

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, being used in a testbed for few weeks is different from being used in a real-world production system of physical plants with possibly more harsh environment. It has been discussed in earlier work [16] that measurement noise profile of the devices might change over time due to wear and tear. However, as seen in Figure 8 if we can drive the output response far from the normal process noise then a higher accuracy can be achieved.…”

Section: Limitationsmentioning

confidence: 99%

A practical physical watermarking approach to detect replay attacks in a CPS

Ahmed

Palleti

Mishra

2022

Journal of Process Control

View full text Add to dashboard Cite

“…There is a multitude of sensors including level, flow, pressure, and chemical sensors for measuring the water quality and quantity. Studies have reported results from using models derived using supervised and unsupervised learning [3] on the SWaT testbed. It has been observed that supervised learning lacks scalability due to the lack of labeled data.…”

Section: Challenges: Model Creationmentioning

confidence: 99%

“…On the other hand, unsupervised algorithms can be trained for a large process plant without the need of having a labeled dataset. An interesting example of the scalability of one class classifiers is found in [3] for the case of sensor fingerprinting. The idea is that by using a oneclass classifier for each sensor, a unique fingerprint is created to detect intrusion without the need to train the classifier based on the labeled data from all the sensors.…”

Section: Challenges: Model Creationmentioning

confidence: 99%

“…Therefore, it is important to derive features that are specific to the physical process in an ICS. For example, one study on SwaT used the measurement noise generated from the sensors with machine learning algorithms to design an anomaly detector [3]. It is recommended that feature engineering should be specific to the physical process for it to be relevant to the ICS.…”

Section: Future Work and Recommendationsmentioning

confidence: 99%

See 1 more Smart Citation

Challenges in Machine Learning based approaches for Real-Time Anomaly Detection in Industrial Control Systems

Ahmed

Mathur

2020

Proceedings of the 6th ACM on Cyber-Physical System Security Workshop

Self Cite

View full text Add to dashboard Cite

Data-centric approaches are becoming increasingly common in the creation of defense mechanisms for critical infrastructure such as the electric power grid and water treatment plants. Such approaches often use well-known methods from machine learning and system identification, i.e., the Multi-Layer Perceptron, Convolutional Neural Network, and Deep Auto Encoders to create process anomaly detectors. Such detectors are then evaluated using data generated from an operational plant or a simulator; rarely is the assessment conducted in real time on a live plant. Regardless of the method to create an anomaly detector, and the data used for performance evaluation, there remain significant challenges that ought to be overcome before such detectors can be deployed with confidence in city-scale plants or large electric power grids. This position paper enumerates such challenges that the authors have faced when creating data-centric anomaly detectors and using them in a live plant. CCS CONCEPTS • Security and privacy → Intrusion/anomaly detection; • Computer systems organization → Sensors and actuators; Embedded systems; Dependable and fault-tolerant systems and networks.

show abstract

NoisePrint

Cited by 53 publications

References 12 publications

A Comprehensive Study on Cyber Attacks in Communication Networks in Water Purification and Distribution Plants: Challenges, Vulnerabilities, and Future Prospects

A Comprehensive Study on Cyber Attacks in Communication Networks in Water Purification and Distribution Plants: Challenges, Vulnerabilities, and Future Prospects

A practical physical watermarking approach to detect replay attacks in a CPS

Challenges in Machine Learning based approaches for Real-Time Anomaly Detection in Industrial Control Systems

Contact Info

Product

Resources

About