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Data-Driven Correlation of Cyber and Physical Anomalies for Holistic System Health Monitoring

Daniel L. Marino, Chathurika S. Wickramasinghe, Billy Tsouvalas, Craig Rieger, Milos Manic
2021 IEEE Access  
In this paper, we present an approach for holistic health monitoring of cyber-physical systems based on cyber and physical anomaly detection and correlation.  ...  We provide a data-driven approach for the detection of cyber and physical anomalies based on machine learning.  ...  The best performance for anomaly detection was obtained using Autoencoders for both cyber and physical ADSs.  ... 
doi:10.1109/access.2021.3131274 fatcat:wu2mw67qbnh7laj7aejksksity

Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection

Ralf Luis de Moura, Virginia N. L. Franqueira, Gustavo Pessin
2021 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)  
Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.  ...  The high rates demonstrate that highly cyclic and repeatable networks make anomaly detection simpler. VIII.  ...  The anomaly can represent a cyber security event or an issue in the typical network behavior (a defect, for example).  ... 
doi:10.1109/ictai52525.2021.00189 fatcat:kn3mi3hgc5du3lunp2lu3wmgta

Autoencoder Based Anomaly Detection for SCADA Networks

Sajid Nazir, Shushma Patel, Dilip Patel
2021 International Journal of Artificial Intelligence and Machine Learning  
These are cyber physical systems, which are increasingly integrated with networks and internet of things devices.  ...  However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns.  ...  Thus cyber intrusions required physical access to the system, for example, as in the case for Stuxnet (Langner, 2011) .  ... 
doi:10.4018/ijaiml.20210701.oa6 fatcat:rht2v4jn3zdnnoiyx457qie3qq

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series [article]

Dan Li and Dacheng Chen and Jonathan Goh and See-kiong Ng
2019 arXiv   pre-print
Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks.  ...  In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs.  ...  CONCLUSIONS Cyber-Physical Systems are large, complex, and affixed with networked sensors and actuators that generate large amounts of data streams.  ... 
arXiv:1809.04758v3 fatcat:lj24chtitba2lfyhxjnxbu556u

Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey

Valliammal N, Barani Shaju
2018 International Journal of Advanced Technology and Engineering Exploration  
Over the last years, cyber-attack detection and control system design has become a significant area in cyber-physical systems (CPSs) due to the rapid growth of cyber-security challenges via sophisticated  ...  The different deep learning algorithm based cyber-attack detection schemes have been designed to detect and mitigate the different types of cyber-attacks through CPSs, smart grids, power systems, etc.  ...  Anomaly detection in a water management system [8] was proposed based on the unsupervised machine learning algorithm.  ... 
doi:10.19101/ijatee.2018.547030 fatcat:ivfb6skvyrcdve6mo5qtrajdty

Enhanced Cyber-Physical Security through Deep Learning Techniques

Mayra Alexandra Macas Carrasco, Chunming Wu
2019 CPS Summer School  
Nowadays that various aspects of our lives depend on complex cyber-physical systems, automated anomaly detection, as well as attack prevention and reaction have become of paramount importance and directly  ...  We proposed an anomaly detection framework for complex systems based on monitored data storage and Statistical Correlation Analysis for different pairs of constituent time series of a multivariate time  ...  In this paper, we focus on an unsupervised machine-learning based anomaly detection approach, based on which we attempt to detect anomalous behavior of the system at the physical level.  ... 
dblp:conf/cpsschool/CarrascoW19 fatcat:fi7secbgyncbfmm6bd6siq4zl4

Prognostic Health Management of Production Systems. New Proposed Approach and Experimental Evidences

Francesca Calabrese, Alberto Regattieri, Lucia Botti, Francesco Gabriele Galizia
2019 Procedia Manufacturing  
The emerging Cyber-Physical Systems (CPS) technologies connect distributed physical systems with their virtual representations in the cyber computational world.  ...  Different unsupervised and online anomaly detection methods are combined with evolving clustering models in order to detect anomalous behaviours in streaming vibration data and integrate the so-generated  ...  IoT-based Industrial Cyber-Physical Systems Industrial Cyber-Physical Systems (I-CPS) integrate the physical world, made of sensors, actuators and equipment deployed in the industrial plant, with the cyber  ... 
doi:10.1016/j.promfg.2020.01.333 fatcat:j7oeu5lmubempipjtjs5ivsgwe

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks [article]

Dan Li, Dacheng Chen, Lei Shi, Baihong Jin, Jonathan Goh, and See-Kiong Ng
2019 arXiv   pre-print
the system for detecting anomalies.  ...  In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs).  ...  Conclusions Today's cyber-physical systems, affixed with networked sensors and actuators, generate large amounts of data streams that can be used for monitoring the system behaviors to detect anomalies  ... 
arXiv:1901.04997v1 fatcat:cl2oslliybeyrp4hqcvokuvxgi

Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

Raogo Kabore, Adlès Kouassi, Rodrigue N'goran, Olivier Asseu, Yvon Kermarrec, Philippe Lenca
2021 Engineering  
Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks.  ...  Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security  ...  Stacked Auto-Encoder for Anomaly Detection in Smart Grids The cyber-physical integration, exposes smart grids to large attack surface with potential severe consequences.  ... 
doi:10.4236/eng.2021.131003 fatcat:dypnktvjszad3nw6yjjuwgeeum

An Autonomous Cyber-Physical Anomaly Detection System Based on Unsupervised Disentangled Representation Learning

Chunyu Li, Xiaobo Guo, Xiaowei Wang, Konstantinos Demertzis
2021 Security and Communication Networks  
In this paper, considering the high importance of the operational status of CPS for heavy industry, an innovative autonomous anomaly detection system based on unsupervised disentangled representation learning  ...  Cyber-Physical Systems (CPS) in heavy industry are a combination of closely integrated physical processes, networking, and scientific computing.  ...  For training the cyber and physical machine learning anomaly detection algorithms, IREST employed unsupervised learning. e findings revealed that unsupervised learning performed similarly to managed techniques  ... 
doi:10.1155/2021/1626025 fatcat:wivtxhoefrhsfdwj7nzu56rxia

STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems [article]

Mohammad Adiban, Arash Safari, Giampiero Salvi
2020 arXiv   pre-print
In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs).  ...  In recent years, however, advances in the field of machine learning, have raised concerns about cyber attacks on these systems.  ...  INTRODUCTION Cyber-physical systems (CPS) are defined as transformative technologies that integrate interconnected systems, including physical devices and computational networks in order to control and  ... 
arXiv:2009.05184v1 fatcat:exh5b336qffffdwa354hj3jvju

Anomaly Detection in ICS Datasets with Machine Learning Algorithms

Sinil Mubarak, Mohamed Hadi Habaebi, Md Rafiqul Islam, Farah Diyana Abdul Rahman, Mohammad Tahir
2021 Computer systems science and engineering  
Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed.  ...  The features of flow-based network traffic are extracted for behavior analysis with port-wise profiling based on the data baseline, and anomaly detection classification and prediction using machine learning  ...  Intrusion detection systems have proved to be a reliable security process for anomaly detection in traditional IT, which identifies all inbound and outbound network traffic for security breach and check  ... 
doi:10.32604/csse.2021.014384 fatcat:3o5l4rgvdngmpmonxmbb4d7foq

On the performance metrics for cyber-physical attack detection in smart grid

Sayawu Yakubu Diaba, Miadreza Shafie-khah, Mohammed Elmusrati
2022 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark.  ...  In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective  ...  Acknowledgements Sayawu Yakubu Diaba would like to thank the Evald and Hilda Nissi Foundation for awarding me scholarship.  ... 
doi:10.1007/s00500-022-06761-1 fatcat:gbukjb2rpfhtzakp6ih7rg3rha

Learning-Based Methods for Cyber Attacks Detection in IoT Systems: Methods, Analysis, and Future Prospects

Usman Inayat, Muhammad Fahad Zia, Sajid Mahmood, Haris M. Khalid, Mohamed Benbouzid
2022 Electronics  
For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems.  ...  The impacts of these intrusions could lead to physical and economical damages.  ...  Table 5 . 5 Unsupervised machine learning methods for cyber attack detection in IoT.  ... 
doi:10.3390/electronics11091502 fatcat:stweql4ru5behg2scpumznzy6i

Helping IT and OT Defenders Collaborate [article]

Glenn A. Fink, Penny McKenzie
2019 arXiv   pre-print
We present two problems in this paper: (1) the difficulty of coordinating detection and response between defenders who work on the cyber/IT and physical/OT sides of cyber-physical infrastructures, and  ...  However, cyber-physical systems present unique challenges to defenders, starting with an inability to communicate.  ...  Building systems data from VOLTTRON agents is made available to researchers for energy-related objectives and also for advanced cyber detection research in cyber-physical systems.  ... 
arXiv:1904.07374v1 fatcat:7fdbvrkw4jfqjizsqnwgnn72he
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