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A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems
2021
Cluster Computing
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In this paper, a stacked deep learning method is introduced to identify malicious attacks targeting SCADA systems. ...
Thus, accurately detecting cyber-attacks in critical SCADA systems is undoubtedly indispensable to enhance their resilience, ensure safe operations, and avoid costly maintenance. ...
This paper introduces a stacked deep learning-driven anomaly detection technique to detect and identify cyberattacks in SCADA systems. ...
doi:10.1007/s10586-021-03426-w
pmid:34629940
pmcid:PMC8490144
fatcat:wgvodkfndbc2xbb5dr2y3wtgam
A new perspective towards the development of robust data-driven intrusion detection for industrial control systems
2020
Nuclear Engineering and Technology
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Recent developments in industrial data acquisition systems have spurred a renewed interest in data-driven approaches to curb the rise of industrial control system cyber attacks. ...
Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems. ...
Common techniques for developing data-driven IDS Most of the available security solutions and protective techniques against cyber-attacks in critical systems have focused on extending traditional IDS networking ...
doi:10.1016/j.net.2020.05.012
fatcat:3wptel7b3ngb5lfh352ycm3ysu
A Review of Research Work on Network-Based SCADA Intrusion Detection Systems
2020
IEEE Access
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Specific intrusion detection systems (IDSs) are needed to secure modern supervisory control and data acquisition (SCADA) systems due to their architecture, stringent real-time requirements, network traffic ...
To achieve these objectives, we start from the factors that impact the design of dedicated intrusion detection systems in SCADA networks and focus on network-based IDS solutions. ...
The system works in two phases: (1) a data-driven clustering technique of process parameters identifies normal and critical states of the target system and activates a criticality scoring mechanism and ...
doi:10.1109/access.2020.2994961
fatcat:pid6aq5t7be7hkf4moaym2wqti
An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems
2015
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
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Anomaly detection is an important aspect of data mining, where the main objective is to identify anomalous or unusual data from a given dataset. ...
In this paper, we categorise anomaly detection techniques based on nearest neighbours, clustering and statistical approaches and investigate the performance analysis of these techniques in critical infrastructure ...
However, for SCADA systems, we are the pioneer to investigate the anomaly detection techniques in big data perspective. ...
doi:10.4108/inis.2.3.e5
fatcat:pcaj3u7ogfg4bc6rlow5bfzize
Architecture and Security of SCADA Systems: A Review
[article]
2020
arXiv
pre-print
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A short investigation of the current state of intrusion detection techniques in SCADA systems is done , followed by a brief study of testbeds for SCADA systems. ...
In this paper, we first review the SCADA system architectures that have been proposed/implemented followed by attacks on such systems to understand and highlight the evolving security needs for SCADA systems ...
Therefore, there is a need to build a viable and efficient system architectures and frameworks to model such issues. ...
arXiv:2001.02925v1
fatcat:c77yzusz3nge5p352fwgoqimx4
Intrusion detection in SCADA systems using machine learning techniques
2014
2014 Science and Information Conference
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Modern intrusion detection systems that can cope with zero days attacks in an efficient and accurate way rely on artificial intelligence, machine learning and data mining techniques. ...
Hence, for network reliability it is necessary to develop an efficient technique to detect misbehaving clients in a timely manner and the correlation coefficients between entities can effectively detect ...
Appendix A
Related Publications The main ideas presented in this thesis appear in the following publications: ...
doi:10.1109/sai.2014.6918252
fatcat:4ueepuwxezehxknf44kv4q23o4
A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification
2021
Sustainability
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Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). ...
Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems ...
The data driven methods have better computing capacity to handle voluminous SCADA datasets, with huge number of features and variables. ...
doi:10.3390/su13179597
fatcat:cpvhccw6brahzadsgw75dgw3ai
Autoencoder Based Anomaly Detection for SCADA Networks
2021
International Journal of Artificial Intelligence and Machine Learning
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Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. ...
results compared to other techniques for SCADA gas pipeline dataset. ...
Attack data is not available in sufficient quantities to train supervised machine learning techniques, compared to normal operation data for SCADA systems. ...
doi:10.4018/ijaiml.20210701.oa6
fatcat:rht2v4jn3zdnnoiyx457qie3qq
Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems
2023
Energies
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A summary of the requirements that CPSs need to satisfy their operation is highlighted, and an analysis of the benefits and drawbacks of model-based and data-driven techniques is carried out. ...
Cyberattack detection and mitigation methods in CPSs involve the use of various techniques such as intrusion detection systems (IDSs), firewalls, access control mechanisms, and encryption. ...
To detect a false data injection attack (FDIA) in a power system CPS, a novel attack detection approach was reported in [51] . This used integrated model-based and data-driven methods. ...
doi:10.3390/en16135206
fatcat:lm6l4mdyijfmdecyqf77dyllvi
Energy theft in smart grids: A survey on data-driven attack strategies and detection methods
2021
IEEE Access
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Lastly, we discuss various open issues in the scope of data-driven energy theft detection methods and provide future directions to carry out research in this field. ...
Different attack detection models for theft detection in the smart grid are categorized. ...
To present the overall overview of such actors and their energy theft activities, we conduct a through study of data-driven energy theft attack and detection techniques in this paper for smart grid systems ...
doi:10.1109/access.2021.3131220
fatcat:3auj524rxvhyflltul4qr3hnoq
Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data
2020
Electronics
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Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. ...
The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection ...
data-driven clustering technique (SDAD) in [25] . ...
doi:10.3390/electronics9061017
fatcat:fkbzqwp3y5addojdpya4grtjz4
Author index
2013
38th Annual IEEE Conference on Local Computer Networks
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Interest Flooding DDoS Attacks in Named Data Networking Turgut, Damla EE-MAC: Energy Efficient Sensor MAC Layer Protocol Uluagac, Selcuk PROVIZ: An Integrated Visualization and Programming Framework for ...
Status Monitoring in Sensor Networks Using Adaptive Piggybacking
Faster Distributed Localization of Large Numbers of Nodes Using Clustering
SmartRevoc: An Efficient and Privacy Preserving Revocation ...
doi:10.1109/lcn.2013.6761235
fatcat:lcoo2auxgjeirmddevnhp5henq
A survey of intrusion detection techniques for cyber-physical systems
2014
ACM Computing Surveys
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Our approach is to classify modern CPS Intrusion Detection System (IDS) techniques based on two design dimensions: detection technique and audit material. ...
In order to identify gaps and propose research directions in CPS intrusion detection research, we survey the literature of this area. ...
Shin et al. [2010] present an extension of an existing WSN technique using one hop clustering for SCADA applications; in a one hop cluster, every member falls within radio range of the cluster head. ...
doi:10.1145/2542049
fatcat:nadrawgh6fhpzotbpypfcl32kq
A Survey of Security in SCADA Networks: Current Issues and Future Challenges
2019
IEEE Access
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Supervisory Control and Data Acquisition (SCADA) systems are used for monitoring industrial devices. ...
The primary objective of this survey paper is to provide a comparative study of the on-going security research in SCADA systems. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable feedback and comments, which have substantially improved the quality of the paper. ...
doi:10.1109/access.2019.2926441
fatcat:nrn3z727azhi5dmpr2ub6tbqum
Anomaly detection in industrial control systems using evolutionary-based optimization of neural networks
2017
Communications on Advanced Computational Science with Applications
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Finally, by using an evolutionary based optimization for training a neural network, a new algorithm for prediction of anomalies in the SCADA system with high accuracy is proposed. ...
Since modelling a large amount of unlabeled data is costly and time-consuming, the automated machine learning methods have the ability to detect anomalies in industrial control systems effectively. ...
[3] a data-driven clustering approach has been suggested which eliminates the need for experts in the field of SCADA and merely ordinary data for creating of detection models. ...
doi:10.5899/2017/cacsa-00074
fatcat:kxl7hg3d65e3hjnfhxyepvm26m
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