An Integrated Framework for Privacy-Preserving Based Anomaly Detection for Cyber-Physical Systems

Keshk, Marwa; Sitnikova, Elena; Moustafa, Nour; Hu, Jiankun; Khalil, Ibrahim

doi:10.1109/tsusc.2019.2906657

Cited by 113 publications

(71 citation statements)

References 36 publications

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“…To validate the efficiency of the proposed methodology, we have compared GBFS based decision tree algorithm with four published methods, namely AdaBoost-JRIP (AdaJRIP) [25], Common Path Mining [34], [28], Expectation Maximization Clustering Technique (EMCT) [32] and Gaussian Mixture -Kalam Filter Model (GMM-KF) using Pearson Correlation Coefficient (PCC) feature selection method [35], by considering various performance evaluation factors such as whether proper pre-processing is applied or not; to accelerate the process, whether feature selection approach is incorporated or not and if applied how many features are selected to evaluate the accuracy for various output classes.…”

Section: Results Discussionmentioning

confidence: 99%

Gradient Boosting Feature Selection With Machine Learning Classifiers for Intrusion Detection on Power Grids

Upadhyay

Manero

Zaman

et al. 2021

IEEE Trans. Netw. Serv. Manage.

110

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Section: Results Discussionmentioning

confidence: 99%

Gradient Boosting Feature Selection With Machine Learning Classifiers for Intrusion Detection on Power Grids

Upadhyay

Manero

Zaman

et al. 2021

IEEE Trans. Netw. Serv. Manage.

110

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“…Specification-based techniques are applied for specific protocols, standalone or in combination with other methods [45], [46], [51]. More comprehensive solutions use a number of integrated techniques to detect a variety of attacks more accurately [43], [46], [48], [52], [53], [61].…”

Section: A Detection Methodology Protected Protocols and Implementamentioning

confidence: 99%

“…Keshk et al propose a privacy-preserving anomaly detection framework for protecting confidential information and discovering malicious activities in power systems networks [61]. To preserve privacy, the data pre-processing module filters and transforms original data into a new format using Pearson correlation coefficient technique.…”

Section: F Hybrid Techniquesmentioning

confidence: 99%

“…Among the results concerning packet as a unit of analysis, deep packet inspection applied in [51] outperforms other techniques [41], [43], [46] at least for an order of magnitude. In the cases where dataset instance is observed as a unit of analysis, deep learning method presented in [63] performs much worse than clustering and outlier detection [60] and hybrid method presented in [61]. This is not surprising since deep learning inherently requires large datasets to obtain high accuracy.…”

Section: ) Timelinessmentioning

confidence: 99%

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A Review of Research Work on Network-Based SCADA Intrusion Detection Systems

2020

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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 features and specific application layer protocols. This article aims to contribute to assess the state-ofthe-art, identify the open issues and provide an insight for future study areas. 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. We propose a structured evaluation methodology that encompasses detection techniques, protected protocols, implementation tools, test environments and IDS performance. Special attention is focused on assessing implementation maturity as well as the applicability of each surveyed solution in the Future Internet environment. Based on that, we provide a brief description and evaluation of 26 selected research papers, published in the period 2015-2019. Results of our analysis indicate considerable progress regarding the development of machine learning-based detection methods, implementation platforms, and to some extent, sophisticated testbeds. We also identify research gaps and conclude the analysis with a list of the most important directions for further research.

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“…Passive Attacks are characterized by a lack of any alteration to information or its flow, thereby only compromising the confidentiality and privacy of communications. In some cases, a passive attack can enable location tracking of IoT devices [52][53][54]. Active Attacks involve active alteration and modification of information and its flow, but are not limited to device settings, control messages and software components.…”

Section: Iot Environmentmentioning

confidence: 99%

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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An Integrated Framework for Privacy-Preserving Based Anomaly Detection for Cyber-Physical Systems

Cited by 113 publications

References 36 publications

Gradient Boosting Feature Selection With Machine Learning Classifiers for Intrusion Detection on Power Grids

Gradient Boosting Feature Selection With Machine Learning Classifiers for Intrusion Detection on Power Grids

A Review of Research Work on Network-Based SCADA Intrusion Detection Systems

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

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