Intrusion Detection in Critical Infrastructures: A Literature Review

Fountas, Panagiotis; Taxiarxchis, Kouskouras; Kranas, Georgios; Μαγλαράς, Λέανδρος; Ferrag, Mohamed Amine

doi:10.3390/smartcities4030061

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…A growing body of literature in cyber-security for critical infrastructure [ 22 , 23 , 24 , 25 ] has already been devoted to detecting and preventing cyber-attack infiltration. Recently, Tuptuk et al [ 26 ] systematically reviewed and categorized state-of-the-art methodologies on cyber-security in water infrastructure that largely integrate smart technologies.…”

Section: Related Workmentioning

confidence: 99%

A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

Koutroulis

Mutlu

Kern

2022

Sensors

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Critical infrastructure, such as water treatment facilities, largely relies on the effective functioning of industrial control systems (ICSs). Due to the wide adoption of high-speed network and digital infrastructure technologies, these systems are now highly interconnected not only to corporate networks but also to the public Internet, mostly for remote control and monitoring purposes. Sophisticated cyber-attacks may take advantage the increased interconnectedness or other security gaps of an ICS and infiltrate the system with devastating consequences to the economy, national security, and even human life. Due to the paramount importance of detecting and isolating these attacks, we propose an unsupervised anomaly detection approach that employs causal inference to construct a robust anomaly score in two phases. First, minimal domain knowledge via causal models helps identify critical interdependencies in the system, while univariate models contribute to individually learn the normal behavior of the system’s components. In the final phase, we employ the extreme studentized deviate (ESD) on the computed score to detect attacks and to exclude any irrelevant sensor signals. Our approach is validated on the widely used Secure Water Treatment (SWaT) benchmark, and it exhibits the highest F1 score with zero false alarms, which is extremely important for real-world deployment.

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Section: Related Workmentioning

confidence: 99%

A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

Koutroulis

Mutlu

Kern

2022

Sensors

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“…This is in line with [26], who demonstrated in their research the relevance of the existing infrastructure. The infrastructure capacity also sets a limit in terms of service potential, potentially acting as a barrier to future project expansion and restricting chances in success [32]. The infrastructures of the logistics industry can be translated in the UCC and city hubs, the latter of which corresponds with Nordstan in the specific case of the SMOOTh project.…”

Section: Existing Infrastructurementioning

confidence: 99%

Scaling Up Smart City Logistics Projects: The Case of the Smooth Project

Sista

Giovanni

2021

Smart Cities

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A large number of smart city logistics projects fail to scale up, remaining a local experimental exercise. This lack of scalability is, in fact, commonly recognized as a major problem. This study aims to determine the key success factors related to the scalability of smart city logistics projects. The process of scaling up, which is articulated as expansion, roll-out, and replication, is defined as the ability of a system to improve its scale by aiming to meet the increasing volume demand. Specifically, this study investigates the scalability intended to be used as expansion and roll-out. A qualitative case study was conducted to fulfill the research purpose. The chosen case study is SMOOTh, a pilot project currently underway in the city of Gothenburg, Sweden, involving a diverse group of companies including Volvo Group and DHL. Semi-structured interviews were conducted with seven of the project’s stakeholders. Through a thematic analysis, four categories and the respective success factors were identified. These were represented by a business model, as well as technical, stakeholder and regulatory factors. The paper concludes with observations and recommendations aimed at the pilot initiatives, adding new perspectives to the upscaling debate.

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“…Therefore, vulnerabilities in smart cities need to be addressed with proper solution. The area of smart cities is quite diverse with having many applications which include e-government, smart homes, intelligent transportation, tele-medicines, smart grid, UAVs monitoring, energy and many more [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Detection of Cyber Attacks using Machine Learning ‎based Intrusion Detection System for IoT Based Smart ‎Cities

Chohan,

Haider,

Muhammad Yaseen Ayub

et al. 2023

EAI Endorsed Trans Smart Cities

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The world’s dynamics is evolving with artificial intelligence (AI) and the results are smart products. A smart city has smart city is collection of smart innovations powered with AI and internet of things (IoTs). Along with the ease and comfort that the concept of a smart city pointed at, many security concerns are being raised that hinders the path of its flourishment. An Intrusion Detection System (IDS) monitors the whole network traffic and alerts in case of any anomaly. A Machine Learning-based IDS intelligently senses the network threats, takes decisions about data packet legibility and alarm the user. Researchers have deployed various ML techniques to IDS to improve the detection accuracy. This work presents a comparative analysis of various ML algorithms trained over UNSW-NB15 dataset. ADA Boost, Linear Support Vector Machine (LSVM), Auto Encoder Classifier, ‎Quadratic Support Vector Machine (QSVM) and Multi-Layer Perceptron algorithms are being employed in the stimulation. ADA Boost showed an excellent accuracy of 98.3% in the results.

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Intrusion Detection in Critical Infrastructures: A Literature Review

Cited by 13 publications

References 34 publications

A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

Scaling Up Smart City Logistics Projects: The Case of the Smooth Project

Detection of Cyber Attacks using Machine Learning ‎based Intrusion Detection System for IoT Based Smart ‎Cities

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