A triple layer intrusion detection system for SCADA security of electric utility

Samdarshi, Rishabh; Sinha, Nidul; Tripathi, Paritosh

doi:10.1109/indicon.2015.7443439

Cited by 15 publications

(6 citation statements)

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“…A typical SCADA network collects data from the field devices (sensors, actuators, etc. ), which directly engage with the CIs physical equipment such as pumps and valves [29]. The RTUs, PLCs, IEDs assist in retrieving real-time data from the field devices, which control and monitor the actions of the CIs' process [30].…”

Section: Brief Overview Of Modern Scada Architecturementioning

confidence: 99%

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

et al. 2021

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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, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

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Section: Brief Overview Of Modern Scada Architecturementioning

confidence: 99%

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

et al. 2021

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“…Supervised learning algorithms use labeled training data to formulate detection models, e.g., set of rules [101], separation plane [75], decision trees [39], neural network [61]. Later on, these mod-els are used to detect anomalies.…”

Section: Supervised Learning Techniquesmentioning

confidence: 99%

“…It formulates a tree-like data structure from a set of attributes of each training tuple. Using a decision tree a rule set is easily derived, which can be used to classify the input into a particular group [79,90,101]. There are several algorithms in the group, such as Classification and Regression Tree (CART), Iterative Dichotomiser (ID3), C4.5 (an improved ID3), Frequent Pattern (FP) Growth, Supervised Learning in Quest (SLIQ), SPRINT, and Random Forest (RF).…”

Section: Taxonomy Of Supervised Learning Approachesmentioning

confidence: 99%

“…Other Methods. The remaining techniques proposed in the literature are across various categories of algorithms, such as divide-and-conquer (i.e., [79], [90], and [101]) and boundary methods (e.g., SVM [75,92,93,122] and Field Boundary [74]). However, lazy learning (i.e., [108] and [114]) and density-based (i.e., EM [135]) are occasionally used for supervised SCADA IDS.…”

Section: Categories Of Scada-based Ids Systemsmentioning

confidence: 99%

“…According to Table 2 column feasibility, only four (4) out of fifty (50) approaches have deployed in the field [9,74,106,133]. Twelve (12) papers are tested on SCADA-specific testbed or implemented as a testing prototype, whereas the rest are evaluated using various machine learning framework (e.g., MATLAB [100], WEKA [101], ACCORD [111]) or a generic network simulator (e.g., hardware in the loop testbed [90]). Table 4 lists available testbeds that were used by the literature surveyed in this review including both physical and simulated testbed.…”

Section: The Feasibility Of the Proposed Workmentioning

confidence: 99%

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A Taxonomy of Supervised Learning for IDSs in SCADA Environments

Suaboot

Fahad²,

Tari

et al. 2020

ACM Comput. Surv.

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Supervisory Control and Data Acquisition (SCADA) systems play an important role in monitoring industrial processes such as electric power distribution, transport systems, water distribution, and wastewater collection systems. Such systems require a particular attention with regards to security aspects, as they deal with critical infrastructures that are crucial to organizations and countries. Protecting SCADA systems from intrusion is a very challenging task because they do not only inherit traditional IT security threats but they also include additional vulnerabilities related to field components (e.g., cyber-physical attacks). Many of the existing intrusion detection techniques rely on supervised learning that consists of algorithms that are first trained with reference inputs to learn specific information, and then tested on unseen inputs for classification purposes. This article surveys supervised learning from a specific security angle, namely SCADA-based intrusion detection. Based on a systematic review process, existing literature is categorized and evaluated according to SCADA-specific requirements. Additionally, this survey reports on well-known SCADA datasets and testbeds used with machine learning methods. Finally, we present key challenges and our recommendations for using specific supervised methods for SCADA systems.

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Survey on Building Block Technologies

2023

Data Exfiltration Threats and Prevention Techniques

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A triple layer intrusion detection system for SCADA security of electric utility

Cited by 15 publications

References 6 publications

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

A Taxonomy of Supervised Learning for IDSs in SCADA Environments

Survey on Building Block Technologies

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