D-IDS for Cyber-Physical DER Modbus System - Architecture, Modeling, Testbed-based Evaluation

Ravikumar, Gelli; Singh, Abhinav; Babu, Jeyanth Rajan; A, Abdelkhalek Moataz; Manimaran, G.

doi:10.1109/rws50334.2020.9241259

Cited by 16 publications

(5 citation statements)

References 17 publications

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“…Ravikumar et al [82] build on the work of Rakas by proposing a distributed IDS for federated CPS environments such as interconnected smart power grids. The proposed distributed IDS uses Ethernet switch port mirroring to capture network traffic flow data and consolidate that data in a centralized or cloud-based IDS environment for enhanced situational awareness of activity in a distributed and/or loosely coupled CPS.…”

Section: Using Ids/ips For Anomaly Detectionmentioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges.

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Section: Using Ids/ips For Anomaly Detectionmentioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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“…Here, the considerable power quality is achieved by using the reconfigurable emulators. In [23][24] authors developed various types of real time controllers such as distributed-IDS, IGBT based controlling system, and MCU-MIPS based environment. In [25], the authors developed distributed power generation system by maintaining the economic operation with respect to both the input and profit; in [26], hybrid Gaussian mutation with PSO was used to get the maximum profit in microgrids, respectively.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

Power Quality Improvement using Dual Multilevel Converter for Micro Grid-Connected PV Energy Systems using ANFIS

Rupa

Manohar²,

Manjula

2023

IJEER

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This paper presents the implementation of dual voltage source inverter (DVSI) approach to improve the microgrid performance by enhancing the power quality. This paper also improves the power quality in photovoltaic (PV) generation interactive microgrids, respectively. The power generated from PV based distributive energy resources (DER) is perfectly applied to the microgrid through the two inverters, thus the nonlinear and unbalance load related problems are compensated. Thus, the power quality problems such as voltage sag, current drops, and power factor, active, and reactive powers are reduced by dual multilevel converter (DMLC). This converter also used for cooperative controlling to minimize the unbalances and voltage harmonics in microgrid by using adaptive neuro fuzzy interface system (ANFIS) based controller, which is applied in the shunt and series manner between the two VSIs. The total load current is mutually shared among the series and shunt VSI, if one VSI fails, then the other VSI continues its operation. Thus, the DMLC components failure rate is reduced, and the system lost energy is also reduced, which leads to improved reliability by maintain the reduction of down time price at environmental free conditions. The proposed control model is implemented in MATLAB/Simulink environment, and the obtained results shows that the superiority of total harmonic distortion (THD) reduction by the proposed ANFIS controller as compared to the conventional artificial neural network (ANN) and fuzzy logic controllers (FLC) by eliminating all the load current and grid voltage-based harmonics.

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“…Most IDSs classify attacks by analyzing network traffic generated from specialized environments [ 50 , 51 , 52 , 53 , 54 , 55 ]. Nevertheless, in reality, network traffic may originate from a broad range of traffic and include excessive data.…”

Section: Related Workmentioning

confidence: 99%

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Liu

Thapa

Shaver

et al. 2021

Sensors

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As Internet of Things (IoT) networks expand globally with an annual increase of active devices, providing better safeguards to threats is becoming more prominent. An intrusion detection system (IDS) is the most viable solution that mitigates the threats of cyberattacks. Given the many constraints of the ever-changing network environment of IoT devices, an effective yet lightweight IDS is required to detect cyber anomalies and categorize various cyberattacks. Additionally, most publicly available datasets used for research do not reflect the recent network behaviors, nor are they made from IoT networks. To address these issues, in this paper, we have the following contributions: (1) we create a dataset from IoT networks, namely, the Center for Cyber Defense (CCD) IoT Network Intrusion Dataset V1 (CCD-INID-V1); (2) we propose a hybrid lightweight form of IDS—an embedded model (EM) for feature selection and a convolutional neural network (CNN) for attack detection and classification. The proposed method has two models: (a) RCNN: Random Forest (RF) is combined with CNN and (b) XCNN: eXtreme Gradient Boosting (XGBoost) is combined with CNN. RF and XGBoost are the embedded models to reduce less impactful features. (3) We attempt anomaly (binary) classifications and attack-based (multiclass) classifications on CCD-INID-V1 and two other IoT datasets, the detection_of_IoT_botnet_attacks_N_BaIoT dataset (Balot) and the CIRA-CIC-DoHBrw-2020 dataset (DoH20), to explore the effectiveness of these learning-based security models. Using RCNN, we achieved an Area under the Receiver Characteristic Operator (ROC) Curve (AUC) score of 0.956 with a runtime of 32.28 s on CCD-INID-V1, 0.999 with a runtime of 71.46 s on Balot, and 0.986 with a runtime of 35.45 s on DoH20. Using XCNN, we achieved an AUC score of 0.998 with a runtime of 51.38 s for CCD-INID-V1, 0.999 with a runtime of 72.12 s for Balot, and 0.999 with a runtime of 72.91 s for DoH20. Compared to KNN, XCNN required 86.98% less computational time, and RCNN required 91.74% less computational time to achieve equal or better accurate anomaly detections. We find XCNN and RCNN are consistently efficient and handle scalability well; in particular, 1000 times faster than KNN when dealing with a relatively larger dataset-Balot. Finally, we highlight RCNN and XCNN’s ability to accurately detect anomalies with a significant reduction in computational time. This advantage grants flexibility for the IDS placement strategy. Our IDS can be placed at a central server as well as resource-constrained edge devices. Our lightweight IDS requires low train time and hence decreases reaction time to zero-day attacks.

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D-IDS for Cyber-Physical DER Modbus System - Architecture, Modeling, Testbed-based Evaluation

Cited by 16 publications

References 17 publications

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Power Quality Improvement using Dual Multilevel Converter for Micro Grid-Connected PV Energy Systems using ANFIS

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

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