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2021-12-20
NING, Baifeng, Xiao, Liang.  2021.  Defense Against Advanced Persistent Threats in Smart Grids: A Reinforcement Learning Approach. 2021 40th Chinese Control Conference (CCC). :8598–8603.
In smart girds, supervisory control and data acquisition (SCADA) systems have to protect data from advanced persistent threats (APTs), which exploit vulnerabilities of the power infrastructures to launch stealthy and targeted attacks. In this paper, we propose a reinforcement learning-based APT defense scheme for the control center to choose the detection interval and the number of Central Processing Units (CPUs) allocated to the data concentrators based on the data priority, the size of the collected meter data, the history detection delay, the previous number of allocated CPUs, and the size of the labeled compromised meter data without the knowledge of the attack interval and attack CPU allocation model. The proposed scheme combines deep learning and policy-gradient based actor-critic algorithm to accelerate the optimization speed at the control center, where an actor network uses the softmax distribution to choose the APT defense policy and the critic network updates the actor network weights to improve the computational performance. The advantage function is applied to reduce the variance of the policy gradient. Simulation results show that our proposed scheme has a performance gain over the benchmarks in terms of the detection delay, data protection level, and utility.
2021-12-02
Martovytskyi, Vitalii, Ruban, Igor, Lahutin, Hennadiy, Ilina, Irina, Rykun, Volodymyr, Diachenko, Vladyslav.  2020.  Method of Detecting FDI Attacks on Smart Grid. 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S T). :132–136.
Nowadays energy systems in many countries improve and develop being based on the concept of deep integration of energy as well as infocomm grids. Thus, energy grids find the possibility to analyze the state of the whole system in real time, to predict the processes in it, to have interactive cooperation with the clients and to run the appliance. Such concept has been named Smart Grid. This work highlights the concept of Smart Grid, possible vectors of attacks and identification of attack of false data injection (FDI) into the flow of measuring received from the sensors. Identification is based on the use of spatial and temporal correlations in Smart Grids.
Anwar, Adnan, Abir, S. M. Abu Adnan.  2020.  Measurement Unit Placement Against Injection Attacks for the Secured Operation of an IIoT-Based Smart Grid. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :767–774.
Carefully constructed cyber-attacks directly influence the data integrity and the operational functionality of the smart energy grid. In this paper, we have explored the data integrity attack behaviour in a wide-area sensor-enabled IIoT-SCADA system. We have demonstrated that an intelligent cyber-attacker can inject false information through the sensor devices that may remain stealthy in the traditional detection module and corrupt estimated system states at the utility control centres. Next, to protect the operation, we defined a set of critical measurements that need to be protected for the resilient operation of the grid. Finally, we placed the measurement units using an optimal allocation strategy by ensuring that a limited number of nodes are protected against the attack while the system observability is satisfied. Under such scenarios, a wide range of experiments has been conducted to evaluate the performance considering IEEE 14-bus, 24 bus-reliability test system, 85-bus, 141-bus and 145-bus test systems. Results show that by ensuring the protection of around 25% of the total nodes, the IIoT-SCADA enabled energy grid can be protected against injection attacks while observability of the network is well-maintained.
Gai, Na, Xue, Kaiping, He, Peixuan, Zhu, Bin, Liu, Jianqing, He, Debiao.  2020.  An Efficient Data Aggregation Scheme with Local Differential Privacy in Smart Grid. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :73–80.
Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.
Gupta, Praveen Kumar, Singh, Neeraj Kumar, Mahajan, Vasundhara.  2020.  Monitoring of Cyber Intrusion in Wireless Smart Grid Network Using Weight Reduction Technique. 2020 International Conference on Electrical and Electronics Engineering (ICE3). :136–139.
The dependency of the smart grid is higher in terms of Wireless Sensors (WS) for flexible monitoring and control. Sensor nodes are required to sense, collect and process the real-time data and transfer it to the monitoring stations. Mostly, it is deployed in extremely rural areas where human access is limited making it vulnerable to cyber intrusion. In this paper, an easy, efficient and low memory usage program is proposed to detect False Data Injection Cyber Attack (FDICA) in very little time to protect the smart grid network. Each bus of the IEEE test system is represented by a connected graph node having a weight equal to 1. During FDICA the weight of the node changes and triggers the alarm if the change is below the predefined threshold value. MATLAB software is used to evaluate the performance of the proposed method under different conditions. Simulation results indicate that the proposed method detects the FDICA in minimal time increasing the resilience capability of the smart grid.
2021-11-30
Akhras, Raphaelle, El-Hajj, Wassim, Majdalani, Michel, Hajj, Hazem, Jabr, Rabih, Shaban, Khaled.  2020.  Securing Smart Grid Communication Using Ethereum Smart Contracts. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1672–1678.
Smart grids are being continually adopted as a replacement of the traditional power grid systems to ensure safe, efficient, and cost-effective power distribution. The smart grid is a heterogeneous communication network made up of various devices such as smart meters, automation, and emerging technologies interacting with each other. As a result, the smart grid inherits most of the security vulnerabilities of cyber systems, putting the smart grid at risk of cyber-attacks. To secure the communication between smart grid entities, namely the smart meters and the utility, we propose in this paper a communication infrastructure built on top of a blockchain network, specifically Ethereum. All two-way communication between the smart meters and the utility is assumed to be transactions governed by smart contracts. Smart contracts are designed in such a way to ensure that each smart meter is authentic and each smart meter reading is reported securely and privately. We present a simulation of a sample smart grid and report all the costs incurred from building such a grid. The simulations illustrate the feasibility and security of the proposed architecture. They also point to weaknesses that must be addressed, such as scalability and cost.
Hu, Xiaoming, Tan, Wenan, Ma, Chuang.  2020.  Comment and Improvement on Two Aggregate Signature Schemes for Smart Grid and VANET in the Learning of Network Security. 2020 International Conference on Information Science and Education (ICISE-IE). :338–341.
Smart substation and Vehicular Ad-Hoc Network (VANET) are two important applications of aggregate signature scheme. Due to the large number of data collection equipment in substation, it needs security authentication and integrity protection to transmit data. Similarly, in VANET, due to limited resources, it has the needs of privacy protection and improving computing efficiency. Aggregate signature scheme can satisfy the above these needs and realize one-time verification of signature for multi-terminal data collection which can improve the performance. Aggregate signature scheme is an important technology to solve network security problem. Recently, many aggregate signature schemes are proposed which can be applied in smart grid or VANET. In this paper, we present two security analyses on two aggregate signature schemes proposed recently. By analysis, it shows that the two aggregate signature schemes do not satisfy the security property of unforgeability. A malicious user can forge a signature on any message. We also present some improved methods to solve these security problems with better performance. From security analysis to improvement of aggregate signature scheme, it is very suitable to be an instance to exhibit the students on designing of security aggregate signature scheme for network security education or course.
Dobrea, Marius-Alexandru, Vasluianu, Mihaela, Neculoiu, Giorgian, Bichiu, Stefan.  2020.  Data Security in Smart Grid. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). :1–6.
Looking at the Smart Grid as a Cyber - Physical system of great complexity, the paper synthesizes the main IT security issues that may arise. Security issues are seen from a hybrid point of view, combining theory of information with system theory. Smart Grid has changed dramatically over the past years. With modern technologies, such as Big Data or Internet of Things (IoT), the Smart Grid is evolving into a more interconnected and dynamic power network model.
Pliatsios, Dimitrios, Sarigiannidis, Panagiotis, Efstathopoulos, Georgios, Sarigiannidis, Antonios, Tsiakalos, Apostolos.  2020.  Trust Management in Smart Grid: A Markov Trust Model. 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST). :1–4.
By leveraging the advancements in Information and Communication Technologies (ICT), Smart Grid (SG) aims to modernize the traditional electric power grid towards efficient distribution and reliable management of energy in the electrical domain. The SG Advanced Metering Infrastructure (AMI) contains numerous smart meters, which are deployed throughout the distribution grid. However, these smart meters are susceptible to cyberthreats that aim to disrupt the normal operation of the SG. Cyberattacks can have various consequences in the smart grid, such as incorrect customer billing or equipment destruction. Therefore, these devices should operate on a trusted basis in order to ensure the availability, confidentiality, and integrity of the metering data. In this paper, we propose a Markov chain trust model that determines the Trust Value (TV) for each AMI device based on its behavior. Finally, numerical computations were carried out in order to investigate the reaction of the proposed model to the behavior changes of a device.
Duan, Junhong, Zhao, Bo, Guo, Sensen.  2020.  The Design and Implementation of Smart Grid SOC Platform. 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA). 1:264–268.
Smart grid is the key infrastructure of the country, and its network security is an important link to ensure the national important infrastructure security. SOC as a secure operation mechanism for adaptive and continuous improvement of information security, it is practically significant to address the challenge to the network security of the smart grid. Based on the analysis of the technical characteristics and security of smart grid, and taking a grid enterprise smart grid as an example, we propose the design scheme and implementation plan of smart grid SOC platform. Experimental results show that the platform we designed can meet the performance requirements, it also meets the requirements of real-time storage of behavioral data and provides support for interactive analysis and batch analysis.
Marah, Rim, Gabassi, Inssaf El, Larioui, Sanae, Yatimi, Hanane.  2020.  Security of Smart Grid Management of Smart Meter Protection. 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). :1–5.
The need of more secured and environmental energy is becoming a necessity and priority in an environment suffering from serious problems due to technological development. Since the Smart Grid is a promising alternative that supports green energy and enhances a better management of electricity, the security side has became one of the major and critical associated issues in building the communication network in the microgrid.In this paper we will present the Smart Grid Cyber security challenges and propose a distributed algorithm that face one of the biggest problems threatening the smart grid which is fires.
Kserawi, Fawaz, Malluhi, Qutaibah M..  2020.  Privacy Preservation of Aggregated Data Using Virtual Battery in the Smart Grid. 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys). :106–111.
Smart Meters (SM) are IoT end devices used to collect user utility consumption with limited processing power on the edge of the smart grid (SG). While SMs have great applications in providing data analysis to the utility provider and consumers, private user information can be inferred from SMs readings. For preserving user privacy, a number of methods were developed that use perturbation by adding noise to alter user load and hide consumer data. Most methods limit the amount of perturbation noise using differential privacy to preserve the benefits of data analysis. However, additive noise perturbation may have an undesirable effect on billing. Additionally, users may desire to select complete privacy without giving consent to having their data analyzed. We present a virtual battery model that uses perturbation with additive noise obtained from a virtual chargeable battery. The level of noise can be set to make user data differentially private preserving statistics or break differential privacy discarding the benefits of data analysis for more privacy. Our model uses fog aggregation with authentication and encryption that employs lightweight cryptographic primitives. We use Diffie-Hellman key exchange for symmetrical encryption of transferred data and a two-way challenge-response method for authentication.
2021-10-12
Sethi, Kamalakanta, Pradhan, Ankit, Bera, Padmalochan.  2020.  Attribute-Based Data Security with Obfuscated Access Policy for Smart Grid Applications. 2020 International Conference on COMmunication Systems NETworkS (COMSNETS). :503–506.
Smart grid employs intelligent transmission and distribution networks for effective and reliable delivery of electricity. It uses fine-grained electrical measurements to attain optimized reliability and stability by sharing these measurements among different entities of energy management systems of the grid. There are many stakeholders like users, phasor measurement units (PMU), and other entities, with changing requirements involved in the sharing of the data. Therefore, data security plays a vital role in the correct functioning of a power grid network. In this paper, we propose an attribute-based encryption (ABE) for secure data sharing in Smart Grid architectures as ABE enables efficient and secure access control. Also, the access policy is obfuscated to preserve privacy. We use Linear Secret Sharing (LSS) Scheme for supporting any monotone access structures, thereby enhancing the expressiveness of access policies. Finally, we also analyze the security, access policy privacy and collusion resistance properties along with efficiency analysis of our cryptosystem.
Ackley, Darryl, Yang, Hengzhao.  2020.  Exploration of Smart Grid Device Cybersecurity Vulnerability Using Shodan. 2020 IEEE Power Energy Society General Meeting (PESGM). :1–5.
The generation, transmission, distribution, and storage of electric power is becoming increasingly decentralized. Advances in Distributed Energy Resources (DERs) are rapidly changing the nature of the power grid. Moreover, the accommodation of these new technologies by the legacy grid requires that an increasing number of devices be Internet connected so as to allow for sensor and actuator information to be collected, transmitted, and processed. With the wide adoption of the Internet of Things (IoT), the cybersecurity vulnerabilities of smart grid devices that can potentially affect the stability, reliability, and resilience of the power grid need to be carefully examined and addressed. This is especially true in situations in which smart grid devices are deployed with default configurations or without reasonable protections against malicious activities. While much work has been done to characterize the vulnerabilities associated with Supervisory Control and Data Acquisition (SCADA) and Industrial Control System (ICS) devices, this paper demonstrates that similar vulnerabilities associated with the newer class of IoT smart grid devices are becoming a concern. Specifically, this paper first performs an evaluation of such devices using the Shodan platform and text processing techniques to analyze a potential vulnerability involving the lack of password protection. This work further explores several Shodan search terms that can be used to identify additional smart grid components that can be evaluated in terms of cybersecurity vulnerabilities. Finally, this paper presents recommendations for the more secure deployment of such smart grid devices.
2021-09-21
Zhe, Wang, Wei, Cheng, Chunlin, Li.  2020.  DoS attack detection model of smart grid based on machine learning method. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :735–738.
In recent years, smart grid has gradually become the common development trend of the world's power industry, and its security issues are increasingly valued by researchers. Smart grids have applied technologies such as physical control, data encryption, and authentication to improve their security, but there is still a lack of timely and effective detection methods to prevent the grid from being threatened by malicious intrusions. Aiming at this problem, a model based on machine learning to detect smart grid DoS attacks has been proposed. The model first collects network data, secondly selects features and uses PCA for data dimensionality reduction, and finally uses SVM algorithm for abnormality detection. By testing the SVM, Decision Tree and Naive Bayesian Network classification algorithms on the KDD99 dataset, it is found that the SVM model works best.
2021-08-11
Ferrag, Mohamed Amine, Maglaras, Leandros.  2020.  DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids. IEEE Transactions on Engineering Management. 67:1285–1297.
In this paper, we propose a novel deep learning and blockchain-based energy framework for smart grids, entitled DeepCoin. The DeepCoin framework uses two schemes, a blockchain-based scheme and a deep learning-based scheme. The blockchain-based scheme consists of five phases: setup phase, agreement phase, creating a block phase and consensus-making phase, and view change phase. It incorporates a novel reliable peer-to-peer energy system that is based on the practical Byzantine fault tolerance algorithm and it achieves high throughput. In order to prevent smart grid attacks, the proposed framework makes the generation of blocks using short signatures and hash functions. The proposed deep learning-based scheme is an intrusion detection system (IDS), which employs recurrent neural networks for detecting network attacks and fraudulent transactions in the blockchain-based energy network. We study the performance of the proposed IDS on three different sources the CICIDS2017 dataset, a power system dataset, and a web robot (Bot)-Internet of Things (IoT) dataset.
2021-08-02
Pedramnia, Kiyana, Shojaei, Shayan.  2020.  Detection of False Data Injection Attack in Smart Grid Using Decomposed Nearest Neighbor Techniques. 2020 10th Smart Grid Conference (SGC). :1—6.
Smart grid communication system deeply rely on information technologies which makes it vulnerable to variable cyber-attacks. Among possible attacks, False Data Injection (FDI) Attack has created a severe threat to smart grid control system. Attackers can manipulate smart grid measurements such as collected data of phasor measurement units (PMU) by implementing FDI attacks. Detection of FDI attacks with a simple and effective approach, makes the system more reliable and prevents network outages. In this paper we propose a Decomposed Nearest Neighbor algorithm to detect FDI attacks. This algorithm improves traditional k-Nearest Neighbor by using metric learning. Also it learns the local-optima free distance metric by solving a convex optimization problem which makes it more accurate in decision making. We test the proposed method on PMU dataset and compare the results with other beneficial machine learning algorithms for FDI attack detection. Results demonstrate the effectiveness of the proposed approach.
2021-06-30
Liu, Donglan, Wang, Rui, Zhang, Hao, Ma, Lei, Liu, Xin, Huang, Hua, Chang, Yingxian.  2020.  Research on Data Security Protection Method Based on Big Data Technology. 2020 12th International Conference on Communication Software and Networks (ICCSN). :79—83.
The construction of power Internet of things is an important development direction of power grid enterprises in the future. Big data not only brings economic and social benefits to the power system industry, but also brings many information security problems. Therefore, in the case of accelerating the construction of ubiquitous electric Internet of things, it is urgent to standardize the data security protection in the ubiquitous electric Internet of things environment. By analyzing the characteristics of big data in power system, this paper discusses the security risks faced by big data in power system. Finally, we propose some methods of data security protection based on the defects of big data security in current power system. By building a data security intelligent management and control platform, it can automatically discover and identify the types and levels of data assets, and build a classification and grading information base of dynamic data assets. And through the detection and identification of data labels and data content characteristics, tracking the use of data flow process. So as to realize the monitoring of data security state. By protecting sensitive data against leakage based on the whole life cycle of data, the big data security of power grid informatization can be effectively guaranteed and the safety immunity of power information system can be improved.
Zhao, Yi, Jia, Xian, An, Dou, Yang, Qingyu.  2020.  LSTM-Based False Data Injection Attack Detection in Smart Grids. 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC). :638—644.
As a typical cyber-physical system, smart grid has attracted growing attention due to the safe and efficient operation. The false data injection attack against energy management system is a new type of cyber-physical attack, which can bypass the bad data detector of the smart grid to influence the results of state estimation directly, causing the energy management system making wrong estimation and thus affects the stable operation of power grid. We transform the false data injection attack detection problem into binary classification problem in this paper, which use the long-term and short-term memory network (LSTM) to construct the detection model. After that, we use the BP algorithm to update neural network parameters and utilize the dropout method to alleviate the overfitting problem and to improve the detection accuracy. Simulation results prove that the LSTM-based detection method can achieve higher detection accuracy comparing with the BPNN-based approach.
Lu, Xiao, Jing, Jiangping, Wu, Yi.  2020.  False Data Injection Attack Location Detection Based on Classification Method in Smart Grid. 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). :133—136.
The state estimation technology is utilized to estimate the grid state based on the data of the meter and grid topology structure. The false data injection attack (FDIA) is an information attack method to disturb the security of the power system based on the meter measurement. Current FDIA detection researches pay attention on detecting its presence. The location information of FDIA is also important for power system security. In this paper, locating the FDIA of the meter is regarded as a multi-label classification problem. Each label represents the state of the corresponding meter. The ensemble model, the multi-label decision tree algorithm, is utilized as the classifier to detect the exact location of the FDIA. This method does not need the information of the power topology and statistical knowledge assumption. The numerical experiments based on the IEEE-14 bus system validates the performance of the proposed method.
2021-06-02
Das, Sima, Panda, Ganapati.  2020.  An Initiative Towards Privacy Risk Mitigation Over IoT Enabled Smart Grid Architecture. 2020 International Conference on Renewable Energy Integration into Smart Grids: A Multidisciplinary Approach to Technology Modelling and Simulation (ICREISG). :168—173.
The Internet of Things (IoT) has transformed many application domains with realtime, continuous, automated control and information transmission. The smart grid is one such futuristic application domain in execution, with a large-scale IoT network as its backbone. By leveraging the functionalities and characteristics of IoT, the smart grid infrastructure benefits not only consumers, but also service providers and power generation organizations. The confluence of IoT and smart grid comes with its own set of challenges. The underlying cyberspace of IoT, though facilitates communication (information propagation) among devices of smart grid infrastructure, it undermines the privacy at the same time. In this paper we propose a new measure for quantifying the probability of privacy leakage based on the behaviors of the devices involved in the communication process. We construct a privacy stochastic game model based on the information shared by the device, and the access to the compromised device. The existence of Nash Equilibrium strategy of the game is proved theoretically. We experimentally validate the effectiveness of the privacy stochastic game model.
2021-05-25
Pradhan, Ankit, R., Punith., Sethi, Kamalakanta, Bera, Padmalochan.  2020.  Smart Grid Data Security using Practical CP-ABE with Obfuscated Policy and Outsourcing Decryption. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–8.
Smart grid consists of multiple different entities related to various energy management systems which share fine-grained energy measurements among themselves in an optimal and reliable manner. Such delivery is achieved through intelligent transmission and distribution networks composed of various stakeholders like Phasor Measurement Units (PMUs), Master and Remote Terminal Units (MTU and RTU), Storage Centers and users in power utility departments subject to volatile changes in requirements. Hence, secure accessibility of data becomes vital in the context of efficient functioning of the smart grid. In this paper, we propose a practical attribute-based encryption scheme for securing data sharing and data access in Smart Grid architectures with the added advantage of obfuscating the access policy. This is aimed at preserving data privacy in the context of competing smart grid operators. We build our scheme on Linear Secret Sharing (LSS) Schemes for supporting any monotone access structures and thus enhancing the expressiveness of access policies. Lastly, we analyze the security, access policy privacy and collusion resistance properties of our cryptosystem and provide an efficiency comparison as well as experimental analysis using the Charm-Crypto framework to validate the proficiency of our proposed solution.
2021-05-13
Fei, Wanghao, Moses, Paul, Davis, Chad.  2020.  Identification of Smart Grid Attacks via State Vector Estimator and Support Vector Machine Methods. 2020 Intermountain Engineering, Technology and Computing (IETC). :1—6.

In recent times, an increasing amount of intelligent electronic devices (IEDs) are being deployed to make power systems more reliable and economical. While these technologies are necessary for realizing a cyber-physical infrastructure for future smart power grids, they also introduce new vulnerabilities in the grid to different cyber-attacks. Traditional methods such as state vector estimation (SVE) are not capable of identifying cyber-attacks while the geometric information is also injected as an attack vector. In this paper, a machine learning based smart grid attack identification method is proposed. The proposed method is carried out by first collecting smart grid power flow data for machine learning training purposes which is later used to classify the attacks. The performance of both the proposed SVM method and the traditional SVE method are validated on IEEE 14, 30, 39, 57 and 118 bus systems, and the performance regarding the scale of the power system is evaluated. The results show that the SVM-based method performs better than the SVE-based in attack identification over a much wider scale of power systems.

2021-04-09
Ravikumar, G., Singh, A., Babu, J. R., A, A. Moataz, Govindarasu, M..  2020.  D-IDS for Cyber-Physical DER Modbus System - Architecture, Modeling, Testbed-based Evaluation. 2020 Resilience Week (RWS). :153—159.
Increasing penetration of distributed energy resources (DERs) in distribution networks expands the cyberattack surface. Moreover, the widely used standard protocols for communicating DER inverters such as Modbus is more vulnerable to data-integrity attacks and denial of service (DoS) attacks because of its native clear-text packet format. This paper proposes a distributed intrusion detection system (D-IDS) architecture and algorithms for detecting anomalies on the DER Modbus communication. We devised a model-based approach to define physics-based threshold bands for analog data points and transaction-based threshold bands for both the analog and discrete data points. The proposed IDS algorithm uses the model- based approach to develop Modbus-specific IDS rule sets, which can enhance the detection accuracy of the anomalies either by data-integrity attacks or maloperation on cyber-physical DER Modbus devices. Further, the IDS algorithm autogenerates the Modbus-specific IDS rulesets in compliance with various open- source IDS rule syntax formats, such as Snort and Suricata, for seamless integration and mitigation of semantic/syntax errors in the development and production environment. We considered the IEEE 13-bus distribution grid, including DERs, as a case study. We conducted various DoS type attacks and data-integrity attacks on the hardware-in-the-loop (HIL) CPS DER testbed at ISU to evaluate the proposed D-IDS. Consequently, we computed the performance metrics such as IDS detection accuracy, IDS detection rate, and end-to-end latency. The results demonstrated that 100% detection accuracy, 100% detection rate for 60k DoS packets, 99.96% detection rate for 80k DoS packets, and 0.25 ms end-to-end latency between DERs to Control Center.
2021-03-29
Fajri, M., Hariyanto, N., Gemsjaeger, B..  2020.  Automatic Protection Implementation Considering Protection Assessment Method of DER Penetration for Smart Distribution Network. 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP). :323—328.
Due to geographical locations of Indonesia, some technology such as hydro and solar photovoltaics are very attractive to be used and developed. Distribution Energy Resources (DER) is the appropriate schemes implemented to achieve optimal operation respecting the location and capacity of the plant. The Gorontalo sub-system network was chosen as a case study considering both of micro-hydro and PV as contributed to supply the grid. The needs of a smart electrical system are required to improve reliability, power quality, and adaptation to any circumstances during DER application. While the topology was changing over time, intermittent of DER output and bidirectional power flow can be overcome with smart grid systems. In this study, an automation algorithm has been conducted to aid the engineers in solving the protection problems caused by DER implementation. The Protection Security Assessment (PSA) method is used to evaluate the state of the protection system. Determine the relay settings using an adaptive rule-based method on expert systems. The application with a Graphical User Interface (GUI) has been developed to make user easier to get the specific relay settings and locations which are sensitive, fast, reliable, and selective.