Visible to the public Biblio

Found 212 results

Filters: Keyword is Smart grid  [Clear All Filters]
Alfassa, Shaik Mirra, Nagasundari, S, Honnavalli, Prasad B.  2021.  Invasion Analysis of Smart Meter In AMI System. 2021 IEEE Mysore Sub Section International Conference (MysuruCon). :831—836.
Conventional systems has to be updated as the technology advances at quick pace. A smart grid is a renovated and digitalized version of a standard electrical infrastructure that allows two-way communication between customers and the utility, which overcomes huge manual hustle. Advanced Metering Infrastructure plays a major role in a smart grid by automatically reporting the power consumption readings to the utility through communication networks. However, there is always a trade-off. Security of AMI communication is a major problem that must be constantly monitored if this technology is to be fully utilized. This paper mainly focuses on developing a virtual setup of fully functional smart meter and a web application for generating electricity bill which allows consumer to obtain demand response, where the data is managed at server side. It also focuses on analyzing the potential security concerns posed by MITM-Arp-spoofing attacks on AMI systems and session hijacking attacks on web interfaces. This work also focusses on mitigating the vulnerabilities of session hijacking on web interface by restricting the cookies so that the attacker is unable to acquire any confidential data.
Burgetová, Ivana, Matoušek, Petr, Ryšavý, Ondřej.  2021.  Anomaly Detection of ICS Communication Using Statistical Models. 2021 17th International Conference on Network and Service Management (CNSM). :166–172.
Industrial Control System (ICS) transmits control and monitoring data between devices in an industrial environment that includes smart grids, water and gas distribution, or traffic control. Unlike traditional internet communication, ICS traffic is stable, periodical, and with regular communication patterns that can be described using statistical modeling. By observing selected features of ICS transmission, e.g., packet direction and inter-arrival times, we can create a statistical profile of the communication based on distribution of features learned from the normal ICS traffic. This paper demonstrates that using statistical modeling, we can detect various anomalies caused by irregular transmissions, device or link failures, and also cyber attacks like packet injection, scanning, or denial of service (DoS). The paper shows how a statistical model is automatically created from a training dataset. We present two types of statistical profiles: the master-oriented profile for one-to-many communication and the peer-to-peer profile that describes traffic between two ICS devices. The proposed approach is fast and easy to implement as a part of an intrusion detection system (IDS) or an anomaly detection (AD) module. The proof-of-concept is demonstrated on two industrial protocols: IEC 60870-5-104 (aka IEC 104) and IEC 61850 (Goose).
Samy, Salma, Banawan, Karim, Azab, Mohamed, Rizk, Mohamed.  2021.  Smart Blockchain-based Control-data Protection Framework for Trustworthy Smart Grid Operations. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). :0963—0969.
The critical nature of smart grids (SGs) attracts various network attacks and malicious manipulations. Existent SG solutions are less capable of ensuring secure and trustworthy operation. This is due to the large-scale nature of SGs and reliance on network protocols for trust management. A particular example of such severe attacks is the false data injection (FDI). FDI refers to a network attack, where meters' measurements are manipulated before being reported in such a way that the energy system takes flawed decisions. In this paper, we exploit the secure nature of blockchains to construct a data management framework based on public blockchain. Our framework enables trustworthy data storage, verification, and exchange between SG components and decision-makers. Our proposed system enables miners to invest their computational power to verify blockchain transactions in a fully distributed manner. The mining logic employs machine learning (ML) techniques to identify the locations of compromised meters in the network, which are responsible for generating FDI attacks. In return, miners receive virtual credit, which may be used to pay their electric bills. Our design circumvents single points of failure and intentional FDI attempts. Our numerical results compare the accuracy of three different ML-based mining logic techniques in two scenarios: focused and distributed FDI attacks for different attack levels. Finally, we proposed a majority-decision mining technique for the practical case of an unknown FDI attack level.
Baruah, Barnana, Dhal, Subhasish.  2021.  An Authenticated Key Agreement Scheme for Secure Communication in Smart Grid. 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS). :447—455.
Rapid development of wireless technologies has driven the evolution of smart grid application. In smart grid, authentication plays an important role for secure communication between smart meter and service provider. Hence, the design of secure authenticated key agreement schemes has received significant attention from researchers. In these schemes, a trusted third party directly participates in key agreement process. Although, this third party is assumed as trusted, however we cannot reject the possibility that being a third party, it can also be malicious. In the existing works, either the established session key is revealed to the agents of a trusted third party, or a trusted third party agent can impersonate the smart meter and establish a valid session key with the service provider, which is likely to cause security vulnerabilities. Therefore, there is a need to design a secure authentication scheme so that only the deserving entities involved in the communication can establish and know the session key. This paper proposes a new secure authenticated key agreement scheme for smart grid considering the fact that the third party can also be malicious. The security of the proposed scheme has been thoroughly evaluated using an adversary model. Correctness of the scheme has been analyzed using the broadly accepted Burrows-Abadi-Needham (BAN) Logic. In addition, the formal security verification of the proposed scheme has been performed using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Results of this simulation confirm that the proposed scheme is safe. Detailed security analysis shows the robustness of the scheme against various known attacks. Moreover, the comparative performance study of the proposed scheme with other relevant schemes is presented to demonstrate its practicality.
Luo, Weifeng, Xiao, Liang.  2021.  Reinforcement Learning Based Vulnerability Analysis of Data Injection Attack for Smart Grids. 2021 40th Chinese Control Conference (CCC). :6788—6792.
Smart grids have to protect meter measurements against false data injection attacks. By modifying the meter measurements, the attacker misleads the control decisions of the control center, which results in physical damages of power systems. In this paper, we propose a reinforcement learning based vulnerability analysis scheme for data injection attack without relying on the power system topology. This scheme enables the attacker to choose the data injection attack vector based on the meter measurements, the power system status, the previous injected errors and the number of meters to compromise. By combining deep reinforcement learning with prioritized experience replay, the proposed scheme more frequently replays the successful vulnerability detection experiences while bypassing the bad data detection, which is able to accelerate the learning speed. Simulation results based on the IEEE 14 bus system show that this scheme increases the probability of successful vulnerability detection and reduce the number of meters to compromise compared with the benchmark scheme.
Fuquan, Huang, Zhiwei, Liu, Jianyong, Zhou, Guoyi, Zhang, Likuan, Gong.  2021.  Vulnerability Analysis of High-Performance Transmission and Bearer Network of 5G Smart Grid Based on Complex Network. 2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN). :292—297.
5G smart grid applications rely on its high-performance transmission and bearer network. With the help of complex network theory, this paper first analyzes the complex network characteristic parameters of 5G smart grid, and explains the necessity and supporting significance of network vulnerability analysis for efficient transmission of 5G network. Then the node importance analysis algorithm based on node degree and clustering coefficient (NIDCC) is proposed. According to the results of simulation analysis, the power network has smaller path length and higher clustering coefficient in terms of static parameters, which indicates that the speed and breadth of fault propagation are significantly higher than that of random network. It further shows the necessity of network vulnerability analysis. By comparing with the other two commonly used algorithms, we can see that NIDCC algorithm can more accurately estimate and analyze the weak links of the network. It is convenient to carry out the targeted transformation of the power grid and the prevention of blackout accidents.
T⊘ndel, Inger Anne, Vefsnmo, Hanne, Gjerde, Oddbj⊘rn, Johannessen, Frode, Fr⊘ystad, Christian.  2021.  Hunting Dependencies: Using Bow-Tie for Combined Analysis of Power and Cyber Security. 2020 2nd International Conference on Societal Automation (SA). :1—8.
Modern electric power systems are complex cyber-physical systems. The integration of traditional power and digital technologies result in interdependencies that need to be considered in risk analysis. In this paper we argue the need for analysis methods that can combine the competencies of various experts in a common analysis focusing on the overall system perspective. We report on our experiences on using the Vulnerability Analysis Framework (VAF) and bow-tie diagrams in a combined analysis of the power and cyber security aspects in a realistic case. Our experiences show that an extended version of VAF with increased support for interdependencies is promising for this type of analysis.
Mukherjee, Debottam, Chakraborty, Samrat, Banerjee, Ramashis, Bhunia, Joydeep.  2021.  A Novel Real-Time False Data Detection Strategy for Smart Grid. 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC). :1—6.
State estimation algorithm ensures an effective realtime monitoring of the modern smart grid leading to an accurate determination of the current operating states. Recently, a new genre of data integrity attacks namely false data injection attack (FDIA) has shown its deleterious effects by bypassing the traditional bad data detection technique. Modern grid operators must detect the presence of such attacks in the raw field measurements to guarantee a safe and reliable operation of the grid. State forecasting based FDIA identification schemes have recently shown its efficacy by determining the deviation of the estimated states due to an attack. This work emphasizes on a scalable deep learning state forecasting model which can accurately determine the presence of FDIA in real-time. An optimal set of hyper-parameters of the proposed architecture leads to an effective forecasting of the operating states with minimal error. A diligent comparison between other state of the art forecasting strategies have promoted the effectiveness of the proposed neural network. A comprehensive analysis on the IEEE 14 bus test bench effectively promotes the proposed real-time attack identification strategy.
Parizad, Ali, Hatziadoniu, Constantine.  2021.  False Data Detection in Power System Under State Variables' Cyber Attacks Using Information Theory. 2021 IEEE Power and Energy Conference at Illinois (PECI). :1—8.
State estimation (SE) plays a vital role in the reliable operation of modern power systems, gives situational awareness to the operators, and is employed in different functions of the Energy Management System (EMS), such as Optimal Power Flow (OPF), Contingency Analysis (CA), power market mechanism, etc. To increase SE's accuracy and protect it from compromised measurements, Bad Data Detection (BDD) algorithm is employed. However, the integration of Information and Communication Technologies (ICT) into the modern power system makes it a complicated cyber-physical system (CPS). It gives this opportunity to an adversary to find some loopholes and flaws, penetrate to CPS layer, inject false data, bypass existing BDD schemes, and consequently, result in security and stability issues. This paper employs a semi-supervised learning method to find normal data patterns and address the False Data Injection Attack (FDIA) problem. Based on this idea, the Probability Distribution Functions (PDFs) of measurement variations are derived for training and test data sets. Two distinct indices, i.e., Absolute Distance (AD) and Relative Entropy (RE), a concept in Information Theory, are utilized to find the distance between these two PDFs. In case an intruder compromises data, the related PDF changes. However, we demonstrate that AD fails to detect these changes. On the contrary, the RE index changes significantly and can properly detect FDIA. This proposed method can be used in a real-time attack detection process where the larger RE index indicates the possibility of an attack on the real-time data. To investigate the proposed methodology's effectiveness, we utilize the New York Independent System Operator (NYISO) data (Jan.-Dec. 2019) with a 5-minute resolution and map it to the IEEE 14-bus test system, and prepare an appropriate data set. After that, two different case studies (attacks on voltage magnitude ( Vm), and phase angle (θ)) with different attack parameters (i.e., 0.90, 0.95, 0.98, 1.02, 1.05, and 1.10) are defined to assess the impact of an attack on the state variables at different buses. The results show that RE index is a robust and reliable index, appropriate for real-time applications, and can detect FDIA in most of the defined case studies.
Tufail, Shahid, Batool, Shanzeh, Sarwat, Arif I..  2021.  False Data Injection Impact Analysis In AI-Based Smart Grid. SoutheastCon 2021. :01—07.
As the traditional grids are transitioning to the smart grid, they are getting more prone to cyber-attacks. Among all the cyber-attack one of the most dangerous attack is false data injection attack. When this attack is performed with historical information of the data packet the attack goes undetected. As the false data is included for training and testing the model, the accuracy is decreased, and decision making is affected. In this paper we analyzed the impact of the false data injection attack(FDIA) on AI based smart grid. These analyses were performed using two different multi-layer perceptron architectures with one of the independent variables being compared and modified by the attacker. The root-mean squared values were compared with different models.
Chen, Lin, Qiu, Huijun, Kuang, Xiaoyun, Xu, Aidong, Yang, Yiwei.  2021.  Intelligent Data Security Threat Discovery Model Based on Grid Data. 2021 6th International Conference on Image, Vision and Computing (ICIVC). :458–463.
With the rapid construction and popularization of smart grid, the security of data in smart grid has become the basis for the safe and stable operation of smart grid. This paper proposes a data security threat discovery model for smart grid. Based on the prediction data analysis method, combined with migration learning technology, it analyzes different data, uses data matching process to classify the losses, and accurately predicts the analysis results, finds the security risks in the data, and prevents the illegal acquisition of data. The reinforcement learning and training process of this method distinguish the effective authentication and illegal access to data.
Chen, Liming, Suo, Siliang, Kuang, Xiaoyun, Cao, Yang, Tao, Wenwei.  2021.  Secure Ubiquitous Wireless Communication Solution for Power Distribution Internet of Things in Smart Grid. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). :780–784.
With rapid advancement of Smart Grid as well as Internet of Things (IoT), current power distribution communication network faces the challenges of satisfying the emerging data transmission requirements of ubiquitous secure coverage for distributed power services. This paper focuses on secure ubiquitous wireless communication solution for power distribution Internet of Things (PDİoT) in Smart Grid. Detailed secure ubiquitous wireless communication networking topology is presented, and integrated encryption and communication device is developed. The proposed solution supports several State Secret cryptographic algorithm including SM1/SM2/SM3/SM4 as well as forward and reverse isolation functions, thus achieving secure wireless communication for PDİoT services.
Bhattacharjee, Arpan, Badsha, Shahriar, Sengupta, Shamik.  2021.  Personalized Privacy Preservation for Smart Grid. 2021 IEEE International Smart Cities Conference (ISC2). :1–7.
The integration of advanced information, communication and data analytic technologies has transformed the traditional grid into an intelligent bidirectional system that can automatically adapt its services for utilities or consumers' needs. However, this change raises new privacy-related challenges. Privacy leakage has become a severe issue in the grid paradigm as adversaries run malicious analytics to identify the system's internal insight or use it to interrupt grids' operation by identifying real-time demand-based supply patterns. As a result, current grid authorities require an integrated mechanism to improve the system's sensitive data's privacy preservation. To this end, we present a multilayered smart grid architecture by characterizing the privacy issues that occur during data sharing, aggregation, and publishing by individual grid end nodes. Based on it, we quantify the nodes preferred privacy requirements. We further introduce personalized differential privacy (PDP) scheme based on trust distance in our proposed framework to provide the system with the added benefit of a user-specific privacy guarantee to eliminate differential privacy's limitation that allows the same level of privacy for all data providers. Lastly, we conduct extensive experimental analysis on a real-world grid dataset to illustrate that our proposed method is efficient enough to provide privacy preservation on sensitive smart grid data.
Miyamae, Takeshi, Kozakura, Fumihiko, Nakamura, Makoto, Zhang, Shenbin, Hua, Song, Pi, Bingfeng, Morinaga, Masanobu.  2021.  ZGridBC: Zero-Knowledge Proof Based Scalable and Private Blockchain Platform for Smart Grid. 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :1–3.
The total number of photovoltaic power producing facilities whose FIT-based ten-year contract expires by 2023 is expected to reach approximately 1.65 million in Japan. If the number of renewable electricity-producing/consuming facilities reached two million, an enormous number of transactions would be invoked beyond blockchain's scalability.We propose mutually cooperative two novel methods to simultaneously solve scalability, data size, and privacy problems in blockchain-based trading platforms for renewable energy environmental value. One is a management scheme of electricity production resources (EPRs) using an extended UTXO token. The other is a data aggregation scheme that aggregates a significant number of smart meter records with evidentiality using zero-knowledge proof (ZKP).
Sun, Chuang, Shen, Sujin.  2021.  An Improved Byzantine Consensus Based Multi-Signature Algorithm. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :777–780.
Traditional grid-centric data storage methods are vulnerable to network attacks or failures due to downtime, causing problems such as data loss or tampering. The security of data storage can be effectively improved by establishing an alliance chain. However, the existing consortium chain consensus algorithm has low scalability, and the consensus time will explode as the number of nodes increases. This paper proposes an improved consensus algorithm (MSBFT) based on multi-signature to address this problem, which spreads data by establishing a system communication tree, reducing communication and network transmission costs, and improving system scalability. By generating schnorr multi-signature as the shared signature of system nodes, the computational cost of verification between nodes is reduced. At the end of the article, simulations prove the superiority of the proposed method.
Danilczyk, William, Sun, Yan Lindsay, He, Haibo.  2021.  Smart Grid Anomaly Detection using a Deep Learning Digital Twin. 2020 52nd North American Power Symposium (NAPS). :1—6.

The power grid is considered to be the most critical piece of infrastructure in the United States because each of the other fifteen critical infrastructures, as defined by the Cyberse-curity and Infrastructure Security Agency (CISA), require the energy sector to properly function. Due the critical nature of the power grid, the ability to detect anomalies in the power grid is of critical importance to prevent power outages, avoid damage to sensitive equipment and to maintain a working power grid. Over the past few decades, the modern power grid has evolved into a large Cyber Physical System (CPS) equipped with wide area monitoring systems (WAMS) and distributed control. As smart technology advances, the power grid continues to be upgraded with high fidelity sensors and measurement devices, such as phasor measurement units (PMUs), that can report the state of the system with a high temporal resolution. However, this influx of data can often become overwhelming to the legacy Supervisory Control and Data Acquisition (SCADA) system, as well as, the power system operator. In this paper, we propose using a deep learning (DL) convolutional neural network (CNN) as a module within the Automatic Network Guardian for ELectrical systems (ANGEL) Digital Twin environment to detect physical faults in a power system. The presented approach uses high fidelity measurement data from the IEEE 9-bus and IEEE 39-bus benchmark power systems to not only detect if there is a fault in the power system but also applies the algorithm to classify which bus contains the fault.

Roy, Sohini, Sen, Arunabha.  2021.  Identification and Mitigation of False Data Injection using Multi State Implicative Interdependency Model (MSIIM) for Smart Grid. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.

Smart grid monitoring, automation and control will completely rely on PMU based sensor data soon. Accordingly, a high throughput, low latency Information and Communication Technology (ICT) infrastructure should be opted in this regard. Due to the low cost, low power profile, dynamic nature, improved accuracy and scalability, wireless sensor networks (WSNs) can be a good choice. Yet, the efficiency of a WSN depends a lot on the network design and the routing technique. In this paper a new design of the ICT network for smart grid using WSN is proposed. In order to understand the interactions between different entities, detect their operational levels, design the routing scheme and identify false data injection by particular ICT entities, a new model of interdependency called the Multi State Implicative Interdependency Model (MSIIM) is proposed in this paper, which is an updated version of the Modified Implicative Interdependency Model (MIIM) [1]. MSIIM considers the data dependency and operational accuracy of entities together with structural and functional dependencies between them. A multi-path secure routing technique is also proposed in this paper which relies on the MSIIM model for its functioning. Simulation results prove that MSIIM based False Data Injection (FDI) detection and mitigation works better and faster than existing methods.

Xing, Ningzhe, Wu, Peng, Jin, Shen, Yao, Jiming, Xu, Zhichen.  2021.  Task Classification Unloading Algorithm For Mobile Edge Computing in Smart Grid. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 5:1636—1640.
With the rapid development of smart grid, the data generated by grid services are growing rapidly, and the requirements for time delay are becoming more and more stringent. The storage and computing capacity of the existing terminal equipment can not meet the needs of high bandwidth and low delay of the system at the same time. Fortunately, mobile edge computing (MEC) can provide users with nearby storage and computing services at the network edge, this can give an option to simultaneously meet the requirement of high bandwidth and low delay. Aiming at the problem of service offload scheduling in edge computing, this paper proposes a delay optimized task offload algorithm based on task priority classification. Firstly, the priority of power grid services is divided by using analytic hierarchy process (AHP), and the processing efficiency and quality of service of emergency tasks are guaranteed by giving higher weight coefficients to delay constraints and security levels. Secondly, the service is initialized and unloaded according to the task preprocessing time. Finally, the reasonable subchannel allocation is carried out based on the task priority design decision method. Simulation results show that compared with the traditional approaches, our algorithm can effectively improve the overall system revenue and reduce the average user task delay.
Kayalvizhy, V., Banumathi, A..  2021.  A Survey on Cyber Security Attacks and Countermeasures in Smart Grid Metering Network. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). :160—165.
Smart grid (SG) network is one of the recently improved networks of tangled entities, objects, and smart metering infrastructure (SMI). It plays a vital part in sensing, acquiring, observing, aggregating, controlling, and dealing with various kinds of fields in SG. The SMI or advanced metering infrastructure (AMI) is proposed to make available a real-time transmissions connection among users and services are Time of use (TOU), Real time pricing (RTP), Critical Peak Pricing (CPP). In adding to, additional benefit of SMs is which are capable to report back to the service control center in near real time nontechnical losses (for instance, tampering with meters, bypassing meters, and illicit tapping into distribution systems). SMI supports two-way transmission meters reading electrical utilization at superior frequency. This data is treated in real time and signals send to manage demand. This paper expresses a transitory impression of cyberattack instances in customary energy networks and SMI. This paper presents cyber security attacks and countermeasures in Smart Grid Metering Network (SGMN). Based on the existing survey threat models, a number of proposed ways have been planned to deal with all threats in the formulation of the secrecy and privacy necessities of SG measurement network.
Zhang, Tengyue, Chen, Liang, Han, Wen, Lin, Haojie, Xu, Aidong, Zhou, Zhiyu, Chen, Zhiwei, Jiang, Yixin, Zhang, Yunan.  2021.  Security Protection Technology of Electrical Power System Based on Edge Computing. 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA). :254—258.
In this paper, we mainly introduce the security protection technology of smart grid based on edge computing and propose an edge computing security protection architecture based on multi-service flexible mechanism. Aiming at the real time requirements of heterogeneous energy terminal access and power edge computing business in multiple interactive environment, a real-time and strong compatibility terminal security access mechanism integrating physical characteristics and lightweight cryptographic mechanism is proposed. According to different power terminal security data requirements, the edge computing data transmission, processing security and privacy protection technology are proposed. In addition, in the power system of distribution, microgrid and advanced metering system, the application of edge computing has been well reflected. Combined with encryption technology, access authentication, the security defense of edge data, edge equipment and edge application is carried out in many aspects, which strengthens the security and reliability of business penetration and information sharing at the edge of power grid, and realizes the end-to-end and end-to-system security prevention and control of power grid edge computing.
Xu, Ben, Liu, Jun.  2021.  False Data Detection Based On LSTM Network In Smart Grid. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :314—317.
In contrast to traditional grids, smart grids can help utilities save energy, thereby reducing operating costs. In the smart grid, the quality of monitoring and control can be fully improved by combining computing and intelligent communication knowledge. However, this will expose the system to FDI attacks, and the system is vulnerable to intrusion. Therefore, it is very important to detect such erroneous data injection attacks and provide an algorithm to protect the system from such attacks. In this paper, a FDI detection method based on LSTM has been proposed, which is validated by the simulation on the ieee-14 bus platform.
Zheng, Weijun, Chen, Ding, Duan, Jun, Xu, Hong, Qian, Wei, Gu, Leichun, Yao, Jiming.  2021.  5G Network Slice Configuration Based on Smart Grid. 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). 4:560—564.
The construction of a strong and smart grid is inseparable from the advancement of the power system, and the effective application of modern communication technologies allows the traditional grid to better transform into the energy Internet. With the advent of 5G, people pay close attention to the application of network slicing, not only as an emerging technology, but also as a new business model. In this article, we consider the delay requirements of certain services in the power grid. First, we analyze the security issues in network slicing and model the 5G core network slicing supply as a mixed integer linear programming problem. On this basis, a heuristic algorithm is proposed. According to the topological properties, resource utilization and delay of the slice nodes, the importance of them is sorted using the VIKOR method. In the slice link configuration stage, the shortest path algorithm is used to obtain the slice link physical path. Considering the delay of the slice link, a strategy for selecting the physical path is proposed. Simulations show that the scheme and algorithm proposed in this paper can achieve a high slice configuration success rate while ensuring the end-to-end delay requirements of the business, and meet the 5G core network slice security requirements.
Meng, Yu, Liangliang, Zhu, Yao, Rao, Yongxian, Yi, Jiaji, Liu.  2021.  Research on Fast Encryption Method for Smart Energy Management System in Smart Gird. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). :76—80.
Smart energy management system in smart grid carries a large number of sensitive data, which needs encryption algorithm to ensure the security of system communication. At present, most of the terminal devices of smart grid are embedded devices with limited computing resources, and their communication encryption mostly relies on AES encryption algorithm. It is difficult in key management and key distribution. Therefore, this paper proposes an improved ECC-AES hybrid encryption algorithm. Firstly, ECC algorithm is improved to improve the speed of encryption and decryption, and then the improved ECC algorithm is used as a supplement to AES algorithm. ECC is used to encrypt the AES key, which improves the security of the algorithm. At the same time, the experimental simulation also proves that the improved ECC algorithm has obvious performance improvement in computing time, CPU occupancy and memory usage.
Shen, Sujin, Sun, Chuang.  2021.  Research on Framework of Smart Grid Data Secure Storage from Blockchain Perspective. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :270—273.
With the development of technology, the structure of power grid becomes more and more complex, and the amount of data collected is also increasing. In the existing smart power grid, the data collected by sensors need to be uploaded and stored to the trusted central node, but the centralized storage method is easy to cause the malicious attack of the central node, resulting in single point failure, data tampering and other security problems. In order to solve these information security problems, this paper proposes a new data security storage framework based on private blockchain. By using the improved raft algorithm, partial decentralized data storage is used instead of traditional centralized storage. It also introduces in detail the working mechanism of the smart grid data security storage framework, including the process of uploading collected data, data verification, and data block consensus. The security analysis shows the effectiveness of the proposed data storage framework.
S, Muthulakshmi, R, Chitra.  2021.  Enhanced Data Privacy Algorithm to Protect the Data in Smart Grid. 2021 Smart Technologies, Communication and Robotics (STCR). :1—4.
Smart Grid is used to improve the accuracy of the grid network query. Though it gives the accuracy, it has the data privacy issues. It is a big challenge to solve the privacy issue in the smart grid. We need secured algorithms to protect the data in the smart grid, since the data is very important. This paper explains about the k-anonymous algorithm and analyzes the enhanced L-diversity algorithm for data privacy and security. The algorithm can protect the data in the smart grid is proven by the experiments.