Securing a power management chain for Smart Grids

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

The smart grid is an upgraded concept of electricity network with tight coupling among information, control, and bi‐directional communication technologies. Along with the silent features of the on‐going smart grid, cyber–physical security appears to be a deep concern due to its significant dependence on sensing technologies, complex networks of computers, intelligence, and wide‐area communication infrastructures. Moreover, the smart grid is an extensive critical infrastructure and vulnerable to coordinated cyber–physical attacks. As a result, cyber–physical attacks cause significant threats to the confidentiality and integrity of the power data, including power outages, cascading failures, and unnecessary expenditure. In this study, security issues of smart grid, including cyber–physical interdependency, attack varieties, detection methods, requirements, standards, challenges, and future prospects, are taken into consideration for both cyber and physical systems, aiming to provide an extensive understanding of vulnerabilities and solutions for the smart power grid. By revealing the inherent features of cyber–physical security in the smart grid, this survey study is addressed to facilitate future research in these two areas.

A highly robust secure protocol is proposed to provide communications link layer security including data source authentication, data integrity, data confidentiality, and replay attack resistance. The protocol is based on shared secret data (SSD) and time codes between two communications entities. The Keyed Hash Message Authentication Code with Secure Hash Algorithm-2 (HMAC-SHA-2) is employed for data source authentication, data integrity and session key generation, while Advanced Encryption Standard (AES) provides the data confidentiality. The Crypto Sync which is already required for encryption/decryption of data in a secure link layer transfer frame operating in cipher feedback mode (CFB) as the initialization vector is also utilized as the Message Authentication Code (MAC) and session key generator and distributor. No additional overhead is required and the link channel bandwidth remains the same. A link layer Consultative Committee on Space Data System (CCSDS) data frame structure is given as the example to illustrate the protocol. The proposed protocol can be applied to any encrypted link layer transfer frame between any two communications entities.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

Integrity protecting hierarchical concealed data aggregation for wireless sensor networks

Different from the traditional grid, smart grid builds a real-time connection network between the user and the grid company by smart terminals, which can achieve bidirectional data transmission and information control. In smart grid, the smart meters send various information to the power generators and substations. Frequent data collection meets real-time management, but it tends to raise privacy concerns from the users about privacy information leakage. Based on the blind signature and the key distribution scheme, an efficient and privacy-preserving data collection (EPPDC) scheme is proposed for smart grid to cope with the above problems. In EPPDC scheme, the users' data information is transmitted to the local aggregator by building gateway with privacy preserving. In addition, the security analysis indicates that EPPDC scheme not only can resist replay attack, but also has source authentication and data integrity, confidentiality, unforgeability, nonrepudiation, and evolution of shared keys. Furthermore, performance analysis shows that EPPDC scheme has less computation cost than existing scheme.

Fog-enabled secure multiparty computation based aggregation scheme in smart grid

The increase in significant advances in information technology greatly provides comfort and convenience in managing data. This convenience is what makes people who are not responsible for using it as a crime such as hacking, cracking, phishing, and so on. In Telkom Region 4 Semarang, there is a container where there are important company data such as customer data. Customer data is very important and the contents of the data must be kept confidential. The company has experienced significant losses due to information leakage due to negligence in the last 5 years. For this reason, data security is necessary so that data is safe and is not misused. This study applies the Advance Encryption Standard algorithm - Cipher Block Chaining (AES-CBC) and Stream cipher in order to secure data so as to reduce the risk of data theft by telecom subscribers. Based on the average avalanche effect value of AES-CBC and a stream cipher of 49.34%, this shows that the AES-CBC and Stream Cipher encrypted files are difficult to crack so that data confidentiality is well maintained.

The most advanced power grid design, known as a ‘smart power grid’, integrates information and communication technology (ICT) with a conventional grid system to enable remote management of electricity distribution. The intelligent cyber‐physical architecture enables bidirectional, real‐time data sharing between electricity suppliers and consumers through smart meters and advanced metering infrastructure (AMI). Data protection issues, such as data tampering, firmware exploitation, and the leakage of sensitive information arise due to the smart power grid's substantial reliance on ICT. To maintain reliable and efficient power distribution, these issues must be identified and resolved quickly. Intrusion detection is essential for providing secure services and alerting system administrators in the case of adversary attacks. This paper proposes an intrusion classification scheme that identifies several types of cyber attacks on modern smart power grids. Grey‐Wolf metaheuristic optimization‐based feature selection is used to learn non‐linear, overlapping, and complex electrical grid properties. An extended deep‐stacked ensemble technique is advanced by putting predictions from weak learners (CNNs) into a meta‐learner (MLP). The outcomes of this approach are explained and confirmed using explainable AI (XAI). The publicly available dataset from Mississippi State University and Oak Ridge National Laboratory (MSU‐ORNL) is used to conduct experiments. The experimental results show that the proposed method achieved a peak accuracy of 96.6% while scrutinizing the original MSU‐ORNL data feature set and a maximum accuracy of 99% when analysing the selected feature set. Therefore, the proposed intrusion classification scheme may protect smart power grid systems against cyber security attacks.

In this paper, a high-performance secure hash algorithm 3 (SHA-3) is proposed to handle massive amounts of data for applications such as edge computing, medical image encryption, and blockchain networks. This work not only focuses on the SHA-3 core as in previous works but also addresses the bottleneck phenomenon caused by transfer rates. Our proposed SHA-3 architecture serves as the hardware accelerator for personal computers (PC) connected via a peripheral component interconnect express (PCIe), enhancing data transfer rates between the host PC and dedicated computation components like SHA-3. Additionally, the throughput of the SHA-3 core is enhanced based on two different proposals for the KECCAK-f algorithm: re-scheduled and sub-pipelined architectures. The multiple KECCAK-f is applied to maximize data transfer throughput. Configurable buffer in/out (BIO) is introduced to support all SHA-3 modes, which is suitable for devices that handle various hashing applications. The proposed SHA-3 architectures are implemented and tested on DE10-Pro supporting Stratix 10 - 1SX280HU2F50E1VG and PCIe, achieving a throughput of up to 35.55 Gbps and 43.12 Gbps for multiple-re-scheduled-KECCAK-f-based SHA3 (MRS) and multiple-sub-pipelined-KECCAK-f-based SHA-3 (MSS), respectively.

Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects

With the increasing importance of data security in digital image transmission and storage, this research presents an implementation of an image encryption and decryption program using the Advanced Encryption Standard (AES) algorithm combined with Message Authentication Code (MAC) for enhanced security. The program utilizes AES in CipherBlock Chaining (CBC) mode to ensure confidentiality and integrity of the image data. The unique key and initialization vector (IV) enhance the security of the encryption process. Additionally, the inclusion of MAC ensures data integrity and prevents unauthorized modifications during transmission or storage. The program offers a user-friendly web-based interface for easy usability. The implemented solution provides a high level of security for image data and can be applied in various applications requiring secure image transmission and storage. The effectiveness and reliability of the program are demonstrated through experimental results and evaluation.

In smart power grid systems, real-time electricity data sharing can bring many benefits. However the security and privacy challenges have been a concern. This is because shared data may contain some private and sensitive information of data owners or users. So it has been an important issue to balance data utilization, data privacy and users privacy. Privacy-preserving multi-authority attribute-based encryption (MA-ABE) is a promising tool to tackle this issue, but it cannot be directly applied to addressed scenario of smart power grid systems. In this paper, we propose a framework to achieve secure data sharing in smart grid. The scheme uses the technique of inner product encryption (IPE). Under this framework, the access policy and the attribute set are fuzzy by converting them into two vectors. Only when the two vectors are orthogonal, users can successfully obtain the sharing data. Besides, our scheme allows that the threshold access policy only involves attribute name indexes, and sensitive attribute values are not sent along with the shared data so the attribute privacy is preserved. In addition, in order to improve efficiency, our scheme adds a testing phase to avoid the unnecessary operation before recovering data. Finally, our performance analysis and experiments confirm the merits of the proposed framework.

In smart grid, critical data like monitoring data, usage data, state estimation, billing data etc are regularly being talked among its elements. So, security of such a system, if violated, results in massive losses and damages. By compromising with security aspect of such a system is as good as committing suicide. Thus in this paper, we have proposed security mechanism in Advanced Metering Infrastructure of smart grid, formed as Mesh-Zigbee topology. This security mechanism involves PKI based Digital certificate Authentication and Intrusion detection system to protect the AMI from internal and external security attack.

As the number of active Internet of Things (IoT) devices increases, managing and securing the enormous amount of data generated by these devices become harder. With their always‐connected smart meters and internet‐enabled electrical appliances, smart grids (SGs) are major data generation sources in IoT. Traditional SGs store and process their collected data in centralized cloud servers. Secure data aggregation (SDA) protocols play an essential role in efficient and private data collection and storage in cloud‐based SGs. Due to a high number of connected devices, centralized cloud‐based SGs may become inadequate to satisfy the processing and storage needs of the collected data. This article introduces two efficient and lightweight SDA protocols for fog computing‐based SGs (FCSG), namely, FCSG‐DF and FCSG‐P, as a complementary solution for processing and storage needs of cloud‐based SGs. By leveraging the distributed nature and other extended capabilities of fog computing, the proposed SDA protocols have a better response time with less computational overhead. Moreover, the privacy of fine‐grained energy consumption data is preserved with the help of the privacy‐preserving data aggregation processes in the hierarchical fog computing structure of FCSG. Extensive performance evaluation results demonstrate that the proposed protocols have superiority in terms of data transmission and storage efficiency compared to cloud‐assisted schemes without data aggregation. Both protocols are analyzed by comparing their success in transmission and storage efficiencies. Moreover, privacy analysis verifies that both protocols effectively ensure the privacy of the data in each layer.