1
|
Huang J, Zeng G, Geng G, Weng J, Lu K. SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet‐of‐Things. IET CYBER-SYSTEMS AND ROBOTICS 2023. [DOI: 10.1049/csy2.12085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
|
2
|
Simultaneous detection for multiple anomaly data in internet of energy based on random forest. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.109993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
3
|
Kumar G, Narducci F, Bakshi S. Knowledge Transfer and Crowdsourcing in Cyber-Physical-Social Systems. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
4
|
Yin L, Li Y. Fuzzy vector reinforcement learning algorithm for generation control of power systems considering flywheel energy storage. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109149] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
5
|
Multi-objective scheduling of IoT-enabled smart homes for energy management based on Arithmetic Optimization Algorithm: A Node-RED and NodeMCU module-based technique. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108762] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
6
|
Novel Hybrid Model for Intrusion Prediction on Cyber Physical Systems’ Communication Networks based on Bio-inspired Deep Neural Network Structure. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2022. [DOI: 10.1016/j.jisa.2021.103107] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
7
|
Sultana A, Bardalai A, Sarma KK. Salp Swarm-Artificial Neural Network Based Cyber-Attack Detection in Smart Grid. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10743-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
8
|
Diaba SY, Shafie-khah M, Elmusrati M. On the performance metrics for cyber-physical attack detection in smart grid. Soft comput 2022. [DOI: 10.1007/s00500-022-06761-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractSupervisory Control and Data Acquisition (SCADA) systems play an important role in Smart Grid. Though the rapid evolution provides numerous advantages it is one of the most desired targets for malicious attackers. So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark. In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective approach. Extensive experiments are conducted using a benchmark dataset to validate the performance of the proposed intrusion detection model in a smart metering environment. Parameters such as accuracy, precision, and false-positive rate are compared with existing deep learning models. The proposed concatenated approach attains 98.84% detection accuracy which is much better than existing techniques.
Collapse
|
9
|
Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach. COMPUT IND 2021. [DOI: 10.1016/j.compind.2021.103509] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
10
|
A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification. SUSTAINABILITY 2021. [DOI: 10.3390/su13179597] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.
Collapse
|