Reference Hub

This research has been cited in:

Article
Smart City Applications to Promote Citizen Participation in City Management and Governance: A Systematic ReviewInformatics10.3390/informatics9040089
Article
Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentationBiomedical Signal Processing and Control10.1016/j.bspc.2023.104792
Article
Fault Diagnosis of Tenessee Eastman Process with Detection Quality Using IMVOA with Hybrid DL Technique in IIOTSN Computer Science10.1007/s42979-023-01851-9
Article
Parameters’ Optimization in Compressed Air Pressure Process Using Hybrid Fmin–GA-Based PID and Fractional Order Internal Model ControllerIETE Journal of Research10.1080/03772063.2022.2139297
Article
An Intrusion Detection Model Based on Hybridization of S-ROA in Deep Learning Model for MANETIranian Journal of Science and Technology, Transactions of Electrical Engineering10.1007/s40998-024-00700-6
Article
A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city securityScientific Reports10.1038/s41598-023-42257-0
Article
A Novel Split Learning-Based Consumer Electronics Network Traffic Anomaly Detection Framework for Smart City EnvironmentIEEE Transactions on Consumer Electronics10.1109/TCE.2024.3367330
Article
Hybrid RSA-ROA Scheduling Algorithm for Minimization of Power Loss and Improving the Renewable with Sustainable Energy Harvesting in Power SystemAdvances in Materials Science and Engineering10.1155/2022/8579180
Article
A hybrid DDAO-RBFNN strategy for fault tolerant operation in fifteen-level cascaded H-bridge (15L-CHB) inverter with solar photovoltaic (SPV) systemSolar Energy10.1016/j.solener.2022.08.015
Article
An Optimal Partitioning and Floor Planning for VLSI Circuit Design Based on a Hybrid Bio-Inspired Whale Optimization and Adaptive Bird Swarm Optimization (WO-ABSO) AlgorithmJournal of Circuits, Systems and Computers10.1142/S0218126623502730
Article
Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart CitiesComputers, Materials & Continua10.32604/cmc.2022.030825
Article
An optimization approach for optimal location & size of DSTATCOM and DGApplied Energy10.1016/j.apenergy.2023.120797
Chapter
Cybersecurity Threat Detection in SDN Clouds Using Attention Mechanism in RNNArtificial Intelligence and Sustainable Computing10.1007/978-981-97-0327-2_27
Article
A hybrid approach for enhancing and optimizing the power quality and power flow in Smart Grid Connected SystemSystems Engineering10.1002/sys.21645

Collaborative Intrusion Detection System in Cognitive Smart City Network (CSC-Net)

Daniel D., Preethi N., Aishwarya Jakka, Sivaraman Eswaran

Source Title: International Journal of Knowledge and Systems Science (IJKSS)12(1)

ISSN: 1947-8208|EISSN: 1947-8216|EISBN13: 9781799861829|DOI: 10.4018/IJKSS.2021010105

Cite Article Cite Article

MLA

Daniel D., et al. "Collaborative Intrusion Detection System in Cognitive Smart City Network (CSC-Net)." IJKSS vol.12, no.1 2021: pp.60-73. http://doi.org/10.4018/IJKSS.2021010105

APA

Daniel D., Preethi N., Jakka, A., & Eswaran, S. (2021). Collaborative Intrusion Detection System in Cognitive Smart City Network (CSC-Net). International Journal of Knowledge and Systems Science (IJKSS), 12(1), 60-73. http://doi.org/10.4018/IJKSS.2021010105

Chicago

Daniel D., et al. "Collaborative Intrusion Detection System in Cognitive Smart City Network (CSC-Net)," International Journal of Knowledge and Systems Science (IJKSS) 12, no.1: 60-73. http://doi.org/10.4018/IJKSS.2021010105

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Smart environment is about incorporating smart thinking in the environment and implementing the technical intervention that improvise the city's environment. Artificial intelligence (AI) provides solutions in huge technological issues in various aspects of day-to-day life such as autonomous transportation, governance, healthcare, agriculture, maintenance, logistics, and education that are automated, managed, controlled, and accessed remotely with the aid of smart devices. Cognitive computing is denoted as a next-generation AI-dependent method that gives human-computer interactions with personalized services that replicate manual behavior. Simultaneously, massive data is generated from the applications of the smart city like smart transportation, retail industry, healthcare, and governance. It is necessary to obtain a reliable, sustainable, continuous, and secure framework in the cloud centralized infrastructure. In this research article, the authors proposed the architecture of cognitive smart city network (CSC-Net) that defines how data are collected from applications of smart city and scrutinized by cognitive computing. This research article predicts the mobile edge computing solution (MEC) that permits node collaboration between internet of things (IoT) devices for providing secure and reliable communication among smart devices and fog layer, conversely fog layer and cloud layer. This proposed work helps to reduce the excessive traffic flow in smart environment with the support of node to node communication protocols. Collaborative-dependent intrusion detection system (C-IDS) is proposed to solve the data security issues in fog and cloud layers.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Collaborative Intrusion Detection System in Cognitive Smart City Network (CSC-Net)

MLA

APA

Chicago

Export Reference

Abstract

Request Access