Prevention of False Data Injection in Dataset using ML and DL

Moganarangan N.; S. Jagadeeswari; Lakshmibharathi S.; Nandhini Bagavane N.; Gokuladharshani R.

doi:10.36948/ijfmr.2023.v05i02.1768

Prevention of False Data Injection in Dataset using ML and DL

Author(s)	Moganarangan N., S. Jagadeeswari, Lakshmibharathi S., Nandhini Bagavane N., Gokuladharshani R.
Country	India
Abstract	IoT is an interconnected and allotted network of embedded systems communicating through wired and wireless communication technology. It is defined as network of physical objects or things empowered with limited computation storage and conversation capabilities as well as embedded with electronics (sensors and actuators). Nowadays there are literally masses of hundreds of Internet of things (IoT) gadgets easily available to the customers. those consist of security cameras, smart home and smart speaker systems, smart toys and infant monitors, drones, domestic appliances, routers and internet gateways, and basically some other hardware products which can transmit information and be controlled over the net. FDIA is an attack this will result in a catastrophic outcomes. False information injection attack can be executed in each dynamic and static datasets, as a present device we easily discover and prevent in a static environment. in which with the present machine even the dynamic records is made static after which detects FDIA’s. FDIA in a static environment is the existing machine, in which we seek to have few solutions for the FDIA in a dynamic environment or for time collection information. So, to enhance protection in dynamic environment is our proposed challenge towards FDIA and we create a brand-new version known as “PdD” model – Predictive Dynamic version. Proposed framework performs crucial role in live streaming records in terms of heterogeneous environment (dynamic nature). So, we use GRU set of rules because it offers better RMSE value than different AI algorithms. This detects every time there may be a few FDI attack.
Keywords	Internet of Things, False Data Injection Attack, Gated Recurrent Unit, Predictive Dynamic Model, Root Mean Square Error
Field	Computer > Data / Information
Published In	Volume 5, Issue 2, March-April 2023
Published On	2023-03-15
Cite This	Prevention of False Data Injection in Dataset using ML and DL - Moganarangan N., S. Jagadeeswari, Lakshmibharathi S., Nandhini Bagavane N., Gokuladharshani R. - IJFMR Volume 5, Issue 2, March-April 2023. DOI 10.36948/ijfmr.2023.v05i02.1768
DOI	https://doi.org/10.36948/ijfmr.2023.v05i02.1768
Short DOI	https://doi.org/gr2krg

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2 Cover Page Vol 6 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Prevention of False Data Injection in Dataset using ML and DL

Share this