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Improvement of False Report Detection Performance Based on Invalid Data Detection Using Neural Network in WSNF
- Source :
- International journal of Computer Networks & Communications. 10:21-34
- Publication Year :
- 2018
- Publisher :
- Academy and Industry Research Collaboration Center (AIRCC), 2018.
-
Abstract
- WSN consists of a number of nodes and base stations and is used for event monitoring in various fields such as war situations, forest fires, and home networks. WSN sensor nodes are placed in fields that are difficult for users to manage. It is therefore vulnerable to attackers, and attackers can use false nodes or MAC injection attacks through the hijacked nodes to reduce the lifetime of the network or trigger false alarms. In order to prevent such attacks, several security protocols have been proposed, and all of them have been subjected to MAC-dependent validation, making it impossible to defend against false report attacks in extreme attack circumstances. As attacks have recently become more diverse and more intelligent, WSNs require intelligent methods of security. Based on the report information gathered from the base station, the proposed method provides a technique to prevent attacks that may occur where all MAC information is damaged by carrying out verification of a false report attack through the machine learning based prediction model and the evaluation function.
- Subjects :
- Event monitoring
Artificial neural network
Computer Networks and Communications
Computer science
ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS
Cryptographic protocol
Computer security
computer.software_genre
Evaluation function
Base station
Hardware and Architecture
Detection performance
Communications protocol
computer
Wireless sensor network
Subjects
Details
- ISSN :
- 09749322 and 09752293
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- International journal of Computer Networks & Communications
- Accession number :
- edsair.doi...........4405c34f61212d7a0d283f9369618b7c