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India: Intruder Node Detection and Isolation Action in Mobile Ad Hoc Networks Using Feature Optimization and Classification Approach
- Source :
- Journal of Medical Systems
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder.
- Subjects :
- Computer science
Feature extraction
India
Medicine (miscellaneous)
MANET
Health Informatics
Intrusion detection system
computer.software_genre
Feature optimization
Health Information Management
Computer Security
Artificial neural network
Network packet
Correction
Particle swarm optimization
Energy consumption
Mobile ad hoc network
Transactional Processing Systems
Classification
Malicious node detection
Neural Networks, Computer
Data mining
computer
Classifier (UML)
Algorithms
Medical Informatics
Information Systems
Subjects
Details
- ISSN :
- 1573689X and 01485598
- Volume :
- 43
- Database :
- OpenAIRE
- Journal :
- Journal of Medical Systems
- Accession number :
- edsair.doi.dedup.....95e88cc20de0273a7c057b5ea9933efc