A novel security scheme using deep learning based low overhead localised flooding algorithm for wireless sensor networks. (26th August 2021)

• Record Type: Journal Article
• Authors: Kumar, T. Ananth; Rajmohan, R.; Adithya, M.; Sunder, R.
• Is Part Of: International journal of data science. Volume 6:Number 1(2021)
• Page Start: 19
• Page End: 32
• Publication Date: 2021-08-26
• Subjects: WANET -- wireless ad-hoc network -- deep learning -- bait message -- LOLF -- low overhead localised flooding -- dropper node -- AODV -- LSTM -- long short-term memory -- secure transmission -- dark-gap -- NS2; Big data -- Periodicals; Data mining -- Periodicals; Information storage and retrieval systems -- Periodicals; Decision making -- Data processing -- Periodicals; 005.7
• Languages: English
• ISSNs: 2053-0811

Abstract

A wireless specially appointed system is a self-sorting out, self-arranging confederation of remote frameworks. Wireless ad-hoc network gadgets (WANET) will interface and leave the system non-concurring freely, and there are no predefined customers or server. The dynamic topologies, portable correspondence's structure, decentralised control, and namelessness makes numerous-difficulties to the security of frameworks and system in a WANET domain. So, by this alternative approach is requiring a revaluation of traditional approaches to security protocols. A deep learning-based low overhead localised flooding (DL-LOLF) strategy dependent on the query localisation system is proposed. The directing packets, which increase back to a source, are disposed of to lighten superfluous rebroadcasting. The improvement while using the network algorithm is making it easier to transmit security information. The results show that the proposed technique can decrease steering overhead and medium access control (MAC) impact rate without giving up parcel conveyance proportion contrasted with existing conventions.