Biblio
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An Enhanced Approach for Attack Detection in VANETs Using Adaptive Neuro-Fuzzy System. 2019 International Conference on Automation, Computational and Technology Management (ICACTM). :191—197.
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2019. Vehicular Ad-hoc Networks (VANETs) are generally acknowledged as an extraordinary sort of Mobile Ad hoc Network (MANET). VANETs have seen enormous development in a decade ago, giving a tremendous scope of employments in both military and in addition non-military personnel exercises. The temporary network in the vehicles can likewise build the driver's capability on the road. In this paper, an effective information dispersal approach is proposed which enhances the vehicle-to-vehicle availability as well as enhances the QoS between the source and the goal. The viability of the proposed approach is shown with regards to the noteworthy gets accomplished in the parameters in particular, end to end delay, packet drop ratio, average download delay and throughput in comparison with the existing approaches.
Wormhole Attack Detection using Energy Model in MANETs. 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC). :264—268.
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2019. The mobile ad-hoc networks comprised of nodes that are communicated through dynamic request and also by static table driven technique. The dynamic route discovery in AODV routing creates an unsecure transmission as well as reception. The reason for insecurity is the route request is given to all the nodes in the network communication. The possibility of the intruder nodes are more in the case of dynamic route request. Wormhole attacks in MANETs are creating challenges in the field of network analysis. In this paper the wormhole scenario is realized using high power transmission. This is implemented using energy model of ns2 simulator. The Apptool simulator identifies the energy level of each node and track the node of high transmission power. The performance curves for throughput, node energy for different encrypted values, packet drop ratio, and end to end delay are plotted.