Detection and Removal Of Node Isolation Attack In OLSR Protocol Using Imaginary Nodes with Neighbour Response in MANET

Gaikwad, Kimaya Subhash; Waykar, Sanjay B.

doi:10.1109/iccubea.2017.8463762

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…At the same time, the OLSR protocol retains the routes of all destination nodes to cope with large-scale node exchanges, making it suitable for large and dense networks [50]. The control messages of this protocol do not rely on reliable transmission, but on periodic delivery by nodes, so it can cope with a loss of some packets occasionally [51,52].…”

Section: Olsr Protocolmentioning

confidence: 99%

A multi-objective optimized OLSR routing protocol

Wei,

Wu,

et al. 2024

PLoS ONE

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The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics.

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Section: Olsr Protocolmentioning

confidence: 99%

A multi-objective optimized OLSR routing protocol

Wei,

Wu,

et al. 2024

PLoS ONE

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“…l l (10) The PO 33 is efficient in solving engineering problems and leads to the global optimal solution. As per PO, the update position is expressed as…”

Section: Substitutementioning

confidence: 99%

“…Multi‐UAV comprises various wireless communication models, camera, server sensors, and Global Positioning System (GPS) to accumulate and send huge‐sized data to the ground station 7 . Quicker deployment of Wireless Sensor Network (WSN) and the capability of adapting UAV in Internet of Everything (IoE) through the wireless network can be subject to different attacks, like key impersonation and replay attacks 8–10 . The easier deployment and mobility of UAV 11 has given rise to novel kinds of smart attacks in wireless technologies that perform as spoofers, eavesdroppers, 12 and jammers.…”

Section: Introductionmentioning

confidence: 99%

Intelligent agent and optimization‐based deep residual network to secure communication in UAV network

Francelin

Daniel

Velliangiri³

2022

Int J of Intelligent Sys

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Unmanned Aerial Vehicle (UAV) is adapted as a novel unit in upcoming wireless infrastructures wherein UAVs can play various roles in different applications. The UAV is utilized for navigating commands to attain surveillance. However, routing and localization are the major issues that own higher mobility and unstable links. This paper devises a novel technique for initiating secured communication in a UAV network (UAVN). At first, the simulation of the UAVN is done. After that, the data transmission is carried out amongst nodes using a routing path; hence, an optimal routing path is formed using the newly devised Tunicate Swarm Political Optimization (TSPO) algorithm. The newly devised TSPO algorithm integrates the Tunicate Swarm Algorithm and Political Optimizer. In addition, the data communication is done using an evaluation and monitoring agent. In addition, the malicious detection is done by a decision‐making agent using the deep residual network (DRN). Here, the newly devised TSPO algorithm is used to train the deep residual. The input parameters considered in the DRN are round trip time, signal strength, packet delivery, packet size, and the number of incoming packets. Once a DRN classifies the attack and genuine users, the defensive agent is employed for attack mitigation. Thus, the attacks in data are mitigated by the defensive agents. The newly devised TSPO‐based DRN obtained enhanced performance with the smallest delay of 0.0014 s, the highest detection rate of 0.9325%, and the highest packet delivery ratio of 11491.2%.

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“…establishment of IoT has augmented the application of networked smart homes. The expansion of smart devices using IoT generates accessibility, comfortability, and security 7,8 in life. IoT poses several benefits, which involved flexibility, cost reduction, and energy consumption.…”

mentioning

confidence: 99%

Atom search sunflower optimization for trust‐based routing in internet of things

Jadhav¹,

Joshi

2020

Int J Numerical Modelling

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Internet of Things (IoT) is a ubiquitous network that assists the system to monitor and organize the world by processing, collection, and analysis of produced data by IoT nodes. The IoT is susceptible to numerous attacks, wherein the sinkhole attack is the most destructive one, which affects the communication amongst network devices. The security of the IoT is affected due to the lack of dynamic network topology, centralized control, and network communications, so that the trust-based routing is important. The conventional trust-based routing strategies are ineffective to offer protection against attack and thus, trust management remains a major challenge in routing. This paper proposes a novel technique namely Atom Search Sunflower Optimization (ASSFO) for providing trust-based routing in IoT network. The proposed ASSFO is designed by incorporating Sunflower Optimization (SFO) in Atom Search Optimization (ASO). The method utilizes several trust factors for determining and isolating attacks while optimizing the performance of the network. The trust factors considered in the method include indirect trust, recent trust, integrity factor, direct trust, and availability factor. Moreover, the trusted nodes are selected using the trust factor and further used for secure routing using the proposed ASSFO algorithm. The proposed ASSFO outperformed other methods with maximal energy of 0.908, maximal trust of 0.845, maximal PDR of 0.907, and maximal throughput of 0.909.

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Detection and Removal Of Node Isolation Attack In OLSR Protocol Using Imaginary Nodes with Neighbour Response in MANET

Cited by 12 publications

References 2 publications

A multi-objective optimized OLSR routing protocol

A multi-objective optimized OLSR routing protocol

Intelligent agent and optimization‐based deep residual network to secure communication in UAV network

Atom search sunflower optimization for trust‐based routing in internet of things

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