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COBAST: Node Eminence State and Cooperative Bait Strategy based Route Discovery to Prevent Black and Gray Hole Attacks in Mobile Ad hoc Networks


Affiliations
1 Department of CSE, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, India
2 College of Engineering, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, India
 

Objectives: The data transmission in Mobile ad hoc networks (MANETs) usually compromises to security issues due to their dynamic topology. One of the malicious acts of the compromised nodes is Black hole attack, which becomes a significant research challenge. Methods/Analysis: We proposed a Node Eminence State and Cooperative Bait Strategy (COBAST) based Secure Route discovery to prevent the selection of route with nodes prone to black hole attack. This contribution is an extension of our earlier model ENES. The cooperative bait strategy proposed here is discovering the suspicious nodes involvement in the selected route. The experiments were done using NS2 simulator and simulated the network with presence of divergent ratios of malicious nodes. The performance of the proposal is estimated by comparing with the results obtained from other model found in contemporary literature, which is labeled as CBDA. Findings: The conventional metrics such as end to end delivery ratio, delay and routing overhead were assessed and also performed the experiments to assess the accuracy sensitivity and specificity of black hole node discovery. The obtained results for the metrics adapted were confirming the scalability and robustness of the COBAST. Novelty/Improvement: The proposed model evinced the scalability and robustness in detection of black hole attack prone nodes under minimal computational cost. The route discovered under the proposed model also found to be optimal in end to end maximum packet delivery ratio, minimum delay.

Keywords

Black Hole Attacks, COBAST, Cooperative Bait Strategy, Mobile Ad Hoc Network (MANET), Node Eminence Score.
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  • COBAST: Node Eminence State and Cooperative Bait Strategy based Route Discovery to Prevent Black and Gray Hole Attacks in Mobile Ad hoc Networks

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Authors

G. Soma Sekhar
Department of CSE, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, India
E. Sreenivas Reddy
College of Engineering, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, India

Abstract


Objectives: The data transmission in Mobile ad hoc networks (MANETs) usually compromises to security issues due to their dynamic topology. One of the malicious acts of the compromised nodes is Black hole attack, which becomes a significant research challenge. Methods/Analysis: We proposed a Node Eminence State and Cooperative Bait Strategy (COBAST) based Secure Route discovery to prevent the selection of route with nodes prone to black hole attack. This contribution is an extension of our earlier model ENES. The cooperative bait strategy proposed here is discovering the suspicious nodes involvement in the selected route. The experiments were done using NS2 simulator and simulated the network with presence of divergent ratios of malicious nodes. The performance of the proposal is estimated by comparing with the results obtained from other model found in contemporary literature, which is labeled as CBDA. Findings: The conventional metrics such as end to end delivery ratio, delay and routing overhead were assessed and also performed the experiments to assess the accuracy sensitivity and specificity of black hole node discovery. The obtained results for the metrics adapted were confirming the scalability and robustness of the COBAST. Novelty/Improvement: The proposed model evinced the scalability and robustness in detection of black hole attack prone nodes under minimal computational cost. The route discovered under the proposed model also found to be optimal in end to end maximum packet delivery ratio, minimum delay.

Keywords


Black Hole Attacks, COBAST, Cooperative Bait Strategy, Mobile Ad Hoc Network (MANET), Node Eminence Score.



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i38%2F126606