International Journal of Computer Networks and Applications

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Co-Ordinated Blackhole and Grayhole Attack Detection Using Smart & Secure Ad Hoc On-Demand Distance Vector Routing Protocol in MANETs


Affiliations
1 Department of Electronics and Communication Engineering, Atria Institute of Technology, Bangalore, India
2 Department of Electronics and Telecommunication Engineering, Ramaiah Institute of Technology, Bangalore, India
 

Mobile Ad Hoc Network (MANET) devices are powered from battery and due to infrastructure-less feature, the security and energy consumption are major concerns. Most of the researchers have assumed that the Cluster Head (CH) nodes are benign and frequently undergo cluster re-election, which shortens the network lifetime. Smart & Secure Ad Hoc On-Demand Distance Vector algorithm (S2-AODV) is proposed with secondary CH (S-CH), primary CH (P-CH) and a super cluster head (SCH) node along with the other nodes. Modified-AODV (M-AODV) is used for neighbor discovery. Weight-based clustering algorithm is proposed, with the primary and a secondary CH node to enhance the network efficiency. S2-AODV enhances security using Honey-pot AODV (H-AODV) and avoids the CH re-election process enhancing the overall network lifetime. The proposed algorithm works in off-line mode and on-line mode. In off-line mode the various Wi-Fi parameters like Received Signal Strength Indicator (RSSI), transmission power, battery level, distance and number of transmissions retries are collected from each CH node in the network. A look-up table indicating the transmission power (TXP) to be set by the CH nodes is determined by machine learning (ML) algorithms. This table is circulated among every CH node by SCH node in the network. Due to this process the intermittent reelection of the P-CH and S-CH nodes can be avoided, enhancing the network lifetime. In on-line mode, SCH executes H-AODV to identify and remove the malicious CH (black hole / gray hole) nodes (ns-2.34).

Keywords

MANETs, AODV, M-AODV, H-AODV, Cluster, Black Hole Attack.
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  • Co-Ordinated Blackhole and Grayhole Attack Detection Using Smart & Secure Ad Hoc On-Demand Distance Vector Routing Protocol in MANETs

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Authors

Sampada H. K.
Department of Electronics and Communication Engineering, Atria Institute of Technology, Bangalore, India
Shobha K. R.
Department of Electronics and Telecommunication Engineering, Ramaiah Institute of Technology, Bangalore, India

Abstract


Mobile Ad Hoc Network (MANET) devices are powered from battery and due to infrastructure-less feature, the security and energy consumption are major concerns. Most of the researchers have assumed that the Cluster Head (CH) nodes are benign and frequently undergo cluster re-election, which shortens the network lifetime. Smart & Secure Ad Hoc On-Demand Distance Vector algorithm (S2-AODV) is proposed with secondary CH (S-CH), primary CH (P-CH) and a super cluster head (SCH) node along with the other nodes. Modified-AODV (M-AODV) is used for neighbor discovery. Weight-based clustering algorithm is proposed, with the primary and a secondary CH node to enhance the network efficiency. S2-AODV enhances security using Honey-pot AODV (H-AODV) and avoids the CH re-election process enhancing the overall network lifetime. The proposed algorithm works in off-line mode and on-line mode. In off-line mode the various Wi-Fi parameters like Received Signal Strength Indicator (RSSI), transmission power, battery level, distance and number of transmissions retries are collected from each CH node in the network. A look-up table indicating the transmission power (TXP) to be set by the CH nodes is determined by machine learning (ML) algorithms. This table is circulated among every CH node by SCH node in the network. Due to this process the intermittent reelection of the P-CH and S-CH nodes can be avoided, enhancing the network lifetime. In on-line mode, SCH executes H-AODV to identify and remove the malicious CH (black hole / gray hole) nodes (ns-2.34).

Keywords


MANETs, AODV, M-AODV, H-AODV, Cluster, Black Hole Attack.

References





DOI: https://doi.org/10.22247/ijcna%2F2024%2F224433