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

Sampada H. K., S. K R
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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 (S 2 -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. S 2 -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).
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利用城域网中的智能安全 Ad Hoc 按需距离矢量路由协议协同检测黑洞和灰洞攻击
- 移动 Ad Hoc 网络(MANET)设备由电池供电,由于其无基础设施的特点,安全性和能源消耗成为主要问题。大多数研究人员都假设簇头(CH)节点是良性的,并经常进行簇重选,从而缩短了网络寿命。智能安全 Ad Hoc 按需距离矢量算法(S 2 -AODV)被提出来,该算法包含二级 CH(S-CH)、一级 CH(P-CH)和一个超级簇头(SCH)节点以及其他节点。修改后的 AODV(M-AODV)用于发现邻居。提出了基于权重的聚类算法,通过主 CH 节点和副 CH 节点来提高网络效率。S 2 -AODV利用蜜罐AODV(H-AODV)增强了安全性,并避免了CH重选过程,从而提高了整体网络寿命。所提出的算法可在离线模式和在线模式下工作。在离线模式下,从网络中的每个 CH 节点收集各种 Wi-Fi 参数,如接收信号强度指示器(RSSI)、传输功率、电池电量、距离和传输重试次数。通过机器学习(ML)算法确定一个查找表,显示 CH 节点要设置的传输功率(TXP)。该表通过网络中的 SCH 节点在每个 CH 节点之间分发。通过这一过程,可以避免间歇性地重新选择 P-CH 和 S-CH 节点,从而提高网络寿命。在联机模式下,SCH 会执行 H-AODV 以识别并清除恶意 CH 节点(黑洞/灰洞)(ns-2.34)。
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来源期刊
International Journal of Computer Networks and Applications
International Journal of Computer Networks and Applications Computer Science-Computer Science Applications
CiteScore
2.30
自引率
0.00%
发文量
40
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