A Trust-Integrated RPL Protocol to Detect Blackhole Attack in Internet of Things

Internet of things (IoT) offers communication between user-to-machine and machine-to-machine. Due to their inherent characteristics of open medium, very dynamic topology, lack of infrastructure and lack of centralized management authority, IoT present serious vulnerabilities to security attacks. The routing protocol for low-power and lossy networks (RPL) does not have an inherent mechanism to detect routing attacks. Popular among these IoT attacks is blackhole attack. An attacker can exploit the routing system of RPL to launch blackhole attack against an IoT network. To secure IoT networks from blackhole attack, trust-integrated RPL protocol (TRPL) is proposed and implemented. The trust system is embedded in the RPL protocol to detect and isolate a blackhole attack while optimizing network performance. The trust is calculated from successful interaction between two nodes. The calculated trust value is considered in parent selection. TRPL demonstrates its superior performance over the standard RPL protocol and existing techniques in the detection and isolation of blackhole attacks.

Anthoc Based Trust Behavioural Network (Anthoc-tbn) Assist to Detect Blackhole Attack by Discover Secure Route

MANET is a wireless topology mainly used to grouping several mobile nodes as network. This wireless network is highly vulnerable by several security threads. Blackhole attack, a severe thread that voluntarily disrupts the healthy nodes and turn it as malicious node. So, introducing security mechanism can forcefully work together to defeat the black hole attack. In this paper, a new approach of AntHoc based trust behavioural network (AntHoc-TBN) model is introduced. This model effectively discovers the route with trust based packet transmission take place. Here, the forward ant agent performs shortest distance computation to discover the route. So that, Dijikstra algorithm is employed to detect the shortest route of all other nodes from source. Then, trust procedure is applied to find that corresponding node is trustable or not. Based on trust threshold, the respective intermediate node is computed and stored the updated value in extended data routing information (EDRI) table. Once the condition is not satisfied, then updated value in EDRI table tends to be distrust node and the concerned node is eliminated from the routing table. Now, the trusted node becomes an active path after that the packet has to be sent. Likewise, the same process is repeated for backward ant agent to transfer the Ack message from destination to source. To simulate the process, the result reveals that the AntHoc-TBN model improves its efficiency by lowest delay, packet loss and routing overhead. And also, the technique gets highest value of packet delivery ratio. The performance measures of our proposed model achieves better outcome when compared to existing techniques such as AODV, AntNet and AntHoc Net.