Experimental Evaluations of Malicious Node Detection on Wireless Sensor Network Environment
Abstract : In this modern era, each and every individual needs to utilize the communication medium for sending and receiving the respective data between source and destination entities. A Wireless Sensor Network (WSN) environments provide such facility to clients to perform a successful and range free communication between source and destination. This paper utilizes the same logic of WSN in an enhanced way, by means of adding some security metrics and associated communication strategies. The routing protocol consideration is a most import ant concern for every communication medium, in this paper a Modified Adhoc-On demand-Distance-Vector (mAODV) is introduced to perform the routing configurations in an efficient way. This proposed approach of mAODV is acquired from the logics of classical AODV model but instead of using the common transmission and reception power ratio the metrics are improvised and it is considered to be the iteration independent routing protocol, in which it consists of self-initiation features over mobile network environment as well as considering the routing metrics such as node-mobility, network link issues, data-packet missing issues and so on. This paper associates a latest technology of Internet of Things (IoT) in this WSN environment to provide a reliable communication between source and destination entities. The purpose of placing the IoT is to improvise the communication abilities between Base station and the network mobile nodes, in which the data directly communicates between the cluster node and the base station requires a huge time interval, so that the adoption of IoT provides a constant surfing power to the channel for communication as well as the routing metrics are improvised in clear manner. An integration of these details such as Internet of Things, WSN and the modified routing protocol mAODV is combinedly called as Improved Cluster enabled Routing Protocol with Fault Detection Metrics (ICRPFDM). This proposed approach of ICRPFDM provides ...
? In contrast to sensor nodes in flat ad hoc sensor networks, sensor nodes in the lowest layer of this hierarchical network do not offer multi-hop routing capability to its neighbors.
? A number of Sensor Nodes (SNs) are organized as a group and controlled by a higher layer node, the Forwarding Node (FN).
? In other words, it is possible that under certain conditions nodes cannot find the minimum number of neighboring nodes in order to be named trusted.
? It is critical to detect and isolate the compromised nodes in order to avoid being misled by the falsified information injected by the adversary.
? One of the main security issues in WSNs is manifested when malicious nodes spoof their identities and locations.
? In this paper, we propose a decentralised malicious node detection technique based on the received signal strength indicator (RSSI).
? The authors propose a non-cooperative game theory approach for handling security issues in WSNs where the game is defined between the nodes and the IDS.
? We fix all but one parameter, and study the impact of this particular parameter.
? We study the impact of neighbour density (the number of neighbouring nodes for each node) on the detection phase.
• In this paper, we proposed a novel weighted-trust evaluation based scheme to detect compromised or misbehaved nodes in wireless sensor networks.
• On top of a hierarchical WSN architecture, in this paper we proposed a novel scheme based on weighted-trust evaluation to detect malicious nodes.
• In this paper, we proposed a weighted-trust evaluation (WTE) based scheme to detect the compromised nodes by monitoring its reported data.
• They proposed to detect malicious node by comparing its output with an aggregation value.
• It demonstrates that the proposed detection algorithm is efficient for both large networks and high attack probability conditions.
? Wireless sensor networks (WSNs) are comprised of multiple sensors, which are connected to each others in order to perform collaborative or cooperative functions.
? The authors used the k-means algorithm in order to detect malicious nodes performing spoofing or Sybil attacks.
? where the nodes performing the localisation technique are represented with black stars, and the sketched four circles locate the attacker location at the centre point of their intersection area.
? WSNs are used in various applications such as environmental monitoring, health and medical monitoring, industrial monitoring, as well as many other applications.
? Nearest neighbour-based approaches can be used for detection but they are computationally expensive for WSNs.
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