A Novel Defence Scheme to Prevent Malicious Node in MANET

2018 ◽  
Author(s):  
C. Senthil Kumar ◽  
R. Nisha ◽  
T. Manikandan ◽  
S. Muthu Ranjith Kumar
Keyword(s):  
Malicious Node

Detecting Wormhole Attack on Data Aggregation in Hierarchical WSN

2017 ◽  
Vol 11 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Mukesh Kumar ◽  
Kamlesh Dutta
Keyword(s):  
Energy Efficiency ◽  
Data Aggregation ◽  
Low Cost ◽  
Attack Detection ◽  
Sensor Nodes ◽  
Network Data ◽  
Light Weight ◽  
Malicious Node ◽  
Combine Data ◽  
Wormhole Attack

Wireless networks are used by everyone for their convenience for transferring packets from one node to another without having a static infrastructure. In WSN, there are some nodes which are light weight, small in size, having low computation overhead, and low cost known as sensor nodes. In literature, there exists many secure data aggregation protocols available but they are not sufficient to detect the malicious node. The authors require a better security mechanism or a technique to secure the network. Data aggregation is an essential paradigm in WSN. The idea is to combine data coming from different source nodes in order to achieve energy efficiency. In this paper, the authors proposed a protocol for worm hole attack detection during data aggregation in WSN. Main focus is on wormhole attack detection and its countermeasures.

scholarly journals An Opinion Evolution Model Based on Heterogeneous Benefit with Malicious Nodes Added

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Junwei Zhao ◽  
Xi Chen
Keyword(s):  
Knowledge Exchange ◽  
Willingness To Communicate ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Highly Educated ◽  
Education Levels ◽  
Node Spreading ◽  
The Game Theory ◽  
Average Education ◽  
Different Levels

Individuals with different levels of education have substantial differences in their willingness to communicate with malicious nodes in a group; thus, the results of evolution of opinions tend to differ significantly. In this study, malicious nodes, driven by the benefits of a game, were added to groups of individuals with different levels of education, and a theoretical model of the game theory of group opinions that introduces malicious nodes was established. The influence of the proportion of malicious node spreading messages, the extent of tampering when malicious nodes spread messages, and the distribution of education levels in the group on the evolution of group opinions were considered. It was found that the rate of evolution of group opinions declined in groups with higher average education levels. The results of this study can be used to explain the phenomenon of fewer knowledge exchange behaviors in communities with high education levels, as is found in actual sociology. The reason is that highly educated individuals are more affected by distorted news when communicating. Therefore, the loss of communication with malicious nodes is greater, resulting in lower vigilance and willingness to communicate.

scholarly journals A Framework to Systematically Analyse the Trustworthiness of Nodes for Securing IoV Interactions

2020 ◽  
Vol 21 (3) ◽  
pp. 451-462
Author(s):  
Indu Bhardwaj ◽  
Sibaram Khara ◽  
Priestly Shan
Keyword(s):  
Standard Deviation ◽  
Essential Role ◽  
Trust Model ◽  
Review Literature ◽  
Malicious Node ◽  
Threshold Values ◽  
Malicious Nodes

Trust plays essential role in any securing communications between Vehicles in IOV. This motivated us to design a trust model for IoV communication. In this paper, we initially review literature on IoV and Trust and present a hybrid trust model that separates the malicious and trusted nodes to secure the interaction of vehicle in IOV. Node segregation is done using value of statistics (St). If St of each node lies in the range of mean (m) plus/minus 2 standard deviation (SD) of PDR then nodes behaviour is considered as normal otherwise malicious. The simulation is conducted for different threshold values. Result depicts that PDR of trusted node is 0.63 that is much higher than the PDR of malicious node that is 0.15. Similarly, the average no. of hops and trust dynamics of trusted nodes are higher than that of malicious node. So, on the basis of values of PDR, number of available hops and trust dynamics, the malicious nodes can be clearly identified and discarded.

scholarly journals Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiming Zhang ◽  
Yu Yang ◽  
Wei Yang ◽  
Fuying Wu ◽  
Ping Li ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evaluation Model ◽  
Original Data ◽  
Base Station ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Trust Value

The current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path; however, for the reliability of data transmission, many sensor data are transmitted by multipath in wireless sensor networks. In order to detect and locate malicious nodes in multiple paths, in this paper, we present a homomorphic fingerprinting-based detection and location of malicious nodes (HFDLMN) scheme in wireless sensor networks. In the HFDLMN scheme, using homomorphic fingerprint and coding technology, the original data is divided into n packets and sent to the base station along n paths, respectively; the base station determines whether there are malicious nodes in each path by verifying the validity of the packets; if there are malicious nodes in one or more paths, the location algorithm of the malicious node is implemented to locate the specific malicious nodes in the path; if all the packets are valid, the original data is recovered. The HFDLMN scheme does not need any complex evaluation model to evaluate and calculate the trust value of the node, nor any monitoring nodes. Theoretical analysis results show that the HFDLMN scheme is secure and effective. The simulation results demonstrate promising outcomes with respect to key parameters such as the detection probability of the malicious path and the locating probability of the malicious node.

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