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 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 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.

A Trust Model for Detecting Device Attacks in Mobile Ad Hoc Ambient Home Network

2016 ◽  
Vol 8 (2) ◽  
pp. 16-37
Author(s):  
Akinboro Solomon ◽  
Emmanuel Olajubu ◽  
Ibrahim Ogundoyin ◽  
Ganiyu Aderounmu
Keyword(s):  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Metrics ◽  
Social Trust ◽  
Trust Model ◽  
Convergence Time ◽  
Home Network ◽  
Detection Accuracy ◽  
Malicious Nodes ◽  
Mobile Ad Hoc

This study designed, simulated and evaluated the performance of a conceptual framework for ambient ad hoc home network. This was with a view to detecting malicious nodes and securing the home devices against attacks. The proposed framework, called mobile ambient social trust consists of mobile devices and mobile ad hoc network as communication channel. The trust model for the device attacks is Adaptive Neuro Fuzzy (ANF) that considered global reputation of the direct and indirect communication of home devices and remote devices. The model was simulated using Matlab 7.0. In the simulation, NSL-KDD dataset was used as input packets, the artificial neural network for packet classification and ANF system for the global trust computation. The proposed model was benchmarked with an existing Eigen Trust (ET) model using detection accuracy and convergence time as performance metrics. The simulation results using the above parameters revealed a better performance of the ANF over ET model. The framework will secure the home network against unforeseen network disruption and node misbehavior.

scholarly journals System for Malicious Node Detection in IPv6-Based Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Kresimir Grgic ◽  
Drago Zagar ◽  
Visnja Krizanovic Cik
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Security Threats ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Network Topologies ◽  
Malicious Node Detection

The trend of implementing the IPv6 into wireless sensor networks (WSNs) has recently occurred as a consequence of a tendency of their integration with other types of IP-based networks. The paper deals with the security aspects of these IPv6-based WSNs. A brief analysis of security threats and attacks which are present in the IPv6-based WSN is given. The solution to an adaptive distributed system for malicious node detection in the IPv6-based WSN is proposed. The proposed intrusion detection system is based on distributed algorithms and a collective decision-making process. It introduces an innovative concept of probability estimation for malicious behaviour of sensor nodes. The proposed system is implemented and tested through several different scenarios in three different network topologies. Finally, the performed analysis showed that the proposed system is energy efficient and has a good capability to detect malicious nodes.

scholarly journals Proposed an Algorithm for Preventing IP Spoofing DoS Attack on Neighbor Discovery Protocol of IPv6 in Link Local Network

2019 ◽  
Vol 4 (12) ◽  
pp. 65-70
Author(s):  
Md. Mustafejur Rahman ◽  
Md. Mustafizur Rahman ◽  
Saif Ibne Reza ◽  
Sumonto Sarker ◽  
Md. Mehedi Islam
Keyword(s):  
Local Network ◽  
Neighbor Discovery ◽  
Malicious Node ◽  
Dos Attack ◽  
Malicious Nodes ◽  
Ip Address ◽  
Duplicate Address Detection ◽  
Ip Spoofing ◽  
Neighbor Discovery Protocol

Duplicate Address Detection (DAD) is one of the most interesting features in IPv6. It allows nodes to connect to a network by generating a unique IP address. It works on two Neighbor Discovery (ND) messages, namely, Neighbor Solicitation (NS) and Neighbor Advertisement (NA). To verify the uniqueness of generating IP, it sends that IP address via NS message to existing hosts. Any malicious node can receive NS message and can send a spoof reply, thereby initiates a DoS attack and prevents auto configuration process. In this manner, DAD is vulnerable to such DoS attack. This study aims to prevent those malicious nodes from sending spoof reply by securing both NS and NA messages. The proposed Advanced Bits Security (ABS) technique is based on Blake2 algorithm and introducing a creative option called ABS field that holds the hash value of tentative IP address and attached to both NA and NS message. We expect the ABS technique can prevent spoof reply during DAD procedure in link local network and can prevent DoS attack

Social and QoS based trust model for secure clustering for wireless body area network

2020 ◽  
pp. 002072092095313
Author(s):  
Sangeetha Ramaswamy ◽  
Jasmine Norman
Keyword(s):  
Energy Consumption ◽  
Service Providers ◽  
Wireless Body Area Network ◽  
High Energy ◽  
Trust Model ◽  
Base Station ◽  
Area Network ◽  
Body Area ◽  
Malicious Nodes ◽  
Main Challenge

Wireless Body Area Networks (WBAN) is an emerging technology, a subset of Wireless Sensor Network. WBAN is a collection of pieces of tiny wireless body sensors with small computational capability and communicates short distance using ZigBee or Bluetooth. The main application of WBAN is in healthcare industry like remote patient monitoring. The small pieces of sensor monitor health factors like body temperature, pulse rate, ECG, heart rate etc., and communicate it to the base station or central coordinator for aggregation or for data computation. The final data is communicated to remote monitoring devices through internet or cloud service providers. The main challenge of this technology is dead nodes due to high energy consumption with all the wireless node working on battery. Minimization of the energy consumption extends life of the network. Security is another major challenge. There are possibilities of internal attacks being executed by malicious nodes, creating problems for the network. This paper proposes a model which provides solution for extending the life span of the network by minimizing energy consumption and also proposes model to provide solution for internal soft attacks created within the network through calculation or trust, computation among nodes to identify malicious nodes with the help of social-and QoS-based trust computation for secure clustering and communication. The proposed model is compared with LEACH and LEACH-MM protocol and performance is measured with various parameters.

Identification and Prevention of Joint Gray Hole and Black Hole Attacks

2019 ◽  
Vol 10 (2) ◽  
pp. 80-90
Author(s):  
Munesh C. Trivedi ◽  
Sachin Malhotra
Keyword(s):  
Black Hole ◽  
Ad Hoc ◽  
Destination Node ◽  
Route Discovery ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Data Routing ◽  
Malicious Activity ◽  
Hoc Networks ◽  
Discovery Method

Ad-hoc networks consist of a set of autonomous communicating devices that can communicate with each other by establishing multi-hop radio connections, and these connections are maintained in a localized manner. In these types of networks, especially where the nature of communicating nodes is mobile, e.g., MANETs, maintaining security remains a serious challenge due to their wireless, open, and shared communication medium, reliance on cooperative algorithms, dynamically adaptable topologies, an absence of centralized watching points, etc. Most of the existing protocols, utilized for routing in MANETs are susceptible to diverse varieties of attacks. However, it is also susceptible to the well-known gray and black hole attacks. In these types of attacks, malicious nodes are incorrectly advertised as sensible ways to a destination node throughout the route discovery method. This attack becomes complicated when a bunch of malicious nodes are acting together. In this work, a novel mechanism is introduced to identify the malicious node or nodes that have tried to perform malicious activity. Here, identification is made by their increased data routing information (DRI).

A Comparative Evaluation of Mining Techniques to Detect Malicious Node in Wireless Sensor Networks

2017 ◽  
Vol 7 (2) ◽  
pp. 42-53
Author(s):  
Mandeep Singh ◽  
Navjyot Kaur ◽  
Amandeep Kaur ◽  
Gaurav Pushkarna
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Random Forest ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Human Management ◽  
Wireless Interface ◽  
Detection Mechanisms

Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.

scholarly journals Construction of Internet of things trusted group based on multidimensional attribute trust model

2021 ◽  
Vol 17 (1) ◽  
pp. 155014772198988
Author(s):  
Jinghan Chen ◽  
Bei Gong ◽  
Yubo Wang ◽  
Yu Zhang
Keyword(s):  
Internet Of Things ◽  
Trust Model ◽  
Group Signature ◽  
Malicious Nodes ◽  
Maintenance Mechanism ◽  
Proposed Model ◽  
Dynamic Maintenance ◽  
Trust Metric ◽  
Network Overhead ◽  
Trust Measurement

Accurate prediction of the trust relationship is the basis for trusted access and secure interaction between Internet of things nodes. To evaluate the degree of trust, a trust metric is assigned to every node depending on its several attributes. Normal nodes in Internet of things tend to suffer collusion attacks from malicious nodes; thus, the accuracy of the trust measurement decreases. To enhance the security of interaction between massive Internet of things nodes, we propose a multidimensional attribute trust model and a dynamic maintenance mechanism of a trusted group. The proposed model provides a reference for the selection and evaluation of node multidimensional attribute factors to adapt to different Internet of things application scenarios. The dispersion of satisfaction records is used to discover abnormal data and weaken its influence on the calculation of the node’s comprehensive trust evaluation. The construction of trusted groups provides an architectural foundation for the application of group signature that maintains low network overhead. The performance of multidimensional attribute trust model and dynamic maintenance mechanism is verified using Netlogo. Simulation results show the efficiency of the proposed model to classify the malicious nodes and honest nodes, as well as to build a trusted group that could ensure honest nodes occupy the major proportion.

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