scholarly journals A Data Driven Trust Mechanism Based on Blockchain in IoT Sensor Networks for Detection and Mitigation of Attacks

Author(s):  
Sivaganesan D

Utilization of smart applications in various domains is facilitated pervasively by sensor nodes (SN) that are connected in a wireless manner and a number of smart things. Hazards due to internal and external attacks exist along with the advantages of the smart things and its applications. Security measures are influenced by three main factors namely scalability, latency and network lifespan, without which mitigation of internal attacks is a challenge. The deployment of SN based Internet of things (IoT) is decentralized in nature. However, centralized solutions and security measures are provided by most researchers. A data driven trust mechanism based on blockchain is presented in this paper as a decentralized and energy efficient solution for detection of internal attacks in IoT powered SNs. In grey and black hole attack settings, the message overhead is improved using the proposed model when compared to the existing solutions. In both grey and black hole attacks, the time taken for detection of malicious nodes is also reduced considerably. The network lifetime is improved significantly due to the enhancement of these factors.

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 23
Author(s):  
Noshina Tariq ◽  
Muhammad Asim ◽  
Farrukh Aslam Khan ◽  
Thar Baker ◽  
Umair Khalid ◽  
...  

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.


2020 ◽  
Vol 13 (39) ◽  
pp. 4092-4108
Author(s):  
M Rajasekaran

Objectives: To propose a suitable algorithm for improving the network lifetime of Wireless Sensor Networks (WSNs). Methods/Findings: We proposed a suitable Location and Energy Aware Trusted Distance Source Routing (LEATDSR) algorithm. Here, the energy consumption, location and the data quality are equalized by the Quality of Service (QoS) based routing algorithms. In addition to this algorithm, an existing clustering algorithm is also incorporates for grouping the sensor nodes based on the trust, location, energy and distance. In this LEATDSR is capable of deciding the evaluation metrics which express the QoS. Moreover, a new trust mechanism is also introduced in this model that incorporates multi-attributes of various sensor nodes in terms of communication, data, energy, and recommendation. This new trust mechanism relies on an enhanced sliding window time by considering the occurrences of attack frequency for facilitating the discovery of anomalous behaviours of attackers. The enhanced energy utilization is established within the sensor nodes for performing the active data transmission. The performance of the proposed model is evaluated by conducting various experiments in a simulation environment which creates by using NS2. From the experiments conducted in this work, the average packet transfer rate is increased drastically when compared to existing models available in the literature.


Wireless Sensor Network has become one of the most emerging areas of research in recent days. WSNs have been applied in a variety of application areas such as military, traffic surveillance, environment monitoring and so on. Since WSN is not a secure network and each sensor node can be compromised by the intruder. There are plenty of security threats in sensor networks like Black hole Attack, Wormhole attack, Sinkhole attack. Recently, there are so many algorithms are proposed to detect or to prevent attack by the researchers. Still, the research is continuing to evaluate sensor nodes' trust and reputation. At present to monitor nodes’ behavior direct and indirect trust values are used and most of the detection method uses additional nodes to detect an attack. These method increases the cost and also overhead. This paper proposed a method which detects the Black hole attack without using any additional node to monitor the network. The proposed work uses Attacker Detection metric (AD metric) to detect malicious node based on the average sequence number, time delay and reliability. OLSR protocol is used for routing which improves the network lifetime by minimizing the packet flooding. Besides, to ensure reliable data transmission Elliptical Curve Digital Signature Algorithm is used. Simulation results are obtained and show malicious nodes are eliminated using AD metrics


Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1659
Author(s):  
Kyeongsun Cho ◽  
Youngho Cho

In Wireless Sensor Networks (WSNs), the Trust Mechanism (TM) is used to defend against insider attacks by measuring the trustworthiness of all inside sensor nodes in the network. Thus, each sensor node with TM observes its neighbor nodes’ behaviors, evaluates their trustworthiness as numeric trust values, and captures untrustworthy nodes as inside attackers. Although the defense performance of trust mechanisms can be further improved by sharing the information about inside attackers detected by TM with all sensor nodes, the detected inside attacker list must be securely shared with and stored in all sensor nodes in the WSN. However, according to our survey, we observed that most existing studies simply assume that the communication channel for sharing the attacker detection list is reliable and trusted even in the presence of inside attackers in the WSN. In this paper, we propose and implement a proactive defense model that integrates the HyperLedger Fabric and trust mechanism to defend against inside attackers by securely sharing the detected inside attacker list with all sensor nodes in the WSN. In addition, we conduct comparative experiments to show that our proposed model can better defend against inside attackers than an existing trust mechanism. According to our experimental results, our proposed model could lower the attack damage (the number of packet drops) caused by an inside packet drop attacker by 59 to 67% compared to an existing trust mechanism.


Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 411
Author(s):  
Saba Awan ◽  
Nadeem Javaid ◽  
Sameeh Ullah ◽  
Asad Ullah Khan ◽  
Ali Mustafa Qamar ◽  
...  

In this paper, an encryption and trust evaluation model is proposed on the basis of a blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains, respectively. However, inauthentic nodes utilize the network’s resources and perform malicious activities. Moreover, the SNs have limited energy, transmission range and computational capabilities, and are attacked by malicious nodes. Afterwards, the malicious nodes transmit wrong information of the route and increase the number of retransmissions due to which the SNs’ energy is rapidly consumed. The lifespan of the wireless sensor network is reduced due to the rapid energy dissipation of the SNs. Furthermore, the throughput increases and packet loss increase with the presence of malicious nodes in the network. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. Moreover, the Rivest–Shamir–Adleman (RSA), a cryptosystem that provides asymmetric keys, is used for securing data transmission. The simulation results show the effectiveness of the proposed model in terms of high packet delivery ratio.


2019 ◽  
Vol 8 (3) ◽  
pp. 1197-1203

The internet of things is the self-configuring type of network in which sensor nodes join or leave the network. The version number is the active type of attack possible in DODAG protocol of IoT. The Shield technique is proposed in the previous research work for the mitigation of version number attack in the network. It is very much complex for the detection of malicious nodes. In this research work, the trust based mechanism is proposed for the isolation of version number attack. The trust based mechanism calculates trust of each sensor node. The sensor nodes with the least trust are identified from the network as malicious nodes. The proposed mechanism is implemented in network simulator version 2. The trust based mechanism and shied based techniques are implemented and results are analyzed in terms of throughput, delay, control message overhead and average power consumption. It is analyzed that in terms of all parameters trust mechanism performs well as compared to shield based technique


2016 ◽  
Vol 1 (2) ◽  
pp. 1-7
Author(s):  
Karamjeet Kaur ◽  
Gianetan Singh Sekhon

Underwater sensor networks are envisioned to enable a broad category of underwater applications such as pollution tracking, offshore exploration, and oil spilling. Such applications require precise location information as otherwise the sensed data might be meaningless. On the other hand, security critical issue as underwater sensor networks are typically deployed in harsh environments. Localization is one of the latest research subjects in UWSNs since many useful applying UWSNs, e.g., event detecting. Now day’s large number of localization methods arrived for UWSNs. However, few of them take place stability or security criteria. In purposed work taking up localization in underwater such that various wireless sensor nodes get localize to each other. RSS based localization technique used remove malicious nodes from the communication intermediate node list based on RSS threshold value. Purposed algorithm improves more throughput and less end to end delay without degrading energy dissipation at each node. The simulation is conducted in MATLAB and it suggests optimal result as comparison of end to end delay with and without malicious node.


2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.


2014 ◽  
Vol 25 (05) ◽  
pp. 563-584 ◽  
Author(s):  
PARTHA SARATHI MANDAL ◽  
ANIL K. GHOSH

Location verification in wireless sensor networks (WSNs) is quite challenging in the presence of malicious sensor nodes, which are called attackers. These attackers try to break the verification protocol by reporting their incorrect locations during the verification stage. In the literature of WSNs, most of the existing methods of location verification use a set of trusted verifiers, which are vulnerable to attacks by malicious nodes. These existing methods also use some distance estimation techniques, which are not accurate in noisy channels. In this article, we adopt a statistical approach for secure location verification to overcome these limitations. Our proposed method does not rely on any trusted entities and it takes care of the limited precision in distance estimation by using a suitable probability model for the noise. The resulting verification scheme detects and filters out all malicious nodes from the network with a very high probability even when it is in a noisy channel.


Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Ágota Bányai ◽  
Tamás Bányai ◽  
Béla Illés

The globalization of economy and market led to increased networking in the field of manufacturing and services. These manufacturing and service processes including supply chain became more and more complex. The supply chain includes in many cases consignment stores. The design and operation of these complex supply chain processes can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on metaheuristic algorithms. This research proposes an integrated supply model based on consignment stores. After a careful literature review, this paper introduces a mathematical model to formulate the problem of consignment-store-based supply chain optimization. The integrated model includes facility location and assignment problems to be solved. Next, an enhanced black hole algorithm dealing with multiobjective supply chain model is presented. The sensitivity analysis of the heuristic black hole optimization method is also described to check the efficiency of new operators to increase the convergence of the algorithm. Numerical results with different datasets demonstrate how the proposed model supports the efficiency, flexibility, and reliability of the consignment-store-based supply chain.


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