A Trust-Based Security Model to Detect Misbehaving Nodes in Internet of Things (IoT) Environment using Logistic Regression

Prathapchandran, K.; Janani, T.

doi:10.1088/1742-6596/1850/1/012031

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…Logistic Regression (LoR): A study in [113] investigated a trust model based on LoR that provides an accurate method in routing protocol for lossy network to classify the non-trustworthy nodes. The proposed model employed the LoR technique to identify the behavior of the nodes based on the combined trust values.…”

Section: Direct Trust Evaluation Modelsmentioning

confidence: 99%

“…However, it is ineffective for low-dimension data. Janani et al [113] developed an RF-based model that provided security to the IoT network and enhanced the trusted routing of the IoT scenario by identifying the sinkhole attack. The developed model was limited to a single attack and did not mention other privacy issues or attacks on IoT devices.…”

Section: Direct Trust Evaluation Modelsmentioning

confidence: 99%

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IoT trust and reputation: a survey and taxonomy

et al. 2023

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IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilised across the globe by the end of 2030. To maximise the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices’ features, such as device’s role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organise the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics,e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.

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Section: Direct Trust Evaluation Modelsmentioning

confidence: 99%

Section: Direct Trust Evaluation Modelsmentioning

confidence: 99%

IoT trust and reputation: a survey and taxonomy

et al. 2023

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“…On the contrary, video capture is implemented to record the current gas concentration. After giving an alarm, the current gas concentration data is stored and analyzed [36].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Online Monitoring of Ecoenvironmental Pollution Data in Internet Environment

2022

Wireless Communications and Mobile Computing

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In view of the problems of poor air quality and serious soil pollution in the current ecological environment, an online monitoring of ecological environment pollution data in the Internet environment is proposed. The concentration of pollution sources that may appear in the atmospheric environment is analyzed through the analysis coefficient of ecological environment pollutants, and the specific grade data corresponding to the air quality index is determined. Observation and mapping are carried out for the area to be monitored, and then, the monitoring points are determined, the monitoring equipment is arranged, and the soil profile of the area is sampled in the monitoring area. The self-organizing neural network model is used to conduct spatial cluster analysis on the pollution of ecological environment pollution areas, evaluate the sensitivity of soil pollution, and build a land ecological pollution monitoring information model. Based on the Internet of Things technology and the comprehensive use of GPS, sensors, and other technologies and equipment, the framework of monitoring technology is constructed, and the online monitoring of ecological environment pollution data is completed. The experimental results show that the proposed method has high data accuracy in different locations and different seasons, which is consistent with the actual situation; because of the maturity of the Internet of Things technology, compared with the literature method, the method in this paper can provide a cost-effective data foundation, which is more cost-effective, and save time and energy.

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“…(21) reviewed different strategies for intrusion detection systems in IoT and addressed the classification of attacks in IoT. In (22) proposed a model to identify misbehaving nodes in RPL black hole attacks to ensure authentication. In (23) provides all the information about RPL with formulas and https://www.indjst.org/ definitions.…”

Section: Introductionmentioning

confidence: 99%

Secured Technique to Detect and Avoid Malicious Nodes in Internet of Things

Newton¹,

Samuel²

2022

IJST

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Objectives: To detect rank attacks during topology establishment and updated the RPL Destination Oriented Directed Acyclic Graph (DODAG) formation algorithm. The algorithm's distributed module runs across all participating nodes, while the centralized module runs in the sink. Methods: The integrity and authenticity of control messages transmitted among two nodes and the sink are verified using a lightweight Hashed Message Authentication Code -Light-weight One-way Cryptographic Hash Algorithm (HMAC-LOCHA). The Secured Technique to Detect and Avoid Malicious Nodes (STDAMN) technique is proposed to overcome the rank attack of the nodes. Findings:The proposed scheme STDAMN outperforms the LEADER and SBIDS schemes when considering 50% malicious nodes, the accuracy rate of STDAMN is 3% higher than LEADER and 17% higher than SBIDS in Security mode whereas it is 4% and 14% higher in non-security mode respectively in the decreased rank attack. Again considering 50% malicious nodes, the accuracy rate of STDAMN is 2% higher than LEADER and 13% higher than SBIDS in with-Security mode whereas it is 2.2% and 16.1% higher in without-security mode respectively in the increased rank attack. Also indeed, the false positive rate for STDAMN is lower by 72.5% and 72%, 15.3% and 21.2% whereas the false negative rate for STDAMN is lower by 77.2% and 62.1%, 32.5%, and 39.5% on average for with-security and without-security respectively than LEADER and SBIDS in the decreased rank attack. Novelty: This paper presents a rank attack detection approach for non-storing mode RPL used in IoT to cope with both increased and decreased rank attacks to address this issue. The performance of the suggested technique is assessed both conceptually and through simulation using the Contiki-based Cooja simulator. The proposed technique surpasses state-of-the-art rank attack detection techniques in terms of detection accuracy and false positive/negative rate while maintaining acceptable network performance, according to simulation results.

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A Trust-Based Security Model to Detect Misbehaving Nodes in Internet of Things (IoT) Environment using Logistic Regression

Cited by 13 publications

References 16 publications

IoT trust and reputation: a survey and taxonomy

IoT trust and reputation: a survey and taxonomy

Online Monitoring of Ecoenvironmental Pollution Data in Internet Environment

Secured Technique to Detect and Avoid Malicious Nodes in Internet of Things

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