Enhanced multi-attribute trust protocol for malicious node detection in wireless sensor networks

Prabha, V. Ram; Parthiban, Latha

doi:10.1007/s12046-016-0588-2

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…In WSNs, many evidences can be collected, such as routing success rate, packet success rate, forwarding delay, data correctness, and so on 19 . Here, we just list three typical types of evidences: the packet success rate, the routing failure rate, and the forwarding delay.…”

Section: Design Of Detection Systemmentioning

confidence: 99%

“…Many methods are proposed to detect these attacks, such as encryption, verification, and trust behavior observation. Prabha and Latha 19 adopted an elapsed time at node (ETN) to find the data modification attack, which measures sensor's elapsed time from the data reception to the data forwarding. Sultana et al 26 proposed a novel lightweight secure scheme for detecting provenance forgery, which uses the in‐packet Bloom filters to encode provenance and does data verification and reconstruction at the base station.…”

Section: Related Workmentioning

confidence: 99%

“…To reduce resource consumption in WSNs, some trust evaluation mechanisms adopt following means: collecting conclusive evidences rather than vast evaluation evidences 18 (however, this system cannot deal with more complex attacks), moving the computing and storage tasks to more powerful sensors 19 (however, cluster heads shoulder too many data forwarding, communication and processing tasks), and reducing computational complexity 20 (however, they are not sensitive to sudden attacks duo to lack of the feedback mechanism). To detect attacks against the recommendation trust (bad‐mouthing attack, on‐off attack, and so on 21 ), some methods are based on excluding/reducing recommendation value weight rules, such as familiarity, 22 outlier, 23 and deviation 24 …”

Section: Related Workmentioning

confidence: 99%

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Detection of hidden data attacks combined fog computing and trust evaluation method in sensor‐cloud system

Zhang

Wang

et al. 2018

Concurrency and Computation

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With the popularity of Sensor-Cloud, its security issues get more attention from industry and academia. Especially, Sensor-Cloud underlying network is very vulnerable to internal attacks due to its limitations in computing, storage, and analysis. Most existing trust evaluation mechanisms are proposed to detect internal attack issues from the behavior level. However, there are some special internal attacks in the data level such as hidden data attacks, which are normal in the behavior level but generate malicious data to lead user to make wrong decisions. To detect this type of attacks, we design a fog-based detection system (FDS), which is based on the trust evaluation mechanism in the behavior level. In this paper, three types of scenes (the redundant data, the parameter curve characteristic, and the data validation) are defined, and three detection schemes are given. Some experiments are conducted, which manifest that FDS has certain advantages in detecting hidden data attacks.

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Section: Design Of Detection Systemmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Detection of hidden data attacks combined fog computing and trust evaluation method in sensor‐cloud system

Zhang

Wang

et al. 2018

Concurrency and Computation

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“…Two malicious nodes are placed as neighbor nodes for source and destination and create an illusion that it is the shortest path for transmitting data. However security issues and energy conservation plays a vital role in WSN [4] [5]. To achieve these objectives, SRI-MAC protocol is proposed this paper.…”

Section: Introductionmentioning

confidence: 99%

Secure Routing with Improved Medium Access Control (SRI-MAC) Protocol for Wireless Sensor Network using Particle Swarm Optimization

Tamilarasi¹,

Santhi²

2019

IJEAT

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A set of wireless sensor nodes comprises to form a sensor field called Wireless Sensor Networks (WSN).The main purpose of using the sensor node is to collect information from the ambience process it and send to a common gateway interface called Base Station(BS). The major problems that we face while using WSN are limited battery power, bandwidth, security issues and transmission delay etc. Many algorithms and protocols were developed in order to solve the above issues. Therefore, better solutions are required to face the improvements and challenges in the current technologies. In WSN, the sensor node highly loses its energy during communication period. One of the major issues of Medium Access Control (MAC) layer is collision. Collision increases the energy consumption and delay of the sensor node. So we have to conserve the energy of the sensor node in order to extend the lifetime of the network. At the same time it is also important to transmit the data through secure path and identify the malicious node. In this paper, we propose a novelty approach called Secure Routing with Improved Medium Access control (SRI –MAC) Protocol to solve the issues. SRIMAC identifies packet precedence sets using Fuzzy Implication System (FIS) to avoid packet collision in MAC layer and also it detects wormhole attacks and selects secure path among k-paths using Particle Swarm Optimization (PSO) algorithm. By simulation results, we show that the proposed approach is efficient in terms of energy consumption and secure routing.

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A Trust Secure Data Aggregation Model with Multiple Attributes for WSNs

Dang

Ma³

et al. 2022

Wireless Algorithms, Systems, and Applications

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Enhanced multi-attribute trust protocol for malicious node detection in wireless sensor networks

Cited by 6 publications

References 16 publications

Detection of hidden data attacks combined fog computing and trust evaluation method in sensor‐cloud system

Detection of hidden data attacks combined fog computing and trust evaluation method in sensor‐cloud system

Secure Routing with Improved Medium Access Control (SRI-MAC) Protocol for Wireless Sensor Network using Particle Swarm Optimization

A Trust Secure Data Aggregation Model with Multiple Attributes for WSNs

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