Detection of Good and Bad Sensor Nodes in the Presence of Malicious Attacks and Its Application to Data Aggregation

Yessembayev, Anes; Sarkar, Dilip; Sikder, Faisal

doi:10.1109/tsipn.2018.2790164

Cited by 20 publications

(9 citation statements)

References 33 publications

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“…This work avoids sending packets from redundant nodes which reduces network traffic greatly. In [26], proposed a local outlier factor (LOF) algorithm which classifies the sensor readings into reliable and unreliable readings. However, this LOF method was not suitable for the large-scale network as it increases the time consumption at every cluster.…”

Section: IImentioning

confidence: 99%

“…Hence, there must be an end-to-end encrypted and redundant data elimination process over the encrypted (no needs to decrypt for redundant elimination) packets that need to be proposed to maintain end-to-end security. On the other hand, the WSN security-oriented literature such as fuzzy-based biometric schema [32], Okamoto-Uchiyama [25], and elliptic curve cryptography (ECC) [26] required high computational power were not suitable for energy-constrained WSN. Hence, a lightweight and secure encryption method must be identified to best suit the resource-constrained WSN.…”

Section: A Problem Statementmentioning

confidence: 99%

“…The performance of the proposed work evaluated three different aspects: energy efficiency, transmission efficiency and Redundant Data security. Also, we compared with NEP [23], homomorphic algorithm [17], SMEER [26], EEICS [37] and 3D-PEGASIS [38]. The observations were made in terms of performance metrics: average energy consumption, average residual energy, packet delivery ratio (PDR) and delay.…”

Section: Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

Ramasamy

Anisi

Jindal

2022

IEEE Trans. on Green Commun. Netw.

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Section: IImentioning

confidence: 99%

Section: A Problem Statementmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

See 1 more Smart Citation

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

Ramasamy

Anisi

Jindal

2022

IEEE Trans. on Green Commun. Netw.

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“…Kernel principal component analysis based Mahalanobis kernel is yet another outlier detection method that has been applied in [7]. In [21], the authors used Local Outlier Factor (LOF) algorithm to segregate normal nodes from anomalous ones. Based on density-based spatial clustering of applications with noise (DBSCAN), Abid et.…”

Section: Related Studiesmentioning

confidence: 99%

Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications

Ghosh¹,

Paul²,

Maity³

et al. 2019

Preprint

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Fault detection in sensor nodes is a pertinent issue that has been an important area of research for a very long time. But it is not explored much as yet in the context of Internet of Things. Internet of Things work with a massive amount of data so the responsibility for guaranteeing the accuracy of the data also lies with it. Moreover, a lot of important and critical decisions are made based on these data, so ensuring its correctness and accuracy is also very important. Also, the detection needs to be as precise as possible to avoid negative alerts. For this purpose, this work has adopted Dempster-Shafer Theory of Evidence which is a popular learning method to collate the information from sensors to come up with a decision regarding the faulty status of a sensor node. To verify the validity of the proposed method, simulations have been performed on a benchmark data set and data collected through a test bed in a laboratory set-up. For the different types of faults, the proposed method shows very competent accuracy for both the benchmark (99.8%) and laboratory data sets (99.9%) when compared to the other state-of-the-art machine learning techniques.

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“…For a selective forwarding attack, a malicious node detection algorithm based on a triangle module fusion operator (MDTMO) (Yessembayev, 2018) is suggested. After the base station node receives the warning information, the monitoring node will then alert the base station node, further verifying whether the packet loss is caused by network congestion or attack.…”

Section: Introductionmentioning

confidence: 99%

Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks

Sunitha

Chandrika²

2021

International Journal of Digital Crime and Forensics

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The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.

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Detection of Good and Bad Sensor Nodes in the Presence of Malicious Attacks and Its Application to Data Aggregation

Cited by 20 publications

References 33 publications

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications

Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks

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