Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks

Derhab, Abdelouahid; Bouras, Abdelghani; Belaoued, Mohamed; Μαγλαράς, Λέανδρος; Khan, Farrukh Aslam

doi:10.3390/s20216106

“…For instance, although wired sensors are efficient, they are expensive and difficult to manipulate in terms of installation, particularly for wide geographic distributions. On the contrary, wireless sensors may be easier to use but are subject to distortion due to environmental conditions and the presence of a higher level of electromagnetic disturbance, and are more vulnerable to cyber-attacks than wired sensors [53,54]. In addition, drone sensors, such as those found on Unmanned Aerial Vehicles (UAVs), are unstable.…”

Section: Technologiesof Detection Sensorsmentioning

confidence: 99%

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Berghout

¹

,

Benbouzid

²

,

Bentrcia

³

et al. 2021

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To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

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“…Although the reputation approaches [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] relying on the watchdog approach permit to punish malicious nodes that drop data packets, they are not able to thwart selective dropping attack [21]. Almost of these approaches are vulnerable to two mains security threats:…”

Section: Introductionmentioning

confidence: 99%

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

Bounouni

¹

,

Bouallouche-Medjkoune

²

,

Beraza

³

et al. 2021

Wireless Pers Commun

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In this paper, we propose a new reputation approach, called I-WG (improved Watchdog). The aims is to eliminate selective dropping attack that occurs when malicious nodes drop packets at low rate to damage the network, while at the same time to avoid to be detected. The proposed approach is structured around four modules. The monitoring module overhears the forwarding activities of neighbors nodes using the promiscuous mode. The reputation module evaluates the nodes reputation values. We have have proposed a new reputation method that enable nodes to evaluate their neighbors in multiple monitoring sessions. Thus, the computed reputation value is used to determine the increment and decrement reputation rate. The exclusion module is responsible for excluding nodes with reputation values below the reputation threshold. The route selection module make restriction about discovered forwarding routes. Only forwarding routes satisfying the route incorporation threshold are accepted. The simulation results demonstrate that I-WG improves the success rate and reduces the number of packets dropped by malicious nodes, while increases the end-to-end delay.

show abstract

“…Although the reputation approaches [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] relying on the watchdog approach permit to punish malicious nodes that drop data packets, they are not able to thwart selective dropping attack [21]. Almost of these approaches are vulnerable to two mains security threats:…”

Section: Introductionmentioning

confidence: 99%

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

Bounouni

¹

,

Bouallouche-Medjkoune

²

,

Beraza

³

et al. 2021

Preprint

1

0

View full text Add to dashboard Cite

In this paper, we propose a new reputation approach, called I-WG (improved Watchdog). The aims is to eliminate selective dropping attack that occurs when malicious nodes drop packets at low rate to damage the network, while at the same time to avoid to be detected. The proposed approach is structured around four modules. The monitoring module overhears the forwarding activities of neighbors nodes using the promiscuous mode. The reputation module evaluates the nodes reputation values. We have have proposed a new reputation method that enable nodes to evaluate their neighbors in multiple monitoring sessions. Thus, the computed reputation value is used to determine the increment and decrement reputation rate. The exclusion module is responsible for excluding nodes with reputation values below the reputation threshold. The route selection module make restriction about discovered forwarding routes. Only forwarding routes satisfying the route incorporation threshold are accepted. The simulation results demonstrate that I-WG improves the success rate and reduces the number of packets dropped by malicious nodes, while increases the end-to-end delay.

show abstract

Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks

Cited by 9 publications

References 44 publications

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

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