Mitigating Localization and Neighbour Spoofing Attacks in Underwater Sensor Networks

Chandavarkar, B. R.; Gadagkar, Akhilraj V.

doi:10.1109/icccnt49239.2020.9225431

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…Chandavarkar and Gadagkar 116 devised an anti‐Spoof (a‐Spoof) method to minimize localization and neighbor spoofing threats in UASN. To share the location, A‐Spoof employs three symmetric passwords that are pre‐shared.…”

Section: Uwsns—attacksmentioning

confidence: 99%

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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SummaryWireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

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Section: Uwsns—attacksmentioning

confidence: 99%

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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“…After being captured, the malicious anchor nodes underwater may exhibit two types of behaviors: direct and indirect malicious behavior. Direct malicious behavior [18] includes providing false location information and advancing or delaying sending response messages. The indirect malicious behavior [17] affects the normal operation of the network by deliberately lowering the trust value of normal anchor nodes or increasing the trust value of underwater malicious anchor nodes, that is, the trust deception behavior.…”

Section: Introductionmentioning

confidence: 99%

Localization of Underwater Sensor Networks for Ranging Interference based on the AdaDelta Gradient Descent Algorithm

Zhou

Xing

et al. 2023

Preprint

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The precise localization of sensor nodes is a key link in underwater wireless sensor networks (UWSNs) applications. Furthermore, the nodes in UWSNs should be able to independently determine their relative positions before they begin to receive and transmit data to each other. Most of the existing underwater localization algorithms are based on the anchor nodes with known locations, and the rest of the nodes can be calculated by the anchor nodes. The iterative localization process has been proposed for the localization of UWSNs, in which some positioned nodes as reference nodes are selected for iterative localization. However, in real scenarios, some anchor nodes may be destroyed naturally or artificially damaged or even used to convey misleading information to interfere with the precise localization of other nodes underwater. This paper proposes a computationally efficient localization algorithm that can accurately locate the sensor nodes underwater to resist such network attacks. The proposed algorithm achieves better localization accuracy by iteratively removing misleading information transmitted from interfering nodes based on the Time Difference of Arrival ranging mapping and combining selective minimum gradient of AdaDelta gradient descent (AGD). The simulation results prove that the proposed algorithm performs better under network attacks than the existing algorithms.

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“…On this basis, it further proposed an authentication scheme using deep reinforcement learning to improve the accuracy of spoofing detection. Literature [8] proposes an a-spoof algorithm based on a shared key mechanism and a hash algorithm for attacks caused by nodes exchanging location and identity information.…”

Section: Introductionmentioning

confidence: 99%

Secure identity authentication scheme based on PUF for IoUT

2021

AJCIS

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The existing identity authentication schemes based on the devices of Internet of Underwater Things (IouT) are all executed at the network layer and higher layers, which require high computing costs and are not suitable for devices with limited computing resources. By applying PUF to key binding, a physical layer-based identity authentication scheme is proposed. The authentication process is realized by the joint typicality coding and decoding technology based on the packing lemma and the covering lemma. The Achievability and Converse propositions prove the feasibility of the scheme, and the confidential capacity domain is delimited from the perspective of information theory. The false authentication analysis theoretically limits the probability of successful deception by the adversary, and reduces it to the lowest limit.

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Mitigating Localization and Neighbour Spoofing Attacks in Underwater Sensor Networks

Cited by 12 publications

References 19 publications

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Localization of Underwater Sensor Networks for Ranging Interference based on the AdaDelta Gradient Descent Algorithm

Secure identity authentication scheme based on PUF for IoUT

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