RETRACTED ARTICLE: Novel Sybil attack detection using RSSI and neighbour information to ensure secure communication in WSN

Angappan, Arthanareeswaran; Saravanabava, T. P.; Sakthivel, P.; Vishvaksenan, K. S.

doi:10.1007/s12652-020-02276-5

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…The proposed method has a firm reliance on historical records, making this approach not stable and durable. Angappan, Sakthivel & Vishvaksenan (2020) proposed a localized scheme for Sybil node detection called NoSad using RSSI value and intra-cluster communication, which can be deployed to the device. However, NoSad is not stable when there are a minimum of two Sybil node and cannot cater to mobility in WSN.…”

Section: Survey Methodologymentioning

confidence: 99%

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

Arshad

Hanapi

Subramaniam

et al. 2021

PeerJ Computer Science

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Wireless sensor networks (WSN) have been among the most prevalent wireless innovations over the years exciting new Internet of Things (IoT) applications. IoT based WSN integrated with Internet Protocol IP allows any physical objects with sensors to be connected ubiquitously and send real-time data to the server connected to the Internet gate. Security in WSN remains an ongoing research trend that falls under the IoT paradigm. A WSN node deployed in a hostile environment is likely to open security attacks such as Sybil attack due to its distributed architecture and network contention implemented in the routing protocol. In a Sybil attack, an adversary illegally advertises several false identities or a single identity that may occur at several locations called Sybil nodes. Therefore, in this paper, we give a survey of the most up-to-date assured methods to defend from the Sybil attack. The Sybil attack countermeasures includes encryption, trust, received signal indicator (RSSI), encryption and artificial intelligence. Specifically, we survey different methods, along with their advantages and disadvantages, to mitigate the Sybil attack. We discussed the lesson learned and the future avenues of study and open issues in WSN security analysis.

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Section: Survey Methodologymentioning

confidence: 99%

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

Arshad

Hanapi

Subramaniam

et al. 2021

PeerJ Computer Science

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“…For example, schemes such as Kabbur and Kumar [24] and Yuan et al [25] use RSS indication values obtained through triangulation, requiring at least three monitoring nodes to be used [12] [23]. Other examples include schemes like Lv et al [26], Abbas et al [27] and Angappan et al [28], which require the use of additional localization information such as those obtainable through neighbors of the suspicious nodes [12]; consequently, unlike schemes that purely and directly use intrinsically generated physical layer data, these schemes may be more susceptible to attacks involving information spoofing.…”

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Chulerttiyawong

Jamalipour

2023

IEEE Internet Things J.

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Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

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“…If a node finds a sybil neighbor node then the sensor node thinks that it is its nearest neighbors and chooses the sybil node as the next hop neighbor. This attack induces fake packets in the network and disrupts the functioning of the network [28]. − Flooding attack: In this attack the malicious node tries to flood the memory of the target node by sending connection request messages.…”

Section: Different Types Of Attacks In Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Survey on data aggregation based security attacks in wireless sensor network

Tabassum

Devanagavi

Biradar

et al. 2023

IJECE

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<p>Wireless sensor network (WSN) has applications in military, health care, environmental monitoring, infrastructure, industrial and commercial applications. The WSN is expected to maintain data integrity in all its network operations. However, due to the nature of wireless connectivity, WSN is prone to various attacks that alter or steal the data exchanged between the nodes. These attacks can disrupt the network processes and also the accuracy of its results. In this survey paper, we have reviewed various attacks available in the literature till date. We have also listed existing methods that focus on data aggregation based security mechanisms in WSN to counter the attacks. We have classified and compared these methods owing to their encryption techniques. This paper intends to support researchers to understand the basic attacks prevalent in WSN and schemes to counter such attacks.</p>

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RETRACTED ARTICLE: Novel Sybil attack detection using RSSI and neighbour information to ensure secure communication in WSN

Cited by 16 publications

References 20 publications

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Survey on data aggregation based security attacks in wireless sensor network

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