Recognition of grey hole attacks in wireless sensor networks using fuzzy logic in IoT

Ye, Qian; Wang, Yufei; Xi, Maolong; Tang, Yulan

doi:10.1002/ett.3873

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…In both approaches, 16,24 Normal packet loss due to noise or congestion has not been considered so false positive rate increases and attack detection accuracy decreases. Ye et al 25 proposed fuzzy logic-based gray hole attack detection in IoT. They proposed fuzzy engine to identify the suspicious activity in the network by the rules generated.…”

Section: Related Workmentioning

confidence: 99%

A reputation‐based RPL protocol to detect selective forwarding attack in Internet of Things

Patel

Jinwala

2021

Int J Communication

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Internet of Things (IoT) is an internet of smart objects where smart objects communicate with each other. IoT objects are deployed in open medium with dynamic topology. Due lack of infrastructure and centralized management, IoT present serious vulnerabilities to security attacks. Therefore, security is an essential prerequisite for the real-world deployment of IoT. In this work, we propose reputation-based RPL protocol where reputation-based mechanism is embedded into RPL protocol to enhance its capabilities against selective forwarding attack. Reputation is calculated by evaluating data forwarding behavior of IoT node. Data forwarding behavior of IoT node is evaluated by the difference between monitored actual packet loss and estimated normal loss. Calculated reputation value is considered in parent selection. Simulation results show that the proposed approach can accurately detect and isolate selective forwarding attack with improving data delivery ratio of the IoT network.

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Section: Related Workmentioning

confidence: 99%

A reputation‐based RPL protocol to detect selective forwarding attack in Internet of Things

Patel

Jinwala

2021

Int J Communication

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“…The proposed fuzzy path selection approach is simulated and evaluated to efficiently decrease the effects of selective forwarding attacks and increases routing reliability. [42] proposed a fuzzy engine to identify the selective forwarding attack and stop its function immediately.…”

Section: Ai-based Schemesmentioning

confidence: 99%

“…But the transient fluctuation of channels may result in the rapid change of the threshold values, and then draw mistake detection results. The AI-based detection schemes [34][35][36][37][38][39][40][41][42][43][44][45] find malicious nodes by actively learning their malicious behaviors which are different from those of normal nodes. But these schemes need a long learning process or game process to model the malicious behaviors.…”

Section: Introductionmentioning

confidence: 99%

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

Liu

2021

IEEE Access

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In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. Next, router nodes (RNs) forward those packets to the sink node (SN) by multi-hop communications. Compromised RNs would become malicious and launch selective forwarding attacks by dropping part of or all the packets from other nodes. On the other hand, a harsh environment makes the channel poor, so the routing nodes under a harsh environment have low packet forwarding rates because they sometimes have to give up forwarding the current packets after many tries to forward them due to poor channel. If the malicious nodes' forwarding rates become close to those of nodes under a harsh environment, the schemes based on packet forwarding rates for detecting selective forwarding attack may fail because they cannot differentiate the low data packet forwarding rates resulting from the malicious behaviors or harsh environment. To solve this problem, we provide a combined scheme for detecting selective forwarding attack in wireless sensor networks (WSNs) under harsh environments. This scheme employs a data clustering algorithm (DCA) to screen the malicious nodes out by clustering their cumulative forwarding rates (CFRs) and designs a voting decision method to protect the nodes under a harsh environment from being judged as malicious nodes. The simulation results show that our scheme has a low false detection rate (FDR) of 1% and a low missed detection rate (MDR) of 5% respectively with negligible energy consumption in WSNs under a local variable harsh environment.INDEX TERMS event-driven wireless sensor networks, selective forwarding attack, cumulative forwarding rate, data clustering algorithm

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“…The data exploration is sophisticated and ineffective in smart energy networks. 7 In the uncertain complexion, it is hard to implement complex evaluation to their inconstant information. 8 Generally, in complicated power models, data are primarily forwarded in the type of time sequence, which captivates for their major progress, high discretion, and its predicament to manifest models.…”

Section: Introductionmentioning

confidence: 99%

“…The data exploration is sophisticated and ineffective in smart energy networks 7 . In the uncertain complexion, it is hard to implement complex evaluation to their inconstant information 8 .…”

Section: Introductionmentioning

confidence: 99%

A guaranteed data transmission system for wireless ad hoc networks

Gopi

Jayanthi²,

Pandiyan

2020

Trans Emerging Tel Tech

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Wireless ad hoc networks are highly used in the current network environment in spite of its effectiveness, reliable, minimum cost utilization, and non-static infrastructure. To achieve the Quality of Service (QoS) in the wireless ad hoc network, resources in the network should be used properly. Here the QoS to be ensured is edge to edge latency, uneasiness, and packet loss. During information passing in a multi-hop edge to edge interaction, the nodes in between the sender and receiver may intrude or monitor or listen to the information without the consent of the sender and receiver. To overcome these disadvantages, an assured and flexible information transmission should take place between the sender and receiver. In this regard, an optimal model called CQASS has been proposed to attain better performance by assuring the edge to edge interaction in wireless ad hoc networks. The edge to edge interaction is assured and protected by authentication. An optimistic connection identity from the pool of delivery connection is used to maintain the QoS. Flexible symmetric encryption using the secret key is implemented for node communication. The proposed model CQASS will not allow any malevolent nodes to interact with the legal in between nodes for the information transmission even the proposed will not allow the malevolent nodes to enter the legal path between the sender and receiver. The proposed model results are compared with the existing systems and the proposed model increases the performance of the network through resulting in reduced packet lose, handling of uneasiness and minimizes the latency in edge to edge communication. Optimal key generation, better ciphering, minimized pricing, and effective storage and interaction are other features of the proposed model.

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Recognition of grey hole attacks in wireless sensor networks using fuzzy logic in IoT

Cited by 7 publications

References 19 publications

A reputation‐based RPL protocol to detect selective forwarding attack in Internet of Things

A reputation‐based RPL protocol to detect selective forwarding attack in Internet of Things

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

A guaranteed data transmission system for wireless ad hoc networks

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