A novel implementation of routing attack detection scheme by using fuzzy and feed-forward neural networks

Ezhilarasi, M.; Gnanaprasanambikai, L.; Kousalya, A.; Shanmugapriya, M.

doi:10.1007/s00500-022-06915-1

Cited by 23 publications

(22 citation statements)

References 19 publications

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“…In [61] author proposed a method for five routing attacks which are Grayhole attack, Selective forwarding attack, Sinkhole attack , Blackhole and Wormhole attacks are covered. The purpose of this paper is to detect all above attacks by one method which is fuzzy rule and feed forward neural network, author also compared the proposed solution with traditional ML approaches such as RF, DT and SVM.…”

Section: ) Fuzzy Rule and Feed Forward Methods For Securitymentioning

confidence: 99%

A Review on the Security of IoT Networks: From Network Layer’s Perspective

et al. 2023

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Internet of Things (IoT) has revolutionized the world in the last decade. Today millions of devices are connected to each other utilizing IoT technology in one way or the other. With the significant growth in IoT devices, the provision of IoT security is imperative. Routing protocol for low power and lossy networks (RPL) is a network layer protocol, specially designed for routing in IoT devices. RPL protocol faces many attacks such as selective forwarding attacks, blackhole attacks, sybil attacks, wormhole attacks, and sinkhole attacks. All these attacks pose great threats to IoT networks and can substantially affect the performance of the network. In this work, a comprehensive review of internal attacks on the network layer is presented. Specifically, we focus on the literature that considers presenting solutions for the detection and prevention of sinkhole attacks. We reviewed the state-of-the-art works and different performance parameters like energy consumption, scalability, threshold value, packet delivery ratio, and throughput. Moreover, we also present a detailed analysis of machine learning-based algorithms and techniques proposed for the security of RPL protocol against internal attacks.

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Section: ) Fuzzy Rule and Feed Forward Methods For Securitymentioning

confidence: 99%

A Review on the Security of IoT Networks: From Network Layer’s Perspective

et al. 2023

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“…Within this scheme, the authors used a decision tree, long and short-term memory, a support vector machine, and knearest neighbor algorithms to ensure the legitimacy of an employed WSN application. Ezhilarasi et al [81] proposed a novel intrusion detection system for WSN to detect and prevent routing attacks such as hello flood attacks, sybil attacks, wormhole attacks and blackhole attacks, etc, in these networks. To continue this discussion, Raghav et al [82], proposed a bio-inspired secure routing framework for WSN applications employing bee algorithms.…”

Section: B Network Layer Counteraction Schemes Of Swsnmentioning

confidence: 99%

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

Adil

Menon²,

Balasubramanian

et al. 2023

IEEE Sensors J.

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“…The disadvantage obtained with this approach was the deployment of an inefficient and unreliable framework of MANET. In [21] suggested an innovative probabilistic key management algorithm with the utilization of asymmetric cryptography. It was suited for a large-scale MANET.…”

Section: Related Workmentioning

confidence: 99%

Generative Boltzmann Adversarial Network in Manet Attack Detection and QOS Enhancement with Latency

Marandi¹,

Dogra²,

Bhatt³

et al. 2022

Int. j. commun. netw. inf. secur.

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Mobile Ad-Hoc Network (MANET) are considered as self-configured network those does not have any centralized base station for the network monitoring and control. MANET environment does not control architecture for routing for the frequent maintenance of topology. The drastic development of Internet leads to adverse effect of development in MANET for different multimedia application those are sensitive to latency. Upon the effective maintenance of the QoS routing route discovery is performed to calculate queue and contention delay. However, the MANET requirement comprises of the complex procedure to withstand the Quality of Service (QoS) with Artificial Intelligence (AI). In MANET it is necessary to compute the MANET attacks with improved QoS with the reduced latency as existing model leads to higher routing and increased latency. In this paper proposed a Generative Boltzmann Networking Weighted Graph (GBNWG) model for the QoS improvement in MANET to reduce latency. With GBNWG model the MANET model network performance are computed with the weighted graph model. The developed weighted graph computes the routes in the MANET network for the estimation of the available path in the routing metrices. The proposed GBNWG model is comparatively estimated with the conventional QOD technique. Simulation analysis stated that GBNWG scheme exhibits the improved performance in the QoS parameters. The GBNWG scheme improves the PDR value by 12%, 41% reduced control packets and 45% improved throughput value.

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A novel implementation of routing attack detection scheme by using fuzzy and feed-forward neural networks

Cited by 23 publications

References 19 publications

A Review on the Security of IoT Networks: From Network Layer’s Perspective

A Review on the Security of IoT Networks: From Network Layer’s Perspective

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

Generative Boltzmann Adversarial Network in Manet Attack Detection and QOS Enhancement with Latency

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