Detection and prevention of spoofing attacks in mobile adhoc networks using hybrid optimization algorithm

Visalakshi, P.; Prabakaran, S.

doi:10.3233/jifs-182881

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…With this, the authors claimed that the suggested prototype is not merely efficacious against IP spoofing attacks, but it has also the capability to oppose the anticipation of malicious nodes in the network. Likewise, Visalakshi et al [86] proposed an improved whale optimization (IWO) algorithm for WSN applications to tackle the IP spoofing attacks related issues. In this prototype, the authors basically used IDS, where they defined parameters for transmitted signal evaluation to ensure the legitimacy of the network traffic followed by connected devices.…”

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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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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“…Moreover, misuse detection is better than abnormal behavior‐based detection 12 . IDS is executed through a condition check algorithm, which does the average route trust and evaluates the intrusion reaction action (IRA) from the nodes through no punishment, route change, and route isolate parameters of nodes 13 . IDS works with significant data collection from WSN nodes and detects aberrant sensor node behavior(s) 14 .…”

Section: Related Workmentioning

confidence: 99%

“…12 IDS is executed through a condition check algorithm, which does the average route trust and evaluates the intrusion reaction action (IRA) from the nodes through no punishment, route change, and route isolate parameters of nodes. 13 IDS works with significant data collection from WSN nodes and detects aberrant sensor node behavior(s). 14 Moreover, WSN attacks are similar and occur mainly in data link, application, and network layers.…”

Section: Related Workmentioning

confidence: 99%

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

Selvi

Shen

2022

Concurrency and Computation

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Wireless sensor network (WSN) is widely used in many fields, including agriculture, military, healthcare, monitoring, and surveillance. WSN application increases day by day due to growth in embedded and internet technologies. However, data security in WSN is challenging, and different types of attacks are increasing. Traditional intrusion detection methods analyze and identify attacks such as Denial of Service, anomaly detection, wormhole, sybil, and blackhole. Traditional methods never addressed the application layer's novel attack, such as the delete attack. Unique DELETE attack (DA) forms in the application layer and targets web servers and applications. Since unique DA use uniform resource locator requests, distinguishing them from legitimate traffic becomes difficult. The unique DA classify as unique (or) single, multiple, and repeated unique DELETE attacks; for detecting the unique DA types, gradient thresholding-long short-term memory (GT-LSTM) algorithm is proposed using packet per second and traffic rate data in WSN nodes. GT-LSTM algorithm detects unique DA through different thresholding at the training option layer in LSTM. Different gradient thresholding values in LSTM reduce the exploding gradients which fail to detect the unique DA nodes. Gradient thresholding in LSTM layers lies 1 to 3 for detecting unique DELETE attack nodes. The proposed method reduces detection time, improves accuracy, and identifies the hidden node that performs a unique DA in WSN. The unique DELETE Attack identification and performance through the proposed GT-LSTM is analyzed in the NS2 simulation environment and the test bed of WSN. Forms the experimental and simulation results, GT-LSTM performs better than fuzzy, KNN, and linear regression-based intruder detection systems. The proposed algorithm achieves 99% accuracy in detecting the DELETE attack nodes in WSN.

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Classification of Cyber Security Threats on Mobile Devices and Applications

Almaiah

Alzahrani

Almomani

et al. 2021

Studies in Big Data

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Detection and prevention of spoofing attacks in mobile adhoc networks using hybrid optimization algorithm

Cited by 6 publications

References 34 publications

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

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

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

Classification of Cyber Security Threats on Mobile Devices and Applications

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