CDMA-Based Security Against Wormhole Attack in Underwater Wireless Sensor Networks

Goyal, Nitin; Sandhu, Jasminder Kaur; Verma, Luxmi

doi:10.1007/978-981-15-5341-7_62

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…In the future work, the AI enabled wireless sensor network environment will be analyzed and simulated by undertaking underwater wireless sensor network criteria with the experimented parameters for comparison by taking references of previous researcher's report (Goyal et al 2021;Gupta et al 2020)…”

Section: Discussionmentioning

confidence: 99%

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Priyanka

Thangavel

Sagayam

et al. 2021

Int J Syst Assur Eng Manag

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In the modern evolution, WSN (Wireless Sensor Network) incorporated with data aggregation platform which involves stimulating research area with various modern upgradation of AI (Artificial Intelligence). Many types of research are carried out by undertaking variety of Deep Learning Network and Fuzzy based data aggregation techniques in the interpretation of Wireless Sensor Circumstance. The focal theme of the proposed research paper is to analyze the present concentrated work on Artificial Intelligence-accompanied data aggregation paradigm in wireless communication by elaborating the integration framework. By this proposed AI with data aggregation wireless sensor system has upgraded the innovation with high empowering pillars in analyzing the data with more processing and interpretations. Since it also improves the data transmission rate by providing more security and encryption schemes to the preprocessed data storing and streaming ton the bandwidth channels. This paper enumerates the AI contribution on the computing platform with the various advancement of blockchain technology schematic framework and further its case study experimentation on the aero-engine applications. Further experimental results are provided with numerical analysis by showing the traditional and advanced-Long term Memory technique prediction results by taking aero-engine wireless sensor network environment through MATLAB simulation.

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Section: Discussionmentioning

confidence: 99%

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Priyanka

Thangavel

Sagayam

et al. 2021

Int J Syst Assur Eng Manag

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show abstract

“…Decision trees are used to resolve different WSN problems, including connectivity, data aggregation, and mobile devices. To evaluate the lifespan of the node, the attack detection rate and node energy are estimated Goyal et al [54] 2021 CDMA-based security Underwater wireless sensor networks wormhole attack. Compared to current methods, the proposed approach also increases energy efficiency Wang et al [55] 2021 Approaches to service selection that is quick and dependable…”

Section: Decision Trees (Dt)mentioning

confidence: 99%

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)

Abdan

Seno

2022

Wireless Communications and Mobile Computing

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A wormhole attack is a type of attack on the network layer that reflects routing protocols. The classification is performed with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), naive Bayes (NB), and convolutional neural network (CNN). Moreover, we used nodes’ properties for feature extraction, especially nodes’ speed, in the MANET. We have collected 3997 distinct (normal 3781 and malicious 216) samples that comprise normal and malicious nodes. The classification results show that the accuracy of the KNN, SVM, DT, LDA, NB, and CNN methods are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, and 96.4%, respectively. Based on our findings, the DT method’s accuracy is 98.9% and higher than other ways. In the next priority, SVM, KNN, CNN, LDA, and NB indicate high accuracy, respectively.

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“…This approach integrates various factors into a comprehensive fitness function, encompassing metrics like the number of hops from the anchor node, time of arrival (ToA) distance estimation error, and transmission delay. Nonetheless, current protocols may struggle to deliver accurate data in vast aquatic environments such as seas and oceans due to their extensive coverage 7 . Moreover, UWSNs confront additional hurdles, including fluctuations in water level and pressure, demanding thorough examination.…”

Section: Introductionmentioning

confidence: 99%

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm

Chandrasekar,

Shanmugavalli,

Mahesh

et al. 2024

Sci Rep

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In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

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CDMA-Based Security Against Wormhole Attack in Underwater Wireless Sensor Networks

Cited by 9 publications

References 14 publications

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm

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