Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks

Gomathy, V.; Padhy, Neelamadhab; Samanta, Debabrata; Sivaram, M.; Jain, Vishal; Amiri, Iraj Sadegh

doi:10.1007/s12652-020-01797-3

Cited by 81 publications

(32 citation statements)

References 12 publications

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“…Gomathy et al [29] presented an MN detection grounded on a heterogeneous cluster-centred secure routing procedure on Wireless Ad-Hoc Sensor Networks. This was deployed to save the network that mainly provided for the enhancement.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Wormhole Detection and Optimal Path Selection for Secure Data Transmission in WSN Environment

Verma

Jha

2021

Wireless Pers Commun

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Recently, the Wormhole Attack (WA) affects the Wireless Sensor Networks (WSN). So far, there are '2' solutions to trounce WA, that is, utilizing specialized hardware or capturing a specific pattern extra overhead over the network. Some prevailing solutions for detecting-WAneed special hardware or firmly synchronized clocks or longer processing time in addition to that some solutions can't even locate the WormHole (WH) and also have low security.This paper proposed an efficient WH detection as well as Optimal Path (OP) selection for safe Data Transmission (DT) in WSN. The proposed workencompasses a '2' phase: i) training, and ii) testing. Here, the WSN values are considered and chosen secure node utilizing the Fit Factor (FF). After selecting the secure nodes, the routes are discovered aimed at the optimal assortment of the optimal routes. Next, the gathered data is tested with a trained system in which feature reduction and classification are performed. Lastly, the MCSC securely transfers the non-attack data. The proposed method's outcomes are examined and weighed against the other prevailing techniques and the outcomesexhibited the proposed method's efficiency for attack detection along withdata security. KeywordsWireless sensor network (WSN) • Fit factor • Weight and diversity based deer hunting optimization (WD 2 HO) • Missing value imputation based t-distributed stochastic neighbor embedding (MVI-t-DSNE) • Linear quadratic estimation based deep artificial neural network (LQE-DANN) • Modified Cramer-Shoup Cryptography (MCSC)

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Section: Related Workmentioning

confidence: 99%

An Efficient Wormhole Detection and Optimal Path Selection for Secure Data Transmission in WSN Environment

Verma

Jha

2021

Wireless Pers Commun

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“…Before designing a security mechanism or an algorithm, the developer should concentrate on the prospective attacks on that security algorithm. Every algorithm is able to resist varied attacks based on its own properties and selection of intended algorithm based on the requirement is a major task to be competing by the developer [14]. Subsequently, continuous monitoring is essential to protect data from attacks [18].…”

Section: A Network Security Challengementioning

confidence: 99%

Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture

Samanta

Alahmadi

Karthikeyan³

et al. 2021

IEEE Access

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The growth of internet era leads to a major transformation in a storage of data and accessing the applications. One such new trend that promises the endurance is the Cloud computing. Computing resources offered by the Cloud includes the servers, networks, storage, and applications,all as services. With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network. The services offered via the Cloud are such as the infrastructure,software, platform, database and web services. The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption. This key generation in the cryptography process falls into three categories in this research work. In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements. To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment. The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files. In case of images, it resists well against statistical and differential attacks. Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services..

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“…It is critical to keep focusing on data visualization to enhance security specialists' capacity to understand a wider spectrum of risks with less time, with less commitment and money. With the rise of Data Analytics, Big Data the ability to store and process massive volumes of data is becoming extremely relevant, and machine learning is playing a key part [13]. AI can be seen in a variety of ways in the area of cyber security of Industry 4.0.…”

Section: Contributionmentioning

confidence: 99%

A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks

et al. 2021

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Increasing prevalence and simplicity of using Artificial Intelligence (AI) techniques, Steganography is shifting from conventional model building to AI model building. AI enables computers to learn from their mistakes, adapt to emerging inputs, and carry out human-like activities. Traditional Linguistic Steganographic approaches lack automation, analysis of Cover text and hidden text volume and accuracy. A formal methodology is used in only a few Steganographic approaches. In the vast majority of situations, traditional approaches fail to survive third-party vulnerability. This study looks at evaluation of an AI-based statistical language model for text Steganography. Since the advent of Natural Language Processing (NLP) into the research field, linguistic Steganography has superseded other types of Steganography. This paper proposes the positive aspects of NLP-based Markov chain model for an autogenerative cover text. The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods. Here the RNN model follows Long Short Term Memory (LSTM) neural network.The paper also includes a case study on Artificial Intelligence and Information Security, which discusses history, applications, AI challenges, and how AI can help with security threats and vulnerabilities. The final portion is dedicated to the study's shortcomings, which may be the subject of future research.

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Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks

Cited by 81 publications

References 12 publications

An Efficient Wormhole Detection and Optimal Path Selection for Secure Data Transmission in WSN Environment

An Efficient Wormhole Detection and Optimal Path Selection for Secure Data Transmission in WSN Environment

Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture

A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks

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