ECC Based Biometric Encryption of Compressed Image for Security over Network Channels

Balaji, Prasanalakshmi; Kannammal, A.

doi:10.1007/978-81-322-1000-9_33

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…11 IDS is the software or the hardware system that examines host or network traffic for detecting the unconventional behaviors and attacks. [11][12][13][14] The IDS detect the computer attacks through the examination of different data records collected in network. 4,[15][16][17] The purpose of IDS is to find and classify the network behavior into abnormal or normal based on certain rules.…”

Section: Introductionmentioning

confidence: 99%

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

Sathiyadhas¹,

Vigila

2022

Adaptive Control & Signal

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SummaryIn cloud computing, the resources and memory are dynamically allocated to the user based on their needs. Security is considered as a major issue in cloud as the use of cloud is increased. Intrusion detection is considered as a significant tool to develop a reliable and secure cloud environment. Performing intrusion detection in cloud is a difficult task because of its distributed nature and extensive usage. Intrusion detection system (IDS) is widely considered to find the malicious actions in network. In cloud, most conventional IDS are vulnerable to attacks and have no capability for maintaining the balance between sensitivity and accuracy. Thus, we proposed an effective dragonfly improved invasive weed optimization‐based Shepard convolutional neural network (DIIWO‐based ShCNN) to detect the intruders and to mitigate the attacks in cloud paradigm and are more feasible to detect the intruders with ShCNN. The proposed method outperforms the existing method with maximum accuracy of 0.960%, sensitivity of 0.967%, and specificity 0.961%, respectively.

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Section: Introductionmentioning

confidence: 99%

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

Sathiyadhas¹,

Vigila

2022

Adaptive Control & Signal

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“…The loan distribution is the major business unit in the majority of banks. Investing the assets safely is the goal of banking sector (Prasanalakshmi and Kannammal, 2013;Prasanalakshmi and Kumar Pugalendhi, 2020). Till today, several banks and financial sectors have no assurance even after proper verification to approve loans (Arun et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Deep learning based loan eligibility prediction with Social Border Collie Optimization

Cyril¹,

Ananth²

2022

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PurposeThe bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.Design/methodology/approachThis aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.FindingsThe analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).Originality/valueThis paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.

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“…The authors of 25 study the stabilization problem of stochastic nonlinear delay systems with exogenous disturbances and the event-triggered feedback control. The issue of stabilization is intricately connected to matters of consensus and stability, [26][27][28][29][30][31][32] and in many practical applications, stabilization is frequently necessary to achieve satisfactory system performance. 33 For instance, in, 34 The present study focused on the examination of non-fragile sampled-data stabilization for a certain category of linear systems with probabilistic timevarying delays.…”

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confidence: 99%

A novel adaptive control design for exponential stabilization of memristor‐based CVNNs with time‐varying delays using matrix measures

Jayanthi,

Santhakumari,

Grienggrai Rajchakit

et al. 2024

Int J Numerical Modelling

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The present study introduces a new adaptive control framework that aims to attain exponential stability in complex‐valued neural network systems utilizing memristors while accounting for time‐varying delays. The control issues in systems of this nature are mostly attributed to the presence of memristors and time‐varying latency. To overcome these challenges and achieve stabilization outcomes, a methodology is employed that integrates adaptive control approaches inside a matrix‐based framework. This study employs Lyapunov's stability theory to establish exponential stabilization conditions and conduct convergence analysis. The efficacy of the suggested control algorithm in achieving exponential stabilization and robustness under varied delays is demonstrated through numerical simulations.

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ECC Based Biometric Encryption of Compressed Image for Security over Network Channels

Cited by 9 publications

References 4 publications

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

Deep learning based loan eligibility prediction with Social Border Collie Optimization

A novel adaptive control design for exponential stabilization of memristor‐based CVNNs with time‐varying delays using matrix measures

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