Intrusion detection for network based cloud computing by custom RC-NN and optimization

Thilagam, T.; Aruna, R.

doi:10.1016/j.icte.2021.04.006

Cited by 50 publications

(26 citation statements)

References 12 publications

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“…accuracy, specificity, sensitivity, F‐score, precision, false positive rate, AUC is analyzed. Then the proposed IDS‐CC‐CCGAN‐WSOA method is examined and likened with the existing methods, such as intrusion detection framework on cloud computing with deep belief network with salp swarm algorithm (IDS‐CC‐DBN‐CSSA), 31 intrusion detection framework on cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS‐CC‐DNN‐IGASAA), 32 intrusion detection for cloud computing with multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS‐CC‐MLPNN‐ABC), 33 intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS‐CC‐RCNN‐ALO), 34 hybrid intrusion detection using map reduce based black widow optimized convolutional long short‐term memory neural networks (IDS‐CC‐BWO‐CONV‐LSTM), 35 and unified deep learning approach for efficient intrusion detection system using integrated spatial‐temporal features (IDS‐CC‐OCNN‐HMLSTM), 36 respectively. The simulation parameter of IDS‐CC‐DCCGAN‐RFOA is given in Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…Thilagam and Aruna 34 have presented an IDS‐CC‐MLPNN‐ABC. The presented method uses the CNN for creating the hybrid method to utilize long short term memory (LSTM).…”

Section: Literature Surveymentioning

confidence: 99%

“…Here, feature selection process is done with the help of univariate ensemble feature selection method 21 The selected features are given to the DCCGAN 29 for classification which classifies the data as secured data and privacy attack data. Mainly, DCCGAN classifier does not illustrate some adopting optimization methods for computing the optimum variables for ensuring precise detection of intruder in CC. In this article, the red fox optimization algorithm 30 is proposed for optimizing the DCCGAN. Here, the proposed technique is implemented in MATLAB, its performance is estimated with performance metrics, like accuracy, sensitivity, specificity, precision, F score, and recall. The comparison of evaluation metrics is analyzed with the various existing methods, like IDS‐CC‐DBN‐CSSA, 31 IDS‐CC‐DNN‐IGASAA, 32 IDS‐CC‐MLPNN‐ABC, 33 IDS‐CC‐RCNN‐ALO, 34 IDS‐CC‐BWO‐CONV‐LSTM, 35 and IDS‐CC‐OCNN‐HMLSTM, respectively 36 …”

Section: Introductionmentioning

confidence: 99%

“…• The comparison of evaluation metrics is analyzed with the various existing methods, like IDS-CC-DBN-CSSA, 31 IDS-CC-DNN-IGASAA, 32 IDS-CC-MLPNN-ABC, 33 IDS-CC-RCNN-ALO, 34 IDS-CC-BWO-CONV-LSTM, 35 and IDS-CC-OCNN-HMLSTM, respectively. 36 The rest of this article is organized as: Section 2 reviews the literature survey, Section 3 explains the proposed methodology, Section 4 demonstrates the results and discussion, and Section 5 concludes the article.…”

Section: Introductionmentioning

confidence: 99%

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Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

Devi

Muthusenthil

2022

Trans Emerging Tel Tech

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Cloud computing (CC) is vulnerable for the attacks of current information technology, because it prolongs and uses the information technology infrastructure, traditional operating system, and applications. Several security issues in detecting irregular network performances are confronted by the CC environment. With the intention of solving these security issues, dual-channel capsule generation adversarial network optimized with red fox optimization algorithm fostered intrusion detection framework (IDS-CC-DCCGAN-RFOA) is proposed in this article, for securing the privacy attacks in CC environment. Initially, the data are taken from NSL-KDD benchmark dataset. Then the data are given to the preprocessing segment. When using the developed random forest and local least squares in preprocessing segment, the redundancy, and missing values are purged. Then the output of preprocessing is given to the feature selection stage. In feature selection, the optimal features are extracted using univariate ensemble feature selection (UEFS) technique. Based on the optimal features, the data are classified into secured data and privacy attack data based on dual-channel capsule generative adversarial network. Here, the weight of dual-channel capsule generative adversarial network is optimally tuned using the red fox optimization algorithm for generating an effective and optimal solution to detect the intruders. The proposed IDS-CC-DCCGAN-RFOA method is implemented in MATLAB. Here, the performance metrics, like accuracy, specificity, sensitivity, F-score, precision, false positive rate, AUC are analyzed.Then, the proposed IDS-CC-CCGAN-WSOA method provides 13.9367%, 11.12%, 13.268%, and 13.739% higher accuracy and 24.54%, 29.76%, 12.87%, and 34.21% higher sensitivity compared with the existing methods, like intrusion detection framework in cloud computing using deep belief network with salp swarm algorithm (IDS-CC-DBN-CSSA), intrusion detection framework in cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS-CC-DNN-IGASAA), intrusion detection for cloud computing using multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS-CC-MLPNN-ABC), and intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS-CC-RCNN-ALO).

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Section: Resultsmentioning

confidence: 99%

“…Thilagam and Aruna 34 have presented an IDS‐CC‐MLPNN‐ABC. The presented method uses the CNN for creating the hybrid method to utilize long short term memory (LSTM).…”

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

Devi

Muthusenthil

2022

Trans Emerging Tel Tech

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“…They achieved over 99.97% accuracy on the NSL-KDD datasets. Thilagam et al ( 21 ), on the other hand, proposed an Intrusion detection system on a more complex basis using RCNN (Recurrent Convolutional Neural Networks) using the culmination of CNN and LSTM (Long Short Term Memory) on the KDD Cup 99 and CIC-IDS 2018 datasets. They achieved over 94% accuracy on both datasets with the same model.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

Kumaar¹,

Samiayya

Vincent

et al. 2022

Front. Public Health

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The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any change in the actual patient data can lead to errors in the diagnosis and treatment. Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks. These processes also make it challenging to secure patient records in medical systems as the intrusion detection mechanisms can become frequently obsolete. This paper proposes a hybrid framework using Deep Learning named “ImmuneNet” to recognize the latest intrusion attacks and defend healthcare data. The proposed framework uses multiple feature engineering processes, oversampling methods to improve class balance, and hyper-parameter optimization techniques to achieve high accuracy and performance. The architecture contains <1 million parameters, making it lightweight, fast, and IoT-friendly, suitable for deploying the IDS on medical devices and healthcare systems. The performance of ImmuneNet was benchmarked against several other machine learning algorithms on the Canadian Institute for Cybersecurity's Intrusion Detection System 2017, 2018, and Bell DNS 2021 datasets which contain extensive real-time and latest cyber attack data. Out of all the experiments, ImmuneNet performed the best on the CIC Bell DNS 2021 dataset with about 99.19% accuracy, 99.22% precision, 99.19% recall, and 99.2% ROC-AUC scores, which are comparatively better and up-to-date than other existing approaches in classifying between requests that are normal, intrusion, and other cyber attacks.

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Cycle‐consistentgenerative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

Preethi

Sugitha²,

Kavitha³

2022

Concurrency and Computation

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Cloud computing is the delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence over the internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale. In this article, cycle-consistent generative adversarial network (CCGAN) optimized with water strider optimization (WSO) algorithm fostered intrusion detection system (IDS) is proposed to secure the cloud computing (CC) environment (IDS-CC-CCGAN-WSOA).Initially, the input data's are gathered via NSL-KDD benchmark dataset. Then it is given to preprocessing. During preprocessing, it debugs the redundancy and missing value is restored using local least squares. Then, the preprocessed output is fed to the feature selection level. The optimum features are compiled utilizing correlation feature selection approach. This optimum features based, the data's are categorized as normal and anomalous data. The weights of this network are optimized by water strider optimization to attain effectual and optimum solution for recognizing the intruders. The proposed approach is executed in MATLAB. The performance metrics is examined to validate the performance of the proposed approach. Finally, the proposed approach attains 13.9367%, 13.268%, and 13.739% higher accuracy analyzed to the existing approaches, such as IDS-CC-DBN-CSSA, IDS-CC-DNN-IGASAA, and IDS-CC-MLPNN-ABC.

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Intrusion detection for network based cloud computing by custom RC-NN and optimization

Cited by 50 publications

References 12 publications

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

Cycle‐consistentgenerative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

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