Enhanced Quantum-Secure Ensemble Intrusion Detection Techniques for Cloud Based on Deep Learning

Salvakkam, Dilli Babu; Saravanan, Vijayalakshmi; Jain, Praphula Kumar; Pamula, Rajendra

doi:10.1007/s12559-023-10139-2

Cited by 13 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This comparative analysis contrasts the suggested model with the existing approaches on the Bot-IoT dataset. These results are shown in Table 3 which demonstrates well that the developed approach performs better than the previous models: BETDO_CNN-TL [13], chronological SSA-based DBN [18], and DIIWO-based ShCNN [19]. With the values of 0.99, 0.95, 0.992, 0.987, 0.982, and 0.987 in the respective terms of precision, f1-score, specificity, recall, accuracy, sensitivity, the suggested HLSTM outperforms the Bot-IoT dataset.…”

Section: Comparative Analysismentioning

confidence: 73%

“…The performance of VTR-HLSTM is validated on both the Bot-IoT and NSL-KDD datasets. In comparison to the existing methods such as BETDO_Deep CNN-TL [13], Chronological SSA-based DBN [18], and DIIWO-based ShCNN [19] EICDL [14], LeNet [16], FDNN-HBAID [17], and IMFL-IDSCS [20], the VTR-HLSTM achieves 0.995% and 99.50% of accuracy on the respective Bot-IoT and NSL-KDD datasets. The VTR-LSTM exhibits finer performance guaranteeing reliability, strength and accuracy of the proposed model over the previous methods.…”

Section: Discussionmentioning

confidence: 96%

“…The suggested VTR-HLSTM's individual performance, as well as its comparative study with the previously introduced methods namely, BETDO_Deep CNN-TL [13], Chronological SSA-based DBN [18], and DIIWO-based ShCNN [19] EICDL [14], LeNet [16], FDNN-HBAID [17], and IMFL-IDSCS [20] as given in section 4.3 are discussed in this section. The existing method BWTDO_DeepCNN-TL [17], has limitations such as the requirement to enhance the outlier detection techniques as well as the clustering incorporation for an efficient detection.…”

Section: Discussionmentioning

confidence: 99%

“…The existing method BWTDO_DeepCNN-TL [17], has limitations such as the requirement to enhance the outlier detection techniques as well as the clustering incorporation for an efficient detection. Furthermore, the EICDL [18] model does not possess an adequate ability to detect prior unknown attacks. The EEDTL [19] method has only a limited set of features which allowed for fast learning so as to provide better IDS for the security of cloud data.…”

Section: Discussionmentioning

confidence: 99%

“…However, an efficient detection by the implemented BWTDO_DeepCNN-TL method further required increased outlier detection techniques and clustering incorporation. Salvakkam et al [18] implemented ensemble intrusion detection model for cloud computing utilizing deep learning (EICDL) to detect intrusions effectively. This implemented method utilized NSL-KDD, UNSW-NB15, and KDDcup 1999 datasets to detect cloud computing intrusion.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Thirumaran,

Joseph,

Srikanth

2024

IJEECS

View full text Add to dashboard Cite

<div>Cloud computing (CC) is a rapidly developing IT approach with intrusion detection system being a crucial tool for safeguarding virtual networks and machines from potential threats, thereby mitigating security concerns in the cloud environment. The intrusion detection system (IDS) system demands significant improvements, primarily based on optimizing performance and bolstering security measures. This research aims to implement an IDS in cloud computing utilizing deep learning (DL) method. The DL model is a promising technique and is widely used to detect intrusions. The implemented hierarchical long short-term memory (HLSTM) method’s performance is evaluated for feature selection through variance threshold-based regression (VTR) on two IDS network datasets: Bot-IoT and network security lab-knowledge discovery and data mining (NSL-KDD). This paper concludes the use of an intrusion detection network resulting in high security and performance. Moreover, the implemented method on the NSL-KDD and Bot-IoT datasets obtains respective accuracies of 99.50% and 0.995. It is compared with the existing methods namely, ensemble ID model for CC utilizing DL, LeNet, fuzzy deep neural network with a Honey Bader algorithm for privacy-preserving ID, and improved metaheuristics with a fuzzy logic-based IDS for cloud security, and beluga whale-tasmanian devil optimization based on deep convolutional neural network (CNN) with TL, chronological slap swarm algorithm-based deep belief network (DBN), and dragonfly improved invasive weed optimization-based Shepard CNN.</div>

show abstract

Section: Comparative Analysismentioning

confidence: 73%

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Thirumaran,

Joseph,

Srikanth

2024

IJEECS

View full text Add to dashboard Cite

show abstract

Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

Küçükkara,

Atban,

Bayılmış

2024

Adv Quantum Tech

View full text Add to dashboard Cite

Quantum Machine Learning (QML) leverages the transformative power of quantum computing to explore a broad range of applications, including optimization, data analysis, and complex problem‐solving. Central to this study is the using of an innovative intrusion detection system leveraging QML models, with a preference for Quantum Neural Network (QNN) architectures for classification tasks. The inherent advantages of QNNs, notably their parallel processing capabilities facilitated by quantum computers and the exploitation of quantum superposition and parallelism, are elucidated. These attributes empower QNNs to execute certain classification tasks expediently and with heightened efficiency. Empirical validation is conducted through the deployment and testing of a QNN‐based intrusion detection system, employing a subset of the CIC‐DDoS 2019 dataset. Notably, despite employing a reduced feature set, the QNN‐based system exhibits remarkable classification accuracy, achieving a commendable rate of 92.63%. Moreover, the study advocates for the utilization of quantum computing libraries such as Qiskit, facilitating QNN training on local machines or quantum simulators. The findings underscore the efficacy of a QNN‐based intrusion detection system in attaining superior classification accuracy when confronted with large‐scale training datasets. However, it is imperative to acknowledge the constraints imposed by the limited number of qubits available on local machines and simulators.

show abstract

Identifying Malicious Software on Android Devices Through Genetic Algorithm-Driven Feature Selection and Machine Learning

Mogiligidda,

Medishetty,

Thuvva

et al. 2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Enhanced Quantum-Secure Ensemble Intrusion Detection Techniques for Cloud Based on Deep Learning

Cited by 13 publications

References 37 publications

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

Identifying Malicious Software on Android Devices Through Genetic Algorithm-Driven Feature Selection and Machine Learning

Contact Info

Product

Resources

About