Hybrid feature selection and BWTDO enabled DeepCNN-TL for intrusion detection in fuzzy cloud computing

Raj, Meghana G; Pani, Santosh Kumar

doi:10.1007/s00500-023-08573-3

Cited by 3 publications

(5 citation statements)

References 23 publications

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“…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. Furthermore, the EICDL [18] model does not possess an adequate ability to detect prior unknown attacks.…”

Section: Discussionmentioning

confidence: 99%

“…In this evaluation, the results of the presented approach on the NSL-KDD dataset is compared with that of the existing approaches. According to the measures of accuracy, precision, recall, and f1-score, the results displayed in Table 4 indicate that the suggested method outperformed all other models namely, EICDL [14], LeNet [16], FDNN-HBAID [17], and IMFL-IDSCS [20]. The implemented HLSTM achieves better accuracy on the dataset of NSL-KDD with values of 99.73%, 99.50%, 99.46%, and 99.70% in terms of f1-score, accuracy, precision, and recall, simultaneously.…”

Section: Comparative Analysismentioning

confidence: 99%

“…When deep learning is used to create an IDS, higher-dimensional features are extracted from original data to improve the classification model [16]. Raj and Pani [17] implemented a beluga whaletasmanian devil optimization (BWTDO)_DeepCNN-TL model, which was BWTDO based on deep CNN with TL. This implemented method was utilized to develop a high-performance IDS based on a fuzzy classifier in an environment cloud.…”

Section: Introductionmentioning

confidence: 99%

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

Thirumaran,

Joseph,

Srikanth

2024

IJEECS

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<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>

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

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“…Its allure originated from its inherent cost-efficiency and remarkable reliability, which allowed organizations to scale their operations with unprecedented flexibility. However, with the increasing reliance on cloud infrastructure, there emerged an ominous and ever-present threat of cyberattacks [3][4][5]. These nefarious assaults on digital infrastructure disrupt normal system operations, perpetrating malicious activities that compromise data integrity, confidentiality, availability, and privacy.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Ramachandran,

Albathan,

Hussain

et al. 2023

Systems

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Developing a simple and efficient attack detection system for ensuring the security of cloud systems against cyberthreats is a crucial and demanding process in the present time. In traditional work, various machine-learning-based detection methodologies have been developed for securing the cloud network. However, those methodologies face the complications of overfitting, complex system design, difficulty understanding, and higher time consumption. Hence, the proposed work contributes to the design and development of an effective security model for detecting cyberthreats from cloud systems. The proposed framework encompasses the modules of preprocessing and normalization, feature extraction, optimization, and prediction. An improved principal component analysis (IPCA) model is used to extract the relevant features from the normalized dataset. Then, a hybrid grasshopper–crow search optimization (GSCSO) is employed to choose the relevant features for training and testing operations. Finally, an isolated heuristic neural network (IHNN) algorithm is used to predict whether the data flow is normal or intrusive. Popular and publicly available datasets such as NSL-KDD, BoT-IoT, KDD Cup’99, and CICIDS 2017 are used for implementing the detection system. For validation, the different performance indicators, such as detection accuracy (AC) and F1-score, are measured and compared with the proposed GSCSO-IHNN system. On average, the GSCO-IHNN system achieved 99.5% ACC and 0.999 F1 scores on these datasets. The results of the performance study show that the GSCSO-IHNN method outperforms the other security models. Ultimately, this research strives to contribute to the ongoing efforts to fortify the security of cloud systems, making them resilient against cyber threats more simply and efficiently.

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Optimizing 3D UAV Path Planning: A Multi-strategy Enhanced Beluga Whale Optimizer

Ye,

Wang,

Zhang

et al. 2023

Lecture Notes in Computer Science

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Hybrid feature selection and BWTDO enabled DeepCNN-TL for intrusion detection in fuzzy cloud computing

Cited by 3 publications

References 23 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

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Optimizing 3D UAV Path Planning: A Multi-strategy Enhanced Beluga Whale Optimizer

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