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ERPCA-GFCM: An enhancement DDOS attack detection model on internet of things / Grace Anne S. Cahulogan, Ederlyn Ann. V. Gordula. 6

By: Grace Anne S. Cahulogan, Ederlyn Ann. V. Gordula. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; June 2023.46Edition: Description: 28 cm. 76 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: This research paper introduced an Enhanced Distributed Denial-of-Service (DDoS) Detection model specifically designed for IoT devices. Given the prevalence of DDoS attacks targeting IoT devices, which involve overwhelming a system with malicious traffic to distrupt its normal functioning, the proposed model aimed to enhance the security and resilience of IoT networks. To address this, the proposed model integrated multiple techniques to improve detection and classification accuracy. The first technique, the ER-Relief algorithm, is a feature selection method that addresses the presence of noise and outliers in the dataset by minimizing a loss function based on the empirical of margins. Principal Component Analysis (PCA) was utilized for dimensionality reduction to enhance the model's performance further. PCA transforms the original high dimensional feature space into a lower-dimensional space while preserving the most critical information. The model incorporates the Global Fuzzy C-means algorithm to achieve better clustering results, and this algorithm addressed the issue of sensitivity to initial conditions, which led to suboptimal clustering results. By incorporating fuzzy logic principles, Global Fuzzy C-means assign data points to multiple clusters with varying degrees of membership, providing a more nuanced representation of the underlying data structure. Lastly, the Random Forest algorithm was employed for training and testing the model. The model was then tested on the CICDDoS2019 dataset, which contains three (3) types of DDoS attacks: DNS, UDP, and MSSQL. Based on the evaluation results, the proposed model achieved an impressive accuracy of 97.92%, recall of 97.92%, FI-score of 97.90%, and precision of 97.93%. These metrics highlighted the model's effectiveness, showcasing its ability to accurately detect and recall various DDoS attacks with high precision and recall. This research advances network security by providing a robust and reliable solution for combating DDoS attacks. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	T QA75.5.C34.2023 (Browse shelf)	Available		FT7749

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Undergraduate Thesis: (Bachelor of Science in Computer Science) Pamantasan ng Lungsod ng Maynila, 2023. 56

ABSTRACT: This research paper introduced an Enhanced Distributed Denial-of-Service (DDoS) Detection model specifically designed for IoT devices. Given the prevalence of DDoS attacks targeting IoT devices, which involve overwhelming a system with malicious traffic to distrupt its normal functioning, the proposed model aimed to enhance the security and resilience of IoT networks. To address this, the proposed model integrated multiple techniques to improve detection and classification accuracy. The first technique, the ER-Relief algorithm, is a feature selection method that addresses the presence of noise and outliers in the dataset by minimizing a loss function based on the empirical of margins. Principal Component Analysis (PCA) was utilized for dimensionality reduction to enhance the model's performance further. PCA transforms the original high dimensional feature space into a lower-dimensional space while preserving the most critical information. The model incorporates the Global Fuzzy C-means algorithm to achieve better clustering results, and this algorithm addressed the issue of sensitivity to initial conditions, which led to suboptimal clustering results. By incorporating fuzzy logic principles, Global Fuzzy C-means assign data points to multiple clusters with varying degrees of membership, providing a more nuanced representation of the underlying data structure. Lastly, the Random Forest algorithm was employed for training and testing the model. The model was then tested on the CICDDoS2019 dataset, which contains three (3) types of DDoS attacks: DNS, UDP, and MSSQL. Based on the evaluation results, the proposed model achieved an impressive accuracy of 97.92%, recall of 97.92%, FI-score of 97.90%, and precision of 97.93%. These metrics highlighted the model's effectiveness, showcasing its ability to accurately detect and recall various DDoS attacks with high precision and recall. This research advances network security by providing a robust and reliable solution for combating DDoS attacks.

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